The current research of air suspension mainly focuses on the characteristics and design of the air spring. In fact, electronically controlled air suspension (ECAS) has excellent performance in flexible height adjust...The current research of air suspension mainly focuses on the characteristics and design of the air spring. In fact, electronically controlled air suspension (ECAS) has excellent performance in flexible height adjustment during different driving conditions. However, the nonlinearity of the ride height adjusting system and the uneven distribution of payload affect the control accuracy of ride height and the body attitude. Firstly, the three-point measurement system of three height sensors is used to establish the mathematical model of the ride height adjusting system. The decentralized control of ride height and the centralized control of body attitude are presented to design the ride height control system for ECAS. The exact feedback linearization method is adopted for the nonlinear mathematical model of the ride height system. Secondly, according to the hierarchical control theory, the variable structure control (VSC) technique is used to design a controller that is able to adjust the ride height for the quarter-vehicle anywhere, and each quarter-vehicle height control system is independent. Meanwhile, the three-point height signals obtained by three height sensors are tracked to calculate the body pitch and roll attitude over time, and then by calculating the deviation of pitch and roll and its rates, the height control correction is reassigned based on the fuzzy algorithm. Finally, to verify the effectiveness and performance of the proposed combined control strategy, a validating test of ride height control system with and without road disturbance is carried out. Testing results show that the height adjusting time of both lifting and lowering is over 5 s, and the pitch angle and the roll angle of body attitude are less than 0.15°. This research proposes a hierarchical control method that can guarantee the attitude stability, as well as satisfy the ride height tracking system.展开更多
This paper describes a supervisory hierarchical fuzzy controller (SHFC) for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made usi...This paper describes a supervisory hierarchical fuzzy controller (SHFC) for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral (PI) controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.展开更多
Demand response has been recognized as a valuable functionality of power systems for mitigating power imbalances.This paper proposes a hierarchical control strategy among the distribution system operator(DSO),load agg...Demand response has been recognized as a valuable functionality of power systems for mitigating power imbalances.This paper proposes a hierarchical control strategy among the distribution system operator(DSO),load aggregators(LAs),and thermostatically controlled loads(TCLs);the strategy includes a scheduling layer and an executive layer to provide load regulation.In the scheduling layer,the DSO(leader)offers compensation price(CP)strategies,and the LAs(followers)respond to CP strategies with available regulation power(ARP)strategies.Profits of the DSO and LAs are modeled according to their behaviors during the load regulation process.Stackelberg game is adopted to capture interactions among the players and leader and to obtain the optimal strategy for each participant to achieve utility.Moreover,considering inevitable random factors in practice,e.g.,renewable generation and behavior of users,two different stochastic models based on sample average approximation(SAA)and parameter modification are formulated with improved scheduling accuracy.In the executive layer,distributed TCLs are triggered based on strategies determined in the scheduling layer.A self-triggering method that does not violate user privacy is presented,where TCLs receive external signals from the LA and independently determine whether to alter their operation statuses.Numerical simulations are performed on the modified IEEE-24 bus system to verify effectiveness of the proposed strategy.展开更多
This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sl...This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external disturbances.Additionally,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots.Then,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state.Finally,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and SMC.The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.展开更多
The existing containment control has been widely developed for several years, but ignores the case for large-scale cooperation. The strong coupling of large-scale networks will increase the costs of system detection a...The existing containment control has been widely developed for several years, but ignores the case for large-scale cooperation. The strong coupling of large-scale networks will increase the costs of system detection and maintenance. Therefore, this paper is concerned with an extensional containment control issue, hierarchical containment control. It aims to enable a multitude of followers achieving a novel cooperation in the convex hull shaped by multiple leaders. Firstly, by constructing the three-layer topology, large-scale networks are decoupled. Then,under the condition of directed spanning group-tree, a class of dynamic hierarchical containment control protocol is designed such that the novel group-consensus behavior in the convex hull can be realized. Moreover, the definitions of coupling strength coefficients and the group-consensus parameter in the proposed dynamic hierarchical control protocol enhance the adjustability of systems. Compared with the existing containment control strategy, the proposed hierarchical containment control strategy improves dynamic control performance. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed hierarchical control protocol.展开更多
This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on th...This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.展开更多
Hierarchical control method which is based on a hierarchical architecture has been developed to be mainly aimed at large-scale complex systems.In order to analyse and control this kind of systems,we construct first an...Hierarchical control method which is based on a hierarchical architecture has been developed to be mainly aimed at large-scale complex systems.In order to analyse and control this kind of systems,we construct first an appropriate and low-dimensional abstract system,then synthesise and lift the control law from the obtained abstraction to the original system.As far as the linear systems with uncertain terms are concerned,this paper studies the robust control problem of high-dimensional uncertain linear systems and derives the results by employing hierarchical controlmethod.Furthermore,the LMI toolbox is allowed to be used for the computation of interface functions.Finally,our method framework is illustrated on a five-dimensional uncertain linear system.展开更多
The longitudinal and lateral coordinated control for autonomous vehicles is fundamental to achieve safe and comfortable driving performance.Aiming at this for hybrid electric vehicles(HEV)during the car-following(CF)a...The longitudinal and lateral coordinated control for autonomous vehicles is fundamental to achieve safe and comfortable driving performance.Aiming at this for hybrid electric vehicles(HEV)during the car-following(CF)and lane-change(LC)process while accelerating,a hierarchical control strategy for vehicle stability control is proposed.This new approach is different from the conventional hierarchical control.On the basis of model predictive control(MPC)theory,a two-layer MPC controller is designed at the top level of the control structure.The upper layer is a linear time-varying MPC(LTV-MPC),while the lower layer is a hybrid MPC(HMPC).For the LTV-MPC controller,a control-oriented linear discrete model for HEV is established,which integrates the dynamic model with three degrees of freedom(DOF)and the car-following model.The lower-layer HMPC controller is designed on the basis of the analysis for HEV hybrid characteristics and the modelling for the mixed logic dynamic(MLD)model of the HEV powertrain.As for the bottom level,a control plant including the HEV powertrain model and the 7 DOF nonlinear dynamics of the vehicle body is established.In addition,the system stability is proven.A deep fusion of vehicle dynamics control and energy management is achieved.Compared with LC-ACC control and conventional ACC control,the simulation and the hardware-in-the-loop(HIL)test results under different driving scenarios show that the proposed hierarchical control strategy can effectively maintain lateral stability and safety under severe driving conditions.Additionally,the HEV powertrain output torque and the gear-shift point are coordinated and controlled by the HMPC controller.展开更多
This paper investigates the hierarchical control of DC microgrids.Compared to AC microgrids,DC microgrids encounter complicated converter-level control,and simplified system-level management.To address these character...This paper investigates the hierarchical control of DC microgrids.Compared to AC microgrids,DC microgrids encounter complicated converter-level control,and simplified system-level management.To address these characteristics,a new three-level control hierarchy is introduced.The converter control level encapsulates sophisticated converter topologies and inner control loops into a black-box representation.The voltage coordination level uses DC voltage signals to coordinate both static and transient power sharing.The energy management level optimizes the power flow and power quality in a broader scope through communication.This architecture lowers the focus of control to bottom levels.More functions are allocated to the converter control and voltage coordination levels.They can maintain basic microgrid performance with fully local control,thereby ensuring reliable power supply in case of communication failures.Moreover,taking advantage of DC microgrids’simplified system-level operation patterns,the energy management level uses straightforward algorithms to achieve intelligent functions.As a result,this architecture achieves both robust and smart control by exploring DC microgrids’critical features.展开更多
Droop control is one of the main control strategies of islanded microgrid(MG),but the droop control cannot achieve reasonable power distribution of microgrid,resulting in frequency and voltage deviation from the ratin...Droop control is one of the main control strategies of islanded microgrid(MG),but the droop control cannot achieve reasonable power distribution of microgrid,resulting in frequency and voltage deviation from the rating value,which needs the upper control link to eliminate the deviation.However,at present,most layered control requires a centralized control center,which excessively relies on microgrid central controller(MGCC)and real-time communication among distributed generation(DG),which has certain limitations.To solve the above problems,this paper proposes a hierarchical distributed power and power quality optimization strategy based on multi-agent finite time consistency algorithm(MA-FTCA).Firstly,based on the first layer droop control,MA-FTCA is applied to introduce frequency and voltage compensation to stabilize the system frequency and voltage at the rated value.Secondly,in the third layer,the MA-FTCA is adopted to estimate the total active power and total reactive power spare capacity of the system,to realize the reasonable distribution of active power and reactive power output of each DG according to its proportion of spare capacity when the system load side changes.The control strategy proposed in this paper adopts a completely distributed control method and does not need a centralized control center in each layer of control.Finally,MATLAB/Simulink simulation platform is used to verify the correctness and effectiveness of the proposed optimization strategy.展开更多
This paper introduces the concept of hierarchical-control-based output synchronization of coexisting attractor networks. Within the new framework, each dynamic node is made passive at first utilizing intra-control aro...This paper introduces the concept of hierarchical-control-based output synchronization of coexisting attractor networks. Within the new framework, each dynamic node is made passive at first utilizing intra-control around its own arena. Then each dynamic node is viewed as one agent, and on account of that, the solution of output synchronization of coexisting attractor networks is transformed into a multi-agent consensus problem, which is made possible by virtue of local interaction between individual neighbours; this distributed working way of coordination is coined as inter-control, which is only specified by the topological structure of the network. Provided that the network is connected and balanced, the output synchronization would come true naturally via synergy between intra and inter-control actions, where the rightness is proved theoretically via convex composite Lyapunov functions. For completeness, several illustrative examples are presented to further elucidate the novelty and efficacy of the proposed scheme.展开更多
The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in...The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in SDN.Every controller supports a set of features.However,the support of the features may be more prominent in one controller.Moreover,a single controller leads to performance,single-point-of-failure(SPOF),and scalability problems.To overcome this,a controller with an optimum feature set must be available for SDN.Furthermore,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN.Herein,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E topology.The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS.Moreover,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.展开更多
In reinforcement learning an agent may explore ineffectively when dealing with sparse reward tasks where finding a reward point is difficult.To solve the problem,we propose an algorithm called hierarchical deep reinfo...In reinforcement learning an agent may explore ineffectively when dealing with sparse reward tasks where finding a reward point is difficult.To solve the problem,we propose an algorithm called hierarchical deep reinforcement learning with automatic sub-goal identification via computer vision(HADS)which takes advantage of hierarchical reinforcement learning to alleviate the sparse reward problem and improve efficiency of exploration by utilizing a sub-goal mechanism.HADS uses a computer vision method to identify sub-goals automatically for hierarchical deep reinforcement learning.Due to the fact that not all sub-goal points are reachable,a mechanism is proposed to remove unreachable sub-goal points so as to further improve the performance of the algorithm.HADS involves contour recognition to identify sub-goals from the state image where some salient states in the state image may be recognized as sub-goals,while those that are not will be removed based on prior knowledge.Our experiments verified the effect of the algorithm.展开更多
To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The con...To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller(GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference,compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DX A/C system with uncertain parameters are demonstrated through some simulation results.展开更多
This paper focuses on the solution to the dynamic affine formation control problem for multiple networked underactuated quad-rotor unmanned aerial vehicles(UAVs)to achieve a configuration that preserves collinearity a...This paper focuses on the solution to the dynamic affine formation control problem for multiple networked underactuated quad-rotor unmanned aerial vehicles(UAVs)to achieve a configuration that preserves collinearity and ratios of distances for a target configuration.In particular,it is investigated that the quad-rotor UAVs are steered to track a reference linear velocity while maintaining a desired three-dimensional target formation.Firstly,by integrating the properties of the affine transformation and the stress matrix,the design of the target formation is convenient and applicable for various three-dimensional geometric patterns.Secondly,a distributed control method is proposed under a hierarchical framework.By introducing an intermediary control input for each quad-rotor UAV in the position loop,the necessary thrust input and the desired attitude are extracted.In the attitude loop,the desired attitude represented by the unit quaternion is tracked by the designed torque input.Both conditions of linear velocity unavailability and mutual collision avoidance are also tackled.In terms of Lyapunov theory,it is prooved that the overall closed-loop error system is asymptotically stable.Finally,two illustrative examples are simulated to validate the effectiveness of the proposed theoretical results.展开更多
An eight wheel independently driving steering(8 WIDBS)electric vehicle is studied in this paper.The vehicle is equipped with eight in-wheel motors and a steer-by-wire system.A hierarchically coordinated vehicle dyna...An eight wheel independently driving steering(8 WIDBS)electric vehicle is studied in this paper.The vehicle is equipped with eight in-wheel motors and a steer-by-wire system.A hierarchically coordinated vehicle dynamic control(HCVDC)system,including a high-level vehicle motion controller,a control allocation,an inverse tire model and a lower-level slip/slip angle controller,is proposed for the over-actuated vehicle system.The high-level sliding mode vehicle motion controller is designed to produce desired total forces and yaw moment,distributed to longitudinal and lateral forces of each tire by an advanced control allocation method.And the slip controller is designed to use a sliding mode control method to follow the desired slip ratios by manipulating the corresponding in-wheel motor torques.Evaluation of the overall system is accomplished by sine maneuver simulation.Simulation results confirm that the proposed control system can coordinate among the redundant and constrained actuators to achieve the vehicle dynamic control task and improve the vehicle stability.展开更多
The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control...The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control,this paper proposes a hierarchical cluster coordination control(HCCC)strategy based on model predictive control(MPC)technique.Considering the time-varying characteristics of wind power generation,the proposed HCCC strategy constructs an improved multitime-scale active power dispatching model,which consists of five parts:formulation of cluster dispatching plan,rolling modification of intra-cluster plan,optimization allocation of wind farm(WF),grouping coordinated control of wind turbine group(WTG),and real-time adjustment of single-machine power.The time resolutions are sequentially given as 1 hour,30 min,15 min,5 min,and 1 min.In addition,a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),wavelet thresholding(WT),and least squares support vector machine(LSSVM)is established.The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision.Simulation results show that the proposed HCCC strategy enables rapid response to active power control(APC),and significantly improves dispatching control accuracy and wind power accommodation capabilities.展开更多
Autonomous navigation is a fundamental problem in robotics.Traditional methods generally build point cloud map or dense feature map in perceptual space;due to lack of cognition and memory formation mechanism,tradition...Autonomous navigation is a fundamental problem in robotics.Traditional methods generally build point cloud map or dense feature map in perceptual space;due to lack of cognition and memory formation mechanism,traditional methods exist poor robustness and low cognitive ability.As a new navigation technology that draws inspiration from mammal’s navigation,bionic navigation method can map perceptual information into cognitive space,and have strong autonomy and environment adaptability.To improve the robot’s autonomous navigation ability,this paper proposes a cognitive map-based hierarchical navigation method.First,the mammals’navigation-related grid cells and head direction cells are modeled to provide the robots with location cognition.And then a global path planning strategy based on cognitive map is proposed,which can anticipate one preferred global path to the target with high efficiency and short distance.Moreover,a hierarchical motion controlling method is proposed,with which the target navigation can be divided into several sub-target navigation,and the mobile robot can reach to these sub-targets with high confidence level.Finally,some experiments are implemented,the results show that the proposed path planning method can avoid passing through obstacles and obtain one preferred global path to the target with high efficiency,and the time cost does not increase extremely with the increase of experience nodes number.The motion controlling results show that the mobile robot can arrive at the target successfully only depending on its self-motion information,which is an effective attempt and reflects strong bionic properties.展开更多
A hierarchical control scheme is proposed for optimal power flow control to minimize loss in a hybrid multiterminal HVDC(hybrid-MTDC)transmission system.In this scheme,the lower level is the droop control,which enable...A hierarchical control scheme is proposed for optimal power flow control to minimize loss in a hybrid multiterminal HVDC(hybrid-MTDC)transmission system.In this scheme,the lower level is the droop control,which enables fast response to power fluctuation and ensures a stable DC voltage,and the upper level is power flow optimization control,which minimizes the losses during the operation of hybrid-MTDC and solves the contradiction between minimizing losses and preventing commutation failure.A 6-terminal hybrid-MTDC is also designed and simulated in PSCAD according to the potential demand of power transmission and wind farms integration in China to verify the proposed control strategy.First,the steady state analysis is conducted and then compared with simulation results.The analysis shows that the proposed control scheme achieves the desired minimum losses while at the same time satisfying system constraints.The proposed control scheme also guarantees that the hybrid-MTDC not only has a good dynamic response,but also remains stable during communication failure.展开更多
This paper solves a mean-field type hierarchical optimal control problem in electricity market.The authors consider n-1 prosumers and one producer.The ith prosumer,for 1<i<n,is a leader of the(i-1)th prosumer an...This paper solves a mean-field type hierarchical optimal control problem in electricity market.The authors consider n-1 prosumers and one producer.The ith prosumer,for 1<i<n,is a leader of the(i-1)th prosumer and is a follower of the(i+1)th prosumer.The first player(agent)is the follower at the bottom whereas the nth is the leader at the top.The problem is described by a linear jump-diffusion system of conditional mean-field type,where the conditioning is with respect to common noise,and a quadratic cost functional involving,the square of the conditional expectation of the controls of the agents.The authors provide a semi-explicit solution of the corresponding meanfield-type hierarchical control problem with common noise.Finally,the authors illustrate the obtained result via a numerical example with two different scenarios.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51105177)Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20131255)+2 种基金Research Fund for the Doctoral Program of Higher Education of China(Grant No.20113227120015)Qing Lan Project of Jiangsu Province of China,Scientific Research Foundation for Advanced Talents,Jiangsu University,China(Grant No.11JDG047)Hunan Provincial Natural Science Foundation of China(Grant No.12JJ6036)
文摘The current research of air suspension mainly focuses on the characteristics and design of the air spring. In fact, electronically controlled air suspension (ECAS) has excellent performance in flexible height adjustment during different driving conditions. However, the nonlinearity of the ride height adjusting system and the uneven distribution of payload affect the control accuracy of ride height and the body attitude. Firstly, the three-point measurement system of three height sensors is used to establish the mathematical model of the ride height adjusting system. The decentralized control of ride height and the centralized control of body attitude are presented to design the ride height control system for ECAS. The exact feedback linearization method is adopted for the nonlinear mathematical model of the ride height system. Secondly, according to the hierarchical control theory, the variable structure control (VSC) technique is used to design a controller that is able to adjust the ride height for the quarter-vehicle anywhere, and each quarter-vehicle height control system is independent. Meanwhile, the three-point height signals obtained by three height sensors are tracked to calculate the body pitch and roll attitude over time, and then by calculating the deviation of pitch and roll and its rates, the height control correction is reassigned based on the fuzzy algorithm. Finally, to verify the effectiveness and performance of the proposed combined control strategy, a validating test of ride height control system with and without road disturbance is carried out. Testing results show that the height adjusting time of both lifting and lowering is over 5 s, and the pitch angle and the roll angle of body attitude are less than 0.15°. This research proposes a hierarchical control method that can guarantee the attitude stability, as well as satisfy the ride height tracking system.
文摘This paper describes a supervisory hierarchical fuzzy controller (SHFC) for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral (PI) controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.
基金supported by the Natural Science Foundation of Jiangsu Province(SBK2023043599)Introduction of teacher research start-up fees(423167)National Natural Science Foundation of China(51837004,U2066601)。
文摘Demand response has been recognized as a valuable functionality of power systems for mitigating power imbalances.This paper proposes a hierarchical control strategy among the distribution system operator(DSO),load aggregators(LAs),and thermostatically controlled loads(TCLs);the strategy includes a scheduling layer and an executive layer to provide load regulation.In the scheduling layer,the DSO(leader)offers compensation price(CP)strategies,and the LAs(followers)respond to CP strategies with available regulation power(ARP)strategies.Profits of the DSO and LAs are modeled according to their behaviors during the load regulation process.Stackelberg game is adopted to capture interactions among the players and leader and to obtain the optimal strategy for each participant to achieve utility.Moreover,considering inevitable random factors in practice,e.g.,renewable generation and behavior of users,two different stochastic models based on sample average approximation(SAA)and parameter modification are formulated with improved scheduling accuracy.In the executive layer,distributed TCLs are triggered based on strategies determined in the scheduling layer.A self-triggering method that does not violate user privacy is presented,where TCLs receive external signals from the LA and independently determine whether to alter their operation statuses.Numerical simulations are performed on the modified IEEE-24 bus system to verify effectiveness of the proposed strategy.
文摘This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external disturbances.Additionally,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots.Then,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state.Finally,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and SMC.The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.
基金supported in part by the National Natural Science Foundation of China(U22A20221,62073064)in part by the Fundamental Research Funds for the Central Universities in China(N2204007)。
文摘The existing containment control has been widely developed for several years, but ignores the case for large-scale cooperation. The strong coupling of large-scale networks will increase the costs of system detection and maintenance. Therefore, this paper is concerned with an extensional containment control issue, hierarchical containment control. It aims to enable a multitude of followers achieving a novel cooperation in the convex hull shaped by multiple leaders. Firstly, by constructing the three-layer topology, large-scale networks are decoupled. Then,under the condition of directed spanning group-tree, a class of dynamic hierarchical containment control protocol is designed such that the novel group-consensus behavior in the convex hull can be realized. Moreover, the definitions of coupling strength coefficients and the group-consensus parameter in the proposed dynamic hierarchical control protocol enhance the adjustability of systems. Compared with the existing containment control strategy, the proposed hierarchical containment control strategy improves dynamic control performance. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed hierarchical control protocol.
基金supported in part by the National Natural Science Foundation of China(61873056,61621004,61420106016)the Fundamental Research Funds for the Central Universities in China(N2004001,N2004002,N182608004)the Research Fund of State Key Laboratory of Synthetical Automation for Process Industries in China(2013ZCX01)。
文摘This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.
基金work was supported by the National Natural Science Foundation of China[grant number 61273090]and[grant number 61333008].
文摘Hierarchical control method which is based on a hierarchical architecture has been developed to be mainly aimed at large-scale complex systems.In order to analyse and control this kind of systems,we construct first an appropriate and low-dimensional abstract system,then synthesise and lift the control law from the obtained abstraction to the original system.As far as the linear systems with uncertain terms are concerned,this paper studies the robust control problem of high-dimensional uncertain linear systems and derives the results by employing hierarchical controlmethod.Furthermore,the LMI toolbox is allowed to be used for the computation of interface functions.Finally,our method framework is illustrated on a five-dimensional uncertain linear system.
基金supported by the National Natural Science Foundation of China(Grant Nos.51975253 and 51905219)the Program of the Youth Natural Science Foundation of Jiangsu Province(Grant No.BK20200909)+1 种基金the Postdoctoral Science Foundation of China(Grant No.2020M671381)the Natural Science Research Project of Jiangsu Higher Education Institutions(Grant No.19KJB580001)。
文摘The longitudinal and lateral coordinated control for autonomous vehicles is fundamental to achieve safe and comfortable driving performance.Aiming at this for hybrid electric vehicles(HEV)during the car-following(CF)and lane-change(LC)process while accelerating,a hierarchical control strategy for vehicle stability control is proposed.This new approach is different from the conventional hierarchical control.On the basis of model predictive control(MPC)theory,a two-layer MPC controller is designed at the top level of the control structure.The upper layer is a linear time-varying MPC(LTV-MPC),while the lower layer is a hybrid MPC(HMPC).For the LTV-MPC controller,a control-oriented linear discrete model for HEV is established,which integrates the dynamic model with three degrees of freedom(DOF)and the car-following model.The lower-layer HMPC controller is designed on the basis of the analysis for HEV hybrid characteristics and the modelling for the mixed logic dynamic(MLD)model of the HEV powertrain.As for the bottom level,a control plant including the HEV powertrain model and the 7 DOF nonlinear dynamics of the vehicle body is established.In addition,the system stability is proven.A deep fusion of vehicle dynamics control and energy management is achieved.Compared with LC-ACC control and conventional ACC control,the simulation and the hardware-in-the-loop(HIL)test results under different driving scenarios show that the proposed hierarchical control strategy can effectively maintain lateral stability and safety under severe driving conditions.Additionally,the HEV powertrain output torque and the gear-shift point are coordinated and controlled by the HMPC controller.
文摘This paper investigates the hierarchical control of DC microgrids.Compared to AC microgrids,DC microgrids encounter complicated converter-level control,and simplified system-level management.To address these characteristics,a new three-level control hierarchy is introduced.The converter control level encapsulates sophisticated converter topologies and inner control loops into a black-box representation.The voltage coordination level uses DC voltage signals to coordinate both static and transient power sharing.The energy management level optimizes the power flow and power quality in a broader scope through communication.This architecture lowers the focus of control to bottom levels.More functions are allocated to the converter control and voltage coordination levels.They can maintain basic microgrid performance with fully local control,thereby ensuring reliable power supply in case of communication failures.Moreover,taking advantage of DC microgrids’simplified system-level operation patterns,the energy management level uses straightforward algorithms to achieve intelligent functions.As a result,this architecture achieves both robust and smart control by exploring DC microgrids’critical features.
基金support provided by Opening Foundation of Key Laboratory of Opto-technology and Intelligent Control(Lanzhou Jiaotong University),Ministry of Education(KFKT2020-11).
文摘Droop control is one of the main control strategies of islanded microgrid(MG),but the droop control cannot achieve reasonable power distribution of microgrid,resulting in frequency and voltage deviation from the rating value,which needs the upper control link to eliminate the deviation.However,at present,most layered control requires a centralized control center,which excessively relies on microgrid central controller(MGCC)and real-time communication among distributed generation(DG),which has certain limitations.To solve the above problems,this paper proposes a hierarchical distributed power and power quality optimization strategy based on multi-agent finite time consistency algorithm(MA-FTCA).Firstly,based on the first layer droop control,MA-FTCA is applied to introduce frequency and voltage compensation to stabilize the system frequency and voltage at the rated value.Secondly,in the third layer,the MA-FTCA is adopted to estimate the total active power and total reactive power spare capacity of the system,to realize the reasonable distribution of active power and reactive power output of each DG according to its proportion of spare capacity when the system load side changes.The control strategy proposed in this paper adopts a completely distributed control method and does not need a centralized control center in each layer of control.Finally,MATLAB/Simulink simulation platform is used to verify the correctness and effectiveness of the proposed optimization strategy.
基金supported by the State Key Laboratory of Scientific&Engineering Computing, Chinese Academy of Sciencesthe National Natural Science Foundation of China (Grant No. 60850004)+4 种基金the Funds for Creative Research Talents of Henan Education Bureau, China (Grant No. 2009HASTIT021)the Natural Science Foundation of Henan Education Bureau, China(Grant No. 2008A120005)Fundamental&Frontier Technology Research Planning Project of Henan Province,China (Grant No.072300460050)Doctoral Program of Henan Polytechnic University (Grant No. 648606)Young Teacher Key Talents Program of Henan Polytechnic University (Grant No. 649033)
文摘This paper introduces the concept of hierarchical-control-based output synchronization of coexisting attractor networks. Within the new framework, each dynamic node is made passive at first utilizing intra-control around its own arena. Then each dynamic node is viewed as one agent, and on account of that, the solution of output synchronization of coexisting attractor networks is transformed into a multi-agent consensus problem, which is made possible by virtue of local interaction between individual neighbours; this distributed working way of coordination is coined as inter-control, which is only specified by the topological structure of the network. Provided that the network is connected and balanced, the output synchronization would come true naturally via synergy between intra and inter-control actions, where the rightness is proved theoretically via convex composite Lyapunov functions. For completeness, several illustrative examples are presented to further elucidate the novelty and efficacy of the proposed scheme.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in SDN.Every controller supports a set of features.However,the support of the features may be more prominent in one controller.Moreover,a single controller leads to performance,single-point-of-failure(SPOF),and scalability problems.To overcome this,a controller with an optimum feature set must be available for SDN.Furthermore,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN.Herein,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E topology.The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS.Moreover,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.
基金supported by the National Natural Science Foundation of China(61303108)Suzhou Key Industries Technological Innovation-Prospective Applied Research Project(SYG201804)+2 种基金A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Fundamental Research Funds for the Gentral UniversitiesJLU(93K172020K25)。
文摘In reinforcement learning an agent may explore ineffectively when dealing with sparse reward tasks where finding a reward point is difficult.To solve the problem,we propose an algorithm called hierarchical deep reinforcement learning with automatic sub-goal identification via computer vision(HADS)which takes advantage of hierarchical reinforcement learning to alleviate the sparse reward problem and improve efficiency of exploration by utilizing a sub-goal mechanism.HADS uses a computer vision method to identify sub-goals automatically for hierarchical deep reinforcement learning.Due to the fact that not all sub-goal points are reachable,a mechanism is proposed to remove unreachable sub-goal points so as to further improve the performance of the algorithm.HADS involves contour recognition to identify sub-goals from the state image where some salient states in the state image may be recognized as sub-goals,while those that are not will be removed based on prior knowledge.Our experiments verified the effect of the algorithm.
基金supported by the National Natural Science Foundation of China(61773220,61876192,61907021)the National Natural Science Foundation of Hubei(ZRMS2019000752)+2 种基金the Fundamental Research Funds for the Central Universities(2662018QD057,CZT20022,CZT20020)Academic Team in Universities(KTZ20051)School Talent Funds(YZZ19004)。
文摘To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller(GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference,compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DX A/C system with uncertain parameters are demonstrated through some simulation results.
基金supported by the National Natural Science Foundation of China(61673327)the Industrial Development and Foster Project of Yangtze River Delta Research Institute of NPU,Taicang(CY20210202)+1 种基金the Fundamental Research Funds for the Central Universities(G2021KY05116,G2022WD01026)the Basic Research Programs of Taicang(TC2021JC28)。
文摘This paper focuses on the solution to the dynamic affine formation control problem for multiple networked underactuated quad-rotor unmanned aerial vehicles(UAVs)to achieve a configuration that preserves collinearity and ratios of distances for a target configuration.In particular,it is investigated that the quad-rotor UAVs are steered to track a reference linear velocity while maintaining a desired three-dimensional target formation.Firstly,by integrating the properties of the affine transformation and the stress matrix,the design of the target formation is convenient and applicable for various three-dimensional geometric patterns.Secondly,a distributed control method is proposed under a hierarchical framework.By introducing an intermediary control input for each quad-rotor UAV in the position loop,the necessary thrust input and the desired attitude are extracted.In the attitude loop,the desired attitude represented by the unit quaternion is tracked by the designed torque input.Both conditions of linear velocity unavailability and mutual collision avoidance are also tackled.In terms of Lyapunov theory,it is prooved that the overall closed-loop error system is asymptotically stable.Finally,two illustrative examples are simulated to validate the effectiveness of the proposed theoretical results.
基金Supported by the Ministerial Level Advance Research Foundation(40402050168)
文摘An eight wheel independently driving steering(8 WIDBS)electric vehicle is studied in this paper.The vehicle is equipped with eight in-wheel motors and a steer-by-wire system.A hierarchically coordinated vehicle dynamic control(HCVDC)system,including a high-level vehicle motion controller,a control allocation,an inverse tire model and a lower-level slip/slip angle controller,is proposed for the over-actuated vehicle system.The high-level sliding mode vehicle motion controller is designed to produce desired total forces and yaw moment,distributed to longitudinal and lateral forces of each tire by an advanced control allocation method.And the slip controller is designed to use a sliding mode control method to follow the desired slip ratios by manipulating the corresponding in-wheel motor torques.Evaluation of the overall system is accomplished by sine maneuver simulation.Simulation results confirm that the proposed control system can coordinate among the redundant and constrained actuators to achieve the vehicle dynamic control task and improve the vehicle stability.
基金supported in part by the Joint Funds of the National Natural Science Foundation of China(No.U1966205)Fundamental Research Funds for the Central Universities(No.B210202067).
文摘The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control,this paper proposes a hierarchical cluster coordination control(HCCC)strategy based on model predictive control(MPC)technique.Considering the time-varying characteristics of wind power generation,the proposed HCCC strategy constructs an improved multitime-scale active power dispatching model,which consists of five parts:formulation of cluster dispatching plan,rolling modification of intra-cluster plan,optimization allocation of wind farm(WF),grouping coordinated control of wind turbine group(WTG),and real-time adjustment of single-machine power.The time resolutions are sequentially given as 1 hour,30 min,15 min,5 min,and 1 min.In addition,a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),wavelet thresholding(WT),and least squares support vector machine(LSSVM)is established.The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision.Simulation results show that the proposed HCCC strategy enables rapid response to active power control(APC),and significantly improves dispatching control accuracy and wind power accommodation capabilities.
基金funded by the National Natural Science Foundation of China-Liaoning Joint Fund(Grants:U20A20197)the National Natural Science Foundation of China(Grants:62173064)the Fundamental Research Funds for the Central Universities(Grants:N2326005).
文摘Autonomous navigation is a fundamental problem in robotics.Traditional methods generally build point cloud map or dense feature map in perceptual space;due to lack of cognition and memory formation mechanism,traditional methods exist poor robustness and low cognitive ability.As a new navigation technology that draws inspiration from mammal’s navigation,bionic navigation method can map perceptual information into cognitive space,and have strong autonomy and environment adaptability.To improve the robot’s autonomous navigation ability,this paper proposes a cognitive map-based hierarchical navigation method.First,the mammals’navigation-related grid cells and head direction cells are modeled to provide the robots with location cognition.And then a global path planning strategy based on cognitive map is proposed,which can anticipate one preferred global path to the target with high efficiency and short distance.Moreover,a hierarchical motion controlling method is proposed,with which the target navigation can be divided into several sub-target navigation,and the mobile robot can reach to these sub-targets with high confidence level.Finally,some experiments are implemented,the results show that the proposed path planning method can avoid passing through obstacles and obtain one preferred global path to the target with high efficiency,and the time cost does not increase extremely with the increase of experience nodes number.The motion controlling results show that the mobile robot can arrive at the target successfully only depending on its self-motion information,which is an effective attempt and reflects strong bionic properties.
基金supported in part by the 111 Project of China under Grant B08013State Grid Corporation of China under Grant XT71-14-042.
文摘A hierarchical control scheme is proposed for optimal power flow control to minimize loss in a hybrid multiterminal HVDC(hybrid-MTDC)transmission system.In this scheme,the lower level is the droop control,which enables fast response to power fluctuation and ensures a stable DC voltage,and the upper level is power flow optimization control,which minimizes the losses during the operation of hybrid-MTDC and solves the contradiction between minimizing losses and preventing commutation failure.A 6-terminal hybrid-MTDC is also designed and simulated in PSCAD according to the potential demand of power transmission and wind farms integration in China to verify the proposed control strategy.First,the steady state analysis is conducted and then compared with simulation results.The analysis shows that the proposed control scheme achieves the desired minimum losses while at the same time satisfying system constraints.The proposed control scheme also guarantees that the hybrid-MTDC not only has a good dynamic response,but also remains stable during communication failure.
基金support from Tamkeen under the NYU Abu Dhabi Research Institute grant CG002U.S.Air Force Office of Scientific Research under Grant No.FA955017-1-0259。
文摘This paper solves a mean-field type hierarchical optimal control problem in electricity market.The authors consider n-1 prosumers and one producer.The ith prosumer,for 1<i<n,is a leader of the(i-1)th prosumer and is a follower of the(i+1)th prosumer.The first player(agent)is the follower at the bottom whereas the nth is the leader at the top.The problem is described by a linear jump-diffusion system of conditional mean-field type,where the conditioning is with respect to common noise,and a quadratic cost functional involving,the square of the conditional expectation of the controls of the agents.The authors provide a semi-explicit solution of the corresponding meanfield-type hierarchical control problem with common noise.Finally,the authors illustrate the obtained result via a numerical example with two different scenarios.