Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to t...In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.展开更多
For a type of high⁃order discrete⁃time nonlinear systems(HDNS)whose system models are undefined,a model⁃free predictive control(MFPC)algorithm is proposed in this paper.At first,an estimation model is given by the imp...For a type of high⁃order discrete⁃time nonlinear systems(HDNS)whose system models are undefined,a model⁃free predictive control(MFPC)algorithm is proposed in this paper.At first,an estimation model is given by the improved projection algorithm to approach the controlled nonlinear system.Then,on the basis of the estimation model,a predictive controller is designed by solving the finite time domain rolling optimization quadratic function,and the controller’s explicit analytic solution is also obtained.Furthermore,the closed⁃loop system's stability can be ensured.Finally,the results of simulation reveal that the presented control strategy has a faster convergence speed as well as more stable dynamic property compared with the model⁃free sliding mode control(MFSC).展开更多
Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch ...Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch systems with speed disturbance suppression.First,an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed,whose rolling time is 1s,and the predicted value serves as a feedforward to obtain the early compensation variation of the pitch angle.Second,a function of the multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed,and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative(PPD).In addition,the compound MFAPC framework is synthetically obtained,whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working conditions.Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions,and a detailed comparison is finally presented.The results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed,but also meet the stability of wind power and the security of grid-connections for all operating conditions.展开更多
The performance of solar heating systems is significantly influenced by outdoor weather fluctuations and building heating loads,leading to dynamic variations that undermine the efficacy of rule-based control(RBC)strat...The performance of solar heating systems is significantly influenced by outdoor weather fluctuations and building heating loads,leading to dynamic variations that undermine the efficacy of rule-based control(RBC)strategies.Additionally,the hydraulic and thermal time-delay characteristics frequently lead to delays in control points for real-time optimization(RTO)control strategies.While Model Predictive Control(MPC)effectively addresses these dynamic and time-delay issues in solar heating systems,its substantial computational demands limit its real-world applications.To overcome these challenges,this study proposes a Model-Free Predictive Control(MFPC)approach utilizing Deep reinforcement learning(DRL).Through TRNSYS simulations,the study conducts a comparison of the performance and energy consumption of RBC and MFPC systems,focusing on a residential solar heating system in Lhasa,Xizang as a case study.The results demonstrate that the MFPC method reduces unmet heating demand by 31%compared to traditional RBC strategies,improves solar collection efficiency by nearly 12%,and decreases tank heat loss by 2.2%.When accounting for thermal storage effects,the optimized MFPC strategy achieves a reduction in net energy consumption of 25.6%.展开更多
This paper addresses a crucial challenge in the domain of smart factories and intelligent warehouse logistics,focusing on conflict-free planning and the smooth operation of large-scale nonlinear mobile robots.To tackl...This paper addresses a crucial challenge in the domain of smart factories and intelligent warehouse logistics,focusing on conflict-free planning and the smooth operation of large-scale nonlinear mobile robots.To tackle the challenges associated with scheduling large-scale mobile robots,an improved space-time multi-robot planning algorithm is proposed.The cloud servers are adopted in this algorithm for computation,which enables faster response to the planning requirements of large-scale mobile robots.Furthermore,enhancements to a model-free adaptive predictive control method are proposed to enhance the networked control effectiveness of the nonlinear robots.The algorithm's capability to accommodate conflict-free path planning for large-scale mobile robots is demonstrated through simulation results.Experimental findings further validate the effectiveness of the cloud-based large-scale mobile robot planning and control system in achieving both conflict-free path planning and accurate path tracking.This research holds substantial implications for enhancing logistics transportation efficiency and driving ad-vancements in the field of smart factories and intelligent warehouse logistics.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
基金supported in part by the National Natural Science Foundation of China(62403396,62433018,62373113)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011527,2023B1515120010)the Postdoctoral Fellowship Program of CPSF(GZB20240621)
文摘In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61803224)the Natural Science Foundation of Shandong Province(Grant No.ZR2019QF005).
文摘For a type of high⁃order discrete⁃time nonlinear systems(HDNS)whose system models are undefined,a model⁃free predictive control(MFPC)algorithm is proposed in this paper.At first,an estimation model is given by the improved projection algorithm to approach the controlled nonlinear system.Then,on the basis of the estimation model,a predictive controller is designed by solving the finite time domain rolling optimization quadratic function,and the controller’s explicit analytic solution is also obtained.Furthermore,the closed⁃loop system's stability can be ensured.Finally,the results of simulation reveal that the presented control strategy has a faster convergence speed as well as more stable dynamic property compared with the model⁃free sliding mode control(MFSC).
基金supported by National Natural Science Foundation of China(Grant No.50776005 and 51577008)。
文摘Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch systems with speed disturbance suppression.First,an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed,whose rolling time is 1s,and the predicted value serves as a feedforward to obtain the early compensation variation of the pitch angle.Second,a function of the multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed,and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative(PPD).In addition,the compound MFAPC framework is synthetically obtained,whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working conditions.Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions,and a detailed comparison is finally presented.The results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed,but also meet the stability of wind power and the security of grid-connections for all operating conditions.
基金supported by the National Natural Science Foundation of China(Project Nos.U23A20657,U20A20311).
文摘The performance of solar heating systems is significantly influenced by outdoor weather fluctuations and building heating loads,leading to dynamic variations that undermine the efficacy of rule-based control(RBC)strategies.Additionally,the hydraulic and thermal time-delay characteristics frequently lead to delays in control points for real-time optimization(RTO)control strategies.While Model Predictive Control(MPC)effectively addresses these dynamic and time-delay issues in solar heating systems,its substantial computational demands limit its real-world applications.To overcome these challenges,this study proposes a Model-Free Predictive Control(MFPC)approach utilizing Deep reinforcement learning(DRL).Through TRNSYS simulations,the study conducts a comparison of the performance and energy consumption of RBC and MFPC systems,focusing on a residential solar heating system in Lhasa,Xizang as a case study.The results demonstrate that the MFPC method reduces unmet heating demand by 31%compared to traditional RBC strategies,improves solar collection efficiency by nearly 12%,and decreases tank heat loss by 2.2%.When accounting for thermal storage effects,the optimized MFPC strategy achieves a reduction in net energy consumption of 25.6%.
基金supported in part by the Natural Science Foundation of Hunan Province(Grant 2023J110015)the Project of State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle(Grant 72275007)the National Natural Science Foundation of China(Grants 62403075 and Vehicle 62293513).
文摘This paper addresses a crucial challenge in the domain of smart factories and intelligent warehouse logistics,focusing on conflict-free planning and the smooth operation of large-scale nonlinear mobile robots.To tackle the challenges associated with scheduling large-scale mobile robots,an improved space-time multi-robot planning algorithm is proposed.The cloud servers are adopted in this algorithm for computation,which enables faster response to the planning requirements of large-scale mobile robots.Furthermore,enhancements to a model-free adaptive predictive control method are proposed to enhance the networked control effectiveness of the nonlinear robots.The algorithm's capability to accommodate conflict-free path planning for large-scale mobile robots is demonstrated through simulation results.Experimental findings further validate the effectiveness of the cloud-based large-scale mobile robot planning and control system in achieving both conflict-free path planning and accurate path tracking.This research holds substantial implications for enhancing logistics transportation efficiency and driving ad-vancements in the field of smart factories and intelligent warehouse logistics.
