Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

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.

Group-Consensus of Hierarchical Containment Control for Linear Multi-Agent Systems

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.

Hierarchical control of a class of uncertain linear systems

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.

Investigation on hierarchical control for driving stability and safety of intelligent HEV during car-following and lane-change process

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.

Distributed Fault-Tolerant Containment Control for Nonlinear Multi-Agent Systems Under Directed Network Topology via Hierarchical Approach

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.

Hierarchical Control of DC Microgrids Robustness and Smartness

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.

Hierarchical and Distributed Optimal Control Strategy for Power and Power Quality of Microgrid Based on Finite-Time Consistency

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.

Dynamic affine formation control of networked under-actuated quad-rotor UAVs with three-dimensional patterns

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.

Hierarchical Reinforcement Learning With Automatic Sub-Goal Identification

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.

Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering

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.

Energy-Efficient Optimal Guaranteed Cost Intermittent-Switch Control of a Direct Expansion Air Conditioning System

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.

Combined Control Allocation and Sliding Mode Control in the Dynamic Control of a Vehicle with Eight In-Wheel Motors

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.

Hierarchical Cluster Coordination Control Strategy for Large-scale Wind Power Based on Model Predictive Control and Improved Multi-time-scale Active Power Dispatching

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.

Brain Cognition Mechanism-Inspired Hierarchical Navigation Method for Mobile Robots

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.

Hierarchical Optimal Power Flow Control for Loss Minimization in Hybrid Multi-terminal HVDC Transmission System

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.

Hierarchical Mean-Field Type Control of Price Dynamics for Electricity in Smart Grid

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.

Optimization of fuel consumption and NO_(x) emission for mild HEV via hierarchical model predictive control

In this paper,we consider the fuel economy optimization problem for a mild hybrid electric vehicle(HEV)using hierarchical model predictive control.In the proposed algorithm,two problems are addressed:eco-driving and torque distribution.In the eco-driving problem,vehicle speed was controlled.Considering the reduction in fuel consumption and NOx emissions,the torque required to follow the target speed was calculated.Subsequently,in the torque distribution problem,the distribution between the engine and motor torques were calculated.In this phase,engine characteristics were considered.These problems differ in terms of time scales;therefore,a hierarchical model predictive control is proposed.Lastly,the numerical simulation results demonstrated the efficacy of this research.

Hierarchical and distributed demand response control strategy for thermostatically controlled appliances in smart grid

Thermostatically controlled appliances(TCAs)have great thermal storage capability and are therefore excellent demand response(DR) resources to solve the problem of power fluctuation caused by renewable energy.Traditional centralized management is affected by communication quality severely and thus usually has poor realtime control performance. To tackle this problem, a hierarchical and distributed control strategy for TCAs is established. In the proposed control strategy, target assignment has the feature of self-regulating, owing to the designed target assignment and compensating algorithm which can utilize DR resources maximally in the controlled regions and get better control effects. Besides, the model prediction strategy and customers’ responsive behavior model are integrated into the original optimal temperature regulation(OTR-O), and OTR-O will be evolved into improved optimal temperature regulation. A series of case studies have been given to demonstrate the control effectiveness of the proposed control strategy.

Hierarchical protection control system of smart substations

This paper analyzes the problems of the relay protections and secondary circuits of traditional substations,and introduces the hierarchical protection and control system of smart substations based on the technologies like IEC 61850 Standard,electronic instrument transformer,process level network,and wide area optical fiber communication network,etc.The hierarchical protection and control system includes three levels:local area protection,substation area protection and control,and wide area protection and control.These three levels are combined to solve the problems of the relay protections in traditional substations,thus improving protection performance and control capability of the system.

Hierarchical distributed control for decentralized battery energy storage system based on consensus algorithm with pinning node

A decentralized battery energy storage system(DBESS)is used for stabilizing power fluctuation in DC microgrids.Different state of charge(SoC)among various battery energy storage units(BESU)during operation will reduce batteries’service life.A hierarchical distributed control method is proposed in this paper for SoC balancing and power control according to dispatching center requirement in DBESS.A consensus algorithm with pinning node is employed to allocate power among BESUs in the secondary control whereas in the primary control,the local controller of BESU adjusts output power according to the reference power from secondary control.Part of BESUs are selected to be pinning node for accepting command from dispatching center while other BESUs as following nodes which exchange output power and SoC information with the adjacent nodes through communication network.After calculating reference power of each BESU by adopting consensus algorithm,the power sharing in DBESS is achieved according to their respective SoC of BESUs.Meanwhile,the total output power of DBESS follows the varying requirements of dispatching center.The stability of DBESS is also improved because of having no center controller.The feasibility of the proposed control strategy is validated by simulation results.