On-Chip Micro Temperature Controllers Based on Freestanding Thermoelectric Nano Films for Low-Power Electronics

Multidimensional integration and multifunctional com-ponent assembly have been greatly explored in recent years to extend Moore’s Law of modern microelectronics.However,this inevitably exac-erbates the inhomogeneity of temperature distribution in microsystems,making precise temperature control for electronic components extremely challenging.Herein,we report an on-chip micro temperature controller including a pair of thermoelectric legs with a total area of 50×50μm^(2),which are fabricated from dense and flat freestanding Bi2Te3-based ther-moelectric nano films deposited on a newly developed nano graphene oxide membrane substrate.Its tunable equivalent thermal resistance is controlled by electrical currents to achieve energy-efficient temperature control for low-power electronics.A large cooling temperature difference of 44.5 K at 380 K is achieved with a power consumption of only 445μW,resulting in an ultrahigh temperature control capability over 100 K mW^(-1).Moreover,an ultra-fast cooling rate exceeding 2000 K s^(-1) and excellent reliability of up to 1 million cycles are observed.Our proposed on-chip temperature controller is expected to enable further miniaturization and multifunctional integration on a single chip for microelectronics.

Path Tracking Controller Design of Automated Parking Systems via NMPC with an Instructible Solution

Parking difficulties have become a social issue that people have to solve.Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents.The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method(RF-LNMPC)in order to improve the overall path tracking accuracy in parking conditions.Firstly,A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively.Secondly,the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization,of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution.Thirdly,the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented.The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically.Finally,the proposed RF-LNMPC is implemented on a selfdriving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions.The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.

Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle(UAV)in the presence of parameter uncertainties,unmodeled uncertainties and wind disturbances.First,a fixed-time disturbance observer(FXDO)based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model.Then,a fixed-time integral sliding mode control(FXISMC)is combined with the FXDO to achieve strong robustness and chattering reduction.Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability.Finally,numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances.In addition,the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

Precision Motion Control of Hydraulic Actuator Using Adaptive Back-Stepping Sliding Mode Controller

Hydraulic actuators are highly nonlinear when they are subjected to different types of model uncertainties and dynamic disturbances.These unfavorable factors adversely affect the control performance of the hydraulic actuator.Although various control methods have been employed to improve the tracking precision of the dynamic system,optimizing and adjusting control gain to mitigate the hydraulic actuator model uncertainties remains elusive.This study presents an adaptive back-stepping sliding mode controller(ABSMC)to enhance the trajectory tracking precision,where the virtual control law is constructed to replace the position error.The adaptive control theory is introduced in back-stepping controller design to compensate for the model uncertainties and time-varying disturbances.Based on Lyapunov theory,the finite-time convergence of the position tracking errors is proved.Furthermore,the effectiveness of the developed control scheme is conducted via extensive comparative experiments.

Contact detumbling toward a nutating target through deformable effectors and prescribed performance controller

Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling.Finally, by employing the proposed effector and the controller,numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.

Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

An autonomous microgrid that runs on renewable energy sources is presented in this article.It has a supercon-ducting magnetic energy storage(SMES)device,wind energy-producing devices,and an energy storage battery.However,because such microgrids are nonlinear and the energy they create varies with time,controlling and managing the energy inside them is a difficult issue.Fractional-order proportional integral(FOPI)controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance.The suggested dedicated control for the SMES comprises two loops:the outer loop,which uses the FOPI to regulate the DC-link voltage,and the inner loop,responsible for regulating the SMES current,is constructed using the intelligent FOPI(iFOPI).The FOPI+iFOPI parameters are best developed using the dandelion optimizer(DO)approach to achieve the optimum performance.The suggested FOPI+iFOPI controller’s performance is contrasted with a conventional PI controller for variations in wind speed and microgrid load.The optimal FOPI+iFOPI controller manages the voltage and frequency of the load.The behavior of the microgrid as a reaction to step changes in load and wind speed was measured using the proposed controller.MATLAB simulations were used to evaluate the recommended system’s performance.The results of the simulations showed that throughout all interruptions,the recommended microgrid provided the load with AC power with a constant amplitude and frequency.In addition,the required load demand was accurately reduced.Furthermore,the microgrid functioned incredibly well despite SMES and varying wind speeds.Results obtained under identical conditions were compared with and without the best FOPI+iFOPI controller.When utilizing the optimal FOPI+iFOPI controller with SMES,it was found that the microgrid performed better than the microgrid without SMES.

Laboratory Implementation of Direct Torque Controller based Speed Loop Pseudo Derivative Feedforward Controller for PMSM Drive

This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.

Hierarchical Controller Synthesis Under Linear Temporal Logic Specifications Using Dynamic Quantization

Linear temporal logic(LTL)is an intuitive and expressive language to specify complex control tasks,and how to design an efficient control strategy for LTL specification is still a challenge.In this paper,we implement the dynamic quantization technique to propose a novel hierarchical control strategy for nonlinear control systems under LTL specifications.Based on the regions of interest involved in the LTL formula,an accepting path is derived first to provide a high-level solution for the controller synthesis problem.Second,we develop a dynamic quantization based approach to verify the realization of the accepting path.The realization verification results in the necessity of the controller design and a sequence of quantization regions for the controller design.Third,the techniques of dynamic quantization and abstraction-based control are combined together to establish the local-to-global control strategy.Both abstraction construction and controller design are local and dynamic,thereby resulting in the potential reduction of the computational complexity.Since each quantization region can be considered locally and individually,the proposed hierarchical mechanism is more efficient and can solve much larger problems than many existing methods.Finally,the proposed control strategy is illustrated via two examples from the path planning and tracking problems of mobile robots.

Adaptive Nonlinear PD Controller of Two-Wheeled Self-Balancing Robot with External Force

This paper proposes an adaptive nonlinear proportional-derivative(ANPD)controller for a two-wheeled self-balancing robot(TWSB)modeled by the Lagrange equation with external forces.The proposed control scheme is designed based on the combination of a nonlinear proportional-derivative(NPD)controller and a genetic algorithm,in which the proportional-derivative(PD)parameters are updated online based on the tracking error and the preset error threshold.In addition,the genetic algorithm is employed to adaptively select initial controller parameters,contributing to system stability and improved control accuracy.The proposed controller is basic in design yet simple to implement.The ANPD controller has the advantage of being computationally lightweight and providing high robustness against external forces.The stability of the closed-loop system is rigorously analyzed and verified using Lyapunov theory,providing theoretical assurance of its robustness.Simulations and experimental results show that the TWSB robot with the proposed ANPD controller achieves quick balance and tracks target values with very small errors,demonstrating the effectiveness and performance of the proposed controller.The proposed ANPD controller demonstrates significant improvements in balancing and tracking performance for two-wheeled self-balancing robots,which has great applicability in the field of robot control systems.This represents a promising solution for applications requiring precise and stable motion control under varying external conditions.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.

In-situ pressure-preserved coring for deep exploration:Insight into the rotation behavior of the valve cover of a pressure controller

In-situ pressure-preserved coring(IPP-Coring)is considered to be the most reliable and efficient method for the identification of the scale of oil and gas resources.During IPP-Coring,because the rotation behavior of the pressure controller valve cover in different medium environments is unclear,interference between the valve cover and inner pipe may occur and negatively affect the IPP-Coring success rate.To address this issue,we conducted a series of indoor experiments employing a high-speed camera to gain greater insights into the valve cover rotation behavior in different medium environments,e.g.,air,water,and simulated drilling fluids.The results indicated that the variation in the valve cover rotation angle in the air and fluid environments can be described by a one-phase exponential decay function with a constant time parameter and by biphasic dose response function,respectively.The rotation behavior in the fluid environments exhibited distinct elastic and gravitational acceleration zones.In the fluid environments,the density clearly impacted the valve cover closing time and rotation behavior,whereas the effect of viscosity was very slight.This can be attributed to the negligible influence of the fluid viscosity on the drag coefficient found in this study;meanwhile,the density can increase the buoyancy and the time period during which the valve cover experienced a high drag coefficient.Considering these results,control schemes for the valve cover rotation behavior during IPP-Coring were proposed for different layers and geological conditions in which the different drilling fluids should be used,e.g.,the use of a high-density valve cover in high-pore pressure layers.

Observer-Based Path Tracking Controller Design for Autonomous Ground Vehicles With Input Saturation

This paper investigates the problem of path tracking control for autonomous ground vehicles(AGVs),where the input saturation,system nonlinearities and uncertainties are considered.Firstly,the nonlinear path tracking system is formulated as a linear parameter varying(LPV)model where the variation of vehicle velocity is taken into account.Secondly,considering the noise effects on the measurement of lateral offset and heading angle,an observer-based control strategy is proposed,and by analyzing the frequency domain characteristics of the derivative of desired heading angle,a finite frequency H_∞index is proposed to attenuate the effects of the derivative of desired heading angle on path tracking error.Thirdly,sufficient conditions are derived to guarantee robust H_∞performance of the path tracking system,and the calculation of observer and controller gains is converted into solving a convex optimization problem.Finally,simulation examples verify the advantages of the control method proposed in this paper.

Hybrid Optimization Based PID Controller Design for Unstable System

PID controllers play an important function in determining tuning para-meters in any process sector to deliver optimal and resilient performance for non-linear,stable and unstable processes.The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller.The Direct Multi Search(DMS)algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model.A Metaheuristics Algorithm such as,SA(Simulated Annealing),MBBO(Modified Biogeography Based Opti-mization),BBO(Biogeography Based Optimization),PBIL(Population Based Incremental Learning),ES(Evolution Strategy),StudGA(Stud Genetic Algo-rithms),PSO(Particle Swarm Optimization),StudGA(Stud Genetic Algorithms),ES(Evolution Strategy),PSO(Particle Swarm Optimization)and ACO(Ant Col-ony Optimization)are used to tune the PID controller and Smith predictor design.The effectiveness of the suggested algorithms DMS-SA,DMS-BBO,DMS-MBBO,DMS-PBIL,DMS-StudGA,DMS-ES,DMS-ACO,and DMS-PSO for a class of dead-time structures employing PID controller and Smith predictor design controllers is illustrated using unit step set point response.When compared to other optimizations,the suggested hybrid metaheuristics approach improves the time response analysis when extended to the problem of smith predictor and PID controller designed tuning.

A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions.Finally,the effectiveness of this method is verified via Simulink simulation.

Leader trajectory planning method considering constraints of formation controller

To ensure safe flight of multiple fixed-wing unmanned aerial vehicles(UAVs)formation,considering trajectory planning and formation control together,a leader trajectory planning method based on the sparse A*algorithm is introduced.Firstly,a formation controller based on prescribed performance theory is designed to control the transient and steady formation configuration,as well as the formation forming time,which not only can form the designated formation configuration but also can guarantee collision avoidance and terrain avoidance theoretically.Next,considering the constraints caused by formation controller on trajectory planning such as the safe distance,turn angle and step length,as well as the constraint of formation shape,a leader trajectory planning method based on sparse A^(*)algorithm is proposed.Simulation results show that the UAV formation can arrive at the destination safely with a short trajectory no matter keeping the formation or encountering formation transformation.

A Closed-Loop Dynamic Controller for Active Vibration Isolation Working on A Parallel Wheel-Legged Robot

Serving the Stewart mechanism as a wheel-legged structure,the most outstanding superiority of the proposed wheel-legged hybrid robot(WLHR)is the active vibration isolation function during rolling on rugged terrain.However,it is difficult to obtain its precise dynamic model,because of the nonlinearity and uncertainty of the heavy robot.This paper presents a dynamic control framework with a decentralized structure for single wheel-leg,position tracking based on model predictive control(MPC)and adaptive impedance module from inside to outside.Through the Newton-Euler dynamic model of the Stewart mechanism,the controller first creates a predictive model by combining Newton-Raphson iteration of forward kinematic and inverse kinematic calculation of Stewart.The actuating force naturally enables each strut to stretch and retract,thereby realizing six degrees-of-freedom(6-DOFs)position-tracking for Stewart wheel-leg.The adaptive impedance control in the outermost loop adjusts environmental impedance parameters by current position and force feedback of wheel-leg along Z-axis.This adjustment allows the robot to adequately control the desired support force tracking,isolating the robot body from vibration that is generated from unknown terrain.The availability of the proposed control methodology on a physical prototype is demonstrated by tracking a Bezier curve and active vibration isolation while the robot is rolling on decelerate strips.By comparing the proportional and integral(PI)and constant impedance controllers,better performance of the proposed algorithm was operated and evaluated through displacement and force sensors internally-installed in each cylinder,as well as an inertial measurement unit(IMU)mounted on the robot body.The proposed algorithm structure significantly enhances the control accuracy and vibration isolation capacity of parallel wheel-legged robot.

PD-like finite time controller with simple structure for satellite attitude control

A finite time controller with PD-like structure for satellite attitude control is proposed in this paper.The controller is constructed with simple structure based on standard PD controller.The fractional order term is designed hence system could both have strong robustness and finite time convergence rate,and the advantage of finite time control and PD control is combined in this paper.System convergence rate is discussed by Lyapunov method,and the constraint on control parameters is given by implementing the coupled term of angular velocity and attitude quaternion.Moreover,the accuracy at steady stage depending on control parameters is given hence system could converge to this field within finite time.System stability and performance is demonstrated by numerical simulation results.

System Identification and Parameter Self-Tuning Controller on Deep-Sea Mining Vehicle

System identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the system identification algorithm, recursive least square method with instrumental variables(IV-RLS), is tailored to model ‘Pioneer I’, a deep-sea mining vehicle which recently completed a 1305-meter-deep sea trial in the Xisha area of the South China Sea in August, 2021. The algorithm operates on the sensor data collected from the trial to obtain the vehicle’s kinematic model and accordingly design the parameter self-tuning controller. The performances demonstrate the accuracy of the model, and prove its generalization capability. With this model, the optimal controller has been designed, the control parameters have been self-tuned, and the response time and robustness of the system have been optimized,which validates the high efficiency on digital modelling for precision control of deep-sea mining vehicles.

Hybrid Optimized PI Controller Design for Grid Tied PV Based Electric Vehicle

Nowadays,researchers are becoming increasingly concerned about developing a highly efficient emission free transportation and energy generation system for addressing the pressing issue of environmental crisis in the form of pollution and climate change.The introduction of Electric Vehicles(EVs)solves the challenge of emission-free transportation while the necessity for decarbonized energy production is fulfilled by the installation and expansion of solar-powered Photovoltaic(PV)systems.Hence,this paper focuses on designing an effective PV based EV charging system that aids in stepping towards the achievement of a pollution free future.For overcoming the inherent intermittency associated with PV,a novel DC-DC converter is designed by integrating both Trans Z-source con-verter and Luo converter,which offers remarkable benefits of high conversion range,lesser voltage stress and excellent efficiency.A novel robust Lion Grey Wolf Optimized Proportional Integral(LGWO-PI)controller is designed for sig-nificantly strengthening the operation of the integrated converter in terms of peak overshoot,Total Harmonic Distortion(THD)and settling time.A 3’Voltage Source Inverter(VSI)is employed to convert the stable DC output from the PV sys-tem to AC,which is then used for driving the Brushless Direct Current Motor(BLDC)motor of EV.The speed of the BLDC is regulated using a PI controller.The BLDC motor gets the power supply from the grid during the unavailability of PV based power supply.The grid is integrated with the designed EV charging system through a 1’VSI and the process of grid voltage synchronization is carried out with the application of PI controller.The simulation for evaluating the operation of the presented EV charging system is done using MATLAB and the attained out-comes have validated that this introduced methodology delivers enhanced perfor-mance with optimal efficiency of 97.6%and lesser THD of 2.1%.

Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

Software-defined networking(SDN)algorithms are gaining increas-ing interest and are making networks flexible and agile.The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components,enabling flexible and dynamic network management.A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers.The deployment of the controller—that is,the controller placement problem(CPP)—becomes a vital model challenge.Through the advancements of blockchain technology,data integrity between nodes can be enhanced with no requirement for a trusted third party.Using the lat-est developments in blockchain technology,this article designs a novel sea turtle foraging optimization algorithm for the controller placement problem(STFOA-CPP)with blockchain-based intrusion detection in an SDN environ-ment.The major intention of the STFOA-CPP technique is the maximization of lifetime,network connectivity,and load balancing with the minimization of latency.In addition,the STFOA-CPP technique is based on the sea turtles’food-searching characteristics of tracking the odour path of dimethyl sulphide(DMS)released from food sources.Moreover,the presented STFOA-CPP technique can adapt with the controller’s count mandated and the shift to controller mapping to variable network traffic.Finally,the blockchain can inspect the data integrity,determine significantly malicious input,and improve the robust nature of developing a trust relationship between sev-eral nodes in the SDN.To demonstrate the improved performance of the STFOA-CPP algorithm,a wide-ranging experimental analysis was carried out.The extensive comparison study highlighted the improved outcomes of the STFOA-CPP technique over other recent approaches.