The development of control techniques to mitigate the effects of unknown hysteresis preceding with plants has recently re-attracted significant attention. In this paper, we first give a brief review of presently devel...The development of control techniques to mitigate the effects of unknown hysteresis preceding with plants has recently re-attracted significant attention. In this paper, we first give a brief review of presently developed hysteresis models and hysteresis compensating control methods.Then, with the use of the Prandtl-Ishlinskii hysteresis model, we propose a robust adaptive control scheme. The novelty is that the model of hysteresis nonlinearities is firstly fused with the available control techniques without necessarily constructing a hysteresis inverse. The global stability of the adaptive system and tracking a desired trajectory to a certain precision are achieved. Simulations performed on a nonlinear system illustrate and clarify the approach.展开更多
An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of...An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks ( NN) . The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semiglobal stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.展开更多
基金Supported by the Concordia University under Research Chair Grant
文摘The development of control techniques to mitigate the effects of unknown hysteresis preceding with plants has recently re-attracted significant attention. In this paper, we first give a brief review of presently developed hysteresis models and hysteresis compensating control methods.Then, with the use of the Prandtl-Ishlinskii hysteresis model, we propose a robust adaptive control scheme. The novelty is that the model of hysteresis nonlinearities is firstly fused with the available control techniques without necessarily constructing a hysteresis inverse. The global stability of the adaptive system and tracking a desired trajectory to a certain precision are achieved. Simulations performed on a nonlinear system illustrate and clarify the approach.
基金This work was partially supported by National Science Foundation of China(No.50265001)Guangxi Science Foundation(No.0339068).
文摘An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks ( NN) . The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semiglobal stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.
