Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,s...Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,so it can handle optimal control problems effectively.This paper proposes an inverse reinforcement learning optimal control method for Takagi-Sugeno(T-S)fuzzy systems.Based on learner systems,an expert system is constructed,where the learner system only knows the expert system's optimal control policy.To reconstruct the unknown cost function,we firstly develop a model-based inverse reinforcement learning algorithm for the case that systems dynamics are known.The developed model-based learning algorithm is consists of two learning stages:an inner reinforcement learning loop and an outer inverse optimal control loop.The inner loop desires to obtain optimal control policy via learner's cost function and the outer loop aims to update learner's state-penalty matrices via only using expert's optimal control policy.Then,to eliminate the requirement that the system dynamics must be known,a data-driven integral learning algorithm is presented.It is proved that the presented two algorithms are convergent and the developed inverse reinforcement learning optimal control scheme can ensure the controlled fuzzy learner systems to be asymptotically stable.Finally,we apply the proposed fuzzy optimal control to the truck-trailer system,and the computer simulation results verify the effectiveness of the presented approach.展开更多
The projection of the chaotic attractor observed from the Lorenz system in the X-Z plane is like a butterfly, hence the classical Lorenz system is widely known as the butterfly attractor, and has served as a prototype...The projection of the chaotic attractor observed from the Lorenz system in the X-Z plane is like a butterfly, hence the classical Lorenz system is widely known as the butterfly attractor, and has served as a prototype model for studying chaotic behaviour since it was coined. In this work we take one step further to investigate some fundamental dynamic behaviours of a novel hybrid Takagi-Sugeno （TS） fuzzy Lorenz-type system, which is essentially derived from the delta-operator-based TS fuzzy modelling for complex nonlinear systems, and contains the original Lorenz system of continuous-time TS fuzzy form as a special case. By simply and appropriately tuning the additional parametric perturbations in the two-rule hybrid TS fuzzy Lorenz-type system, complex （two-wing） butterfly attractors observed from this system in the three dimensional （3D） X-Y-Z space are created, which have not yet been reported in the literature, and the forming mechanism of the compound structures have been numerically investigated.展开更多
By the best approximation theory, it is first proved that the SISO （single-input single-output） linear Takagi-Sugeno （TS） fuzzy systems can approximate an arbitrary polynomial which, according to Weierstrass appro...By the best approximation theory, it is first proved that the SISO （single-input single-output） linear Takagi-Sugeno （TS） fuzzy systems can approximate an arbitrary polynomial which, according to Weierstrass approximation theorem, can uniformly approximate any continuous functions on the compact domain. Then new sufficient conditions for general linear SISO TS fuzzy systems as universal approximators are obtained. Formulae are derived to calculate the number of input fuzzy sets to satisfy the given approximation accuracy. Then the presented result is compared with the existing literature＇s results. The comparison shows that the presented result needs less input fuzzy sets, which can simplify the design of the fuzzy system, and examples are given to show its effectiveness.展开更多
为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,...为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,针对经典TS模糊预测算法应用于波动负荷时出现的输出异常置0情况,提出一种范围自适应修正的改进方法,该方法能消除一类算法应用机理导致的异常值,从而提高TS模糊算法对波动负荷无功功率预测的可靠性和准确性;最后,基于模型训练时间约束,建立无功功率半周期滚动预测控制模型,提前10 ms预测无功功率,改善了SVC传统控制系统响应的滞后特性。仿真结果表明,相比于SVC传统控制方法,所提方法的平均闪变改善率提高了54.17%,验证了所提方法对闪变现象的抑制效果提升显著。展开更多
Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structur...Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.展开更多
This paper proposes fuzzy model predictive control(FMPC)strategies for nonlinear interconnected systems based mainly on a system decomposition approach.First,the Takagi-Sugeno(TS)fuzzy model is formulated in such a wa...This paper proposes fuzzy model predictive control(FMPC)strategies for nonlinear interconnected systems based mainly on a system decomposition approach.First,the Takagi-Sugeno(TS)fuzzy model is formulated in such a way to describe the behavior of the nonlinear system.Based on that description,a fuzzy model predictive control is determined.The system under consideration is decomposed into several subsystems.For each subsystem,the main idea consists of the decomposition of the control action into two parts:The decentralized part contains the parameters of the subsystem and the centralized part contains the elements of other subsystems.According to such decomposition,two strategies are defined aiming to circumvent the problems caused by interconnection bet ween subsystems.The feasibility and efficiency of the proposed method are illustrated through numerical examples.展开更多
Attitude identification method for unmanned helicopter based on fuzzy model at hovering is presented. The dynamical attitude model is considered as basis for attitude control and it is very complex. To reduce the comp...Attitude identification method for unmanned helicopter based on fuzzy model at hovering is presented. The dynamical attitude model is considered as basis for attitude control and it is very complex. To reduce the complexity of model, nonlinear model of unmanned helicopter with unknown parameters are to be determined by fuzzy system first and then derivative based gradient method is used to identify unknown parameters of model. Gradient method is used due to ability that fuzzy system is not necessarily to be linear in parameters, therefore all fuzzy sets for input and output can be adjusted. The validity of the proposed model was verified using experimental data obtained by the commercially available flight simulator X-Plane. The simulation results showed high accuracy of the modeling method and attitude dynamics data matched well with experimental data.展开更多
The guaranteed cost control challenge for discrete-time nonlinear systems that include time-varying delays is the central topic of this paper.We propose a novel synthesis of state feedback controllers to achieve asymp...The guaranteed cost control challenge for discrete-time nonlinear systems that include time-varying delays is the central topic of this paper.We propose a novel synthesis of state feedback controllers to achieve asymptotic stability and ensure a satisfactory level of performance in the closed-loop system.To address the time-varying parameters and control,we leverage the Takagi-Sugeno(TS)fuzzy formalism,the Lyapunov-Krasovskii functional(LKF)framework,and free-weighting matrices.Furthermore,we establish novel delay-dependent linear matrix inequalities(LMIs)that guarantee the stability of the closed-loop system.To illustrate the benefits of our approach and compare it with existing literature works,we provide numerical examples.These examples showcase the practical application and advantages of the suggested approach.展开更多
基金The National Natural Science Foundation of China(62173172).
文摘Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,so it can handle optimal control problems effectively.This paper proposes an inverse reinforcement learning optimal control method for Takagi-Sugeno(T-S)fuzzy systems.Based on learner systems,an expert system is constructed,where the learner system only knows the expert system's optimal control policy.To reconstruct the unknown cost function,we firstly develop a model-based inverse reinforcement learning algorithm for the case that systems dynamics are known.The developed model-based learning algorithm is consists of two learning stages:an inner reinforcement learning loop and an outer inverse optimal control loop.The inner loop desires to obtain optimal control policy via learner's cost function and the outer loop aims to update learner's state-penalty matrices via only using expert's optimal control policy.Then,to eliminate the requirement that the system dynamics must be known,a data-driven integral learning algorithm is presented.It is proved that the presented two algorithms are convergent and the developed inverse reinforcement learning optimal control scheme can ensure the controlled fuzzy learner systems to be asymptotically stable.Finally,we apply the proposed fuzzy optimal control to the truck-trailer system,and the computer simulation results verify the effectiveness of the presented approach.
基金Project partially supported by the Natural Science Foundation of Educational Committee of Anhui Province, China （Grant No 2006kj250B）.
文摘The projection of the chaotic attractor observed from the Lorenz system in the X-Z plane is like a butterfly, hence the classical Lorenz system is widely known as the butterfly attractor, and has served as a prototype model for studying chaotic behaviour since it was coined. In this work we take one step further to investigate some fundamental dynamic behaviours of a novel hybrid Takagi-Sugeno （TS） fuzzy Lorenz-type system, which is essentially derived from the delta-operator-based TS fuzzy modelling for complex nonlinear systems, and contains the original Lorenz system of continuous-time TS fuzzy form as a special case. By simply and appropriately tuning the additional parametric perturbations in the two-rule hybrid TS fuzzy Lorenz-type system, complex （two-wing） butterfly attractors observed from this system in the three dimensional （3D） X-Y-Z space are created, which have not yet been reported in the literature, and the forming mechanism of the compound structures have been numerically investigated.
文摘By the best approximation theory, it is first proved that the SISO （single-input single-output） linear Takagi-Sugeno （TS） fuzzy systems can approximate an arbitrary polynomial which, according to Weierstrass approximation theorem, can uniformly approximate any continuous functions on the compact domain. Then new sufficient conditions for general linear SISO TS fuzzy systems as universal approximators are obtained. Formulae are derived to calculate the number of input fuzzy sets to satisfy the given approximation accuracy. Then the presented result is compared with the existing literature＇s results. The comparison shows that the presented result needs less input fuzzy sets, which can simplify the design of the fuzzy system, and examples are given to show its effectiveness.
文摘为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,针对经典TS模糊预测算法应用于波动负荷时出现的输出异常置0情况,提出一种范围自适应修正的改进方法,该方法能消除一类算法应用机理导致的异常值,从而提高TS模糊算法对波动负荷无功功率预测的可靠性和准确性;最后,基于模型训练时间约束,建立无功功率半周期滚动预测控制模型,提前10 ms预测无功功率,改善了SVC传统控制系统响应的滞后特性。仿真结果表明,相比于SVC传统控制方法,所提方法的平均闪变改善率提高了54.17%,验证了所提方法对闪变现象的抑制效果提升显著。
基金This work was supported by the Natural Science Foundation of Hebei Province(F2019203505).
文摘Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.
文摘This paper proposes fuzzy model predictive control(FMPC)strategies for nonlinear interconnected systems based mainly on a system decomposition approach.First,the Takagi-Sugeno(TS)fuzzy model is formulated in such a way to describe the behavior of the nonlinear system.Based on that description,a fuzzy model predictive control is determined.The system under consideration is decomposed into several subsystems.For each subsystem,the main idea consists of the decomposition of the control action into two parts:The decentralized part contains the parameters of the subsystem and the centralized part contains the elements of other subsystems.According to such decomposition,two strategies are defined aiming to circumvent the problems caused by interconnection bet ween subsystems.The feasibility and efficiency of the proposed method are illustrated through numerical examples.
文摘Attitude identification method for unmanned helicopter based on fuzzy model at hovering is presented. The dynamical attitude model is considered as basis for attitude control and it is very complex. To reduce the complexity of model, nonlinear model of unmanned helicopter with unknown parameters are to be determined by fuzzy system first and then derivative based gradient method is used to identify unknown parameters of model. Gradient method is used due to ability that fuzzy system is not necessarily to be linear in parameters, therefore all fuzzy sets for input and output can be adjusted. The validity of the proposed model was verified using experimental data obtained by the commercially available flight simulator X-Plane. The simulation results showed high accuracy of the modeling method and attitude dynamics data matched well with experimental data.
文摘The guaranteed cost control challenge for discrete-time nonlinear systems that include time-varying delays is the central topic of this paper.We propose a novel synthesis of state feedback controllers to achieve asymptotic stability and ensure a satisfactory level of performance in the closed-loop system.To address the time-varying parameters and control,we leverage the Takagi-Sugeno(TS)fuzzy formalism,the Lyapunov-Krasovskii functional(LKF)framework,and free-weighting matrices.Furthermore,we establish novel delay-dependent linear matrix inequalities(LMIs)that guarantee the stability of the closed-loop system.To illustrate the benefits of our approach and compare it with existing literature works,we provide numerical examples.These examples showcase the practical application and advantages of the suggested approach.
