A robust control for uncertain nonlinear systems based on T-S fuzzy model is discussed in this paper. First, a T-S fuzzy system is adopted to model the uncertain nonlinear systems. Then, for the system with input vari...A robust control for uncertain nonlinear systems based on T-S fuzzy model is discussed in this paper. First, a T-S fuzzy system is adopted to model the uncertain nonlinear systems. Then, for the system with input variables adopting standard fuzzy partitions, the efficient maximal overlapped-rules group （EMORG） is presented, and a new sufficient condition to check the stability of T-S fuzzy system with uncertainty is derived, which is expressed in terms of Linear Matrix Inequalities. The derived stability condition, which only requires a local common positive definite matrix in each EMORG, can reduce the conservatism and difficulty in existing stability conditions. Finally, a simulation example shows the proposed approach is effective.展开更多
This paper presents a method of state estimation for uncertain nonlinear systems described by multiple models approach. The uncertainties, supposed as norm bounded type, are caused by some parameters＇ variations of t...This paper presents a method of state estimation for uncertain nonlinear systems described by multiple models approach. The uncertainties, supposed as norm bounded type, are caused by some parameters＇ variations of the nonlinear system. Linear matri~ inequalities （LMIs） have been established in order to ensure the stability conditions of the multiple observer which lead to determine the estimation gains. A sliding mode gain has been added in order to compensate the uncertainties. Numerical simulations through a state space model of a real process have been realized to show the robustness of the synthesized observer.展开更多
A new approach to model and control an unknown system using subjective uncertain rules is proposed. This method is established by combining the grey system theory and the qualitative simulation method. The proposed ap...A new approach to model and control an unknown system using subjective uncertain rules is proposed. This method is established by combining the grey system theory and the qualitative simulation method. The proposed approach mainly contains three steps. In the first step, subjective uncertain rules are accumulated gradually during cognizing the system; the mapping relations between the system inputs and outputs are built and represented using the grey qualitative matrix in the second step; in the third step,the generalized whitening function is defined to realize the transformation between qualitative and quantitative information. Besides the theoretical results, two sets of simulations based on a water level control system are conducted comparatively to demonstrate the effectiveness of the proposed method. The water level expectation is set to be constant in the first set, while it changes in the second set. The simulation results show that the proposed method tracks the water level expectation well. By combining the proposed method with proportional-integral-derivative(PID) or fuzzy logic controller(FLC), it can be concluded that the system can reach the stable state more quickly and the overshoot can also be reduced compared to using PID or FLC alone.展开更多
In this paper, a fuzzy adaptive tracking control for uncertain strict-feedback nonlinear systems with unknown bounded disturbances is proposed. The generalized fuzzy hyperbolic model (GFHM) with better approximation p...In this paper, a fuzzy adaptive tracking control for uncertain strict-feedback nonlinear systems with unknown bounded disturbances is proposed. The generalized fuzzy hyperbolic model (GFHM) with better approximation performance is used to approximate the unknown nonlinear function in the system. The dynamic surface control (DSC) is used to design the controller, which not only avoids the “explosion of complexity” problem in the process of repeated derivation, but also makes the control system simpler in structure and lower in computational cost because only one adaptive law is designed in the controller design process. Through the Lyapunov stability analysis, all signals in the closed loop system designed in this paper are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the method is verified by a simulation example.展开更多
This work deals with robust inverse neural control strategy for a class of single-input single-output(SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the ...This work deals with robust inverse neural control strategy for a class of single-input single-output(SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model(DNM)is used to learn the behavior of the system, then, an inverse neural model(INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation(BP) algorithm. In this work, the sliding mode-backpropagation(SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy.展开更多
We study the stability analysis and control synthesis of uncertain discrete-time two-dimensional（2D） systems.The mathematical model of the discrete-time 2D system is established upon the well-known Roesser model,and...We study the stability analysis and control synthesis of uncertain discrete-time two-dimensional（2D） systems.The mathematical model of the discrete-time 2D system is established upon the well-known Roesser model,and the uncertainty phenomenon,which appears typically in practical environments,is modeled by a convex bounded（polytope type） uncertain domain.The stability analysis and control synthesis of uncertain discrete-time 2D systems are then developed by applying the Lyapunov stability theory.In the processes of stability analysis and control synthesis,the obtained stability/stabilzaition conditions become less conservative by applying some novel relaxed techniques.Moreover,the obtained results are formulated in the form of linear matrix inequalities,which can be easily solved via standard numerical software.Finally,numerical examples are given to demonstrate the effectiveness of the obtained results.展开更多
This paper studies the problem of the guaranteed cost control via static-state feedback controllers for a class of two-dimensional (2-D) discrete systems described by the Fornasini-Marchesini second local state-space ...This paper studies the problem of the guaranteed cost control via static-state feedback controllers for a class of two-dimensional (2-D) discrete systems described by the Fornasini-Marchesini second local state-space (FMSLSS) model with norm bounded uncertainties. A convex optimization problem with linear matrix inequality (LMI) constraints is formulated to design the suboptimal guaranteed cost controller which ensures the quadratic stability of the closed-loop system and minimizes the associated closed-loop cost function. Application of the proposed controller design method is illustrated with the help of one example.展开更多
The lateral stability for railway vehicle dynamic system with uncertainparameters and nonlinear uncertain force vector is studied by using the Lyapunov stability theory. Arobust stability condition for the considered ...The lateral stability for railway vehicle dynamic system with uncertainparameters and nonlinear uncertain force vector is studied by using the Lyapunov stability theory. Arobust stability condition for the considered system is derived, and the obtained stability boundsare not necessarily symmetric with respect to the origin in the parameter space. The lateralstability analysis for a railway bogie model is analyzed by using the proposed approach. Thesymmetric and asymmetric results are both given and the influence of the adjustable parameter betaon the stability bounds is also discussed. With the help of the proposed method, the robuststability analysis can provide a reference for the design of the railway vehicle systems.展开更多
基金supported by the National Natural Science Foundation of China (No.70471087)China Postdoctoral Science Foundation Funded Project(No.20080430929)Liaoning Province Education Bureau Foundation (No.20060106)
文摘A robust control for uncertain nonlinear systems based on T-S fuzzy model is discussed in this paper. First, a T-S fuzzy system is adopted to model the uncertain nonlinear systems. Then, for the system with input variables adopting standard fuzzy partitions, the efficient maximal overlapped-rules group （EMORG） is presented, and a new sufficient condition to check the stability of T-S fuzzy system with uncertainty is derived, which is expressed in terms of Linear Matrix Inequalities. The derived stability condition, which only requires a local common positive definite matrix in each EMORG, can reduce the conservatism and difficulty in existing stability conditions. Finally, a simulation example shows the proposed approach is effective.
文摘This paper presents a method of state estimation for uncertain nonlinear systems described by multiple models approach. The uncertainties, supposed as norm bounded type, are caused by some parameters＇ variations of the nonlinear system. Linear matri~ inequalities （LMIs） have been established in order to ensure the stability conditions of the multiple observer which lead to determine the estimation gains. A sliding mode gain has been added in order to compensate the uncertainties. Numerical simulations through a state space model of a real process have been realized to show the robustness of the synthesized observer.
基金supported by National Natural Science Foundation of China(No.61075073 and 61375079)
文摘A new approach to model and control an unknown system using subjective uncertain rules is proposed. This method is established by combining the grey system theory and the qualitative simulation method. The proposed approach mainly contains three steps. In the first step, subjective uncertain rules are accumulated gradually during cognizing the system; the mapping relations between the system inputs and outputs are built and represented using the grey qualitative matrix in the second step; in the third step,the generalized whitening function is defined to realize the transformation between qualitative and quantitative information. Besides the theoretical results, two sets of simulations based on a water level control system are conducted comparatively to demonstrate the effectiveness of the proposed method. The water level expectation is set to be constant in the first set, while it changes in the second set. The simulation results show that the proposed method tracks the water level expectation well. By combining the proposed method with proportional-integral-derivative(PID) or fuzzy logic controller(FLC), it can be concluded that the system can reach the stable state more quickly and the overshoot can also be reduced compared to using PID or FLC alone.
文摘In this paper, a fuzzy adaptive tracking control for uncertain strict-feedback nonlinear systems with unknown bounded disturbances is proposed. The generalized fuzzy hyperbolic model (GFHM) with better approximation performance is used to approximate the unknown nonlinear function in the system. The dynamic surface control (DSC) is used to design the controller, which not only avoids the “explosion of complexity” problem in the process of repeated derivation, but also makes the control system simpler in structure and lower in computational cost because only one adaptive law is designed in the controller design process. Through the Lyapunov stability analysis, all signals in the closed loop system designed in this paper are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the method is verified by a simulation example.
文摘This work deals with robust inverse neural control strategy for a class of single-input single-output(SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model(DNM)is used to learn the behavior of the system, then, an inverse neural model(INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation(BP) algorithm. In this work, the sliding mode-backpropagation(SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy.
基金Project supported by the National Natural Science Foundation of China (Grant No. 61104010)
文摘We study the stability analysis and control synthesis of uncertain discrete-time two-dimensional（2D） systems.The mathematical model of the discrete-time 2D system is established upon the well-known Roesser model,and the uncertainty phenomenon,which appears typically in practical environments,is modeled by a convex bounded（polytope type） uncertain domain.The stability analysis and control synthesis of uncertain discrete-time 2D systems are then developed by applying the Lyapunov stability theory.In the processes of stability analysis and control synthesis,the obtained stability/stabilzaition conditions become less conservative by applying some novel relaxed techniques.Moreover,the obtained results are formulated in the form of linear matrix inequalities,which can be easily solved via standard numerical software.Finally,numerical examples are given to demonstrate the effectiveness of the obtained results.
文摘This paper studies the problem of the guaranteed cost control via static-state feedback controllers for a class of two-dimensional (2-D) discrete systems described by the Fornasini-Marchesini second local state-space (FMSLSS) model with norm bounded uncertainties. A convex optimization problem with linear matrix inequality (LMI) constraints is formulated to design the suboptimal guaranteed cost controller which ensures the quadratic stability of the closed-loop system and minimizes the associated closed-loop cost function. Application of the proposed controller design method is illustrated with the help of one example.
基金This project is supported by National Natural Science Foundation of China (No.50175094)Excellent PhD Dissertation Foundation of Ministry of Education, China (No.200048).
文摘The lateral stability for railway vehicle dynamic system with uncertainparameters and nonlinear uncertain force vector is studied by using the Lyapunov stability theory. Arobust stability condition for the considered system is derived, and the obtained stability boundsare not necessarily symmetric with respect to the origin in the parameter space. The lateralstability analysis for a railway bogie model is analyzed by using the proposed approach. Thesymmetric and asymmetric results are both given and the influence of the adjustable parameter betaon the stability bounds is also discussed. With the help of the proposed method, the robuststability analysis can provide a reference for the design of the railway vehicle systems.
