According to a type of normal nonlinear system, an indirect adaptive fuzzy （IAF） controller has been applied to those systems where no accurate mathematical models of the systems under control are available. To sati...According to a type of normal nonlinear system, an indirect adaptive fuzzy （IAF） controller has been applied to those systems where no accurate mathematical models of the systems under control are available. To satisfy with system performance, an indirect accelerated adaptive fuzzy （IAAF） controller is proposed, and its general form is presented. The general form IAAF controller ensures necessary control criteria and system＇s global stability using Lyapunov Theorem. It has been proved that the close-loop system error converges to a small neighborhood of equilibrium point. The optimal IAAF controller is derived to guarantee the process＇s shortest settling time. Simulation results indicate the IAAF controller make the system more stable, accurate, and fast.展开更多
When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of...When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of multiple fixed models, one free-running adaptive model and one re-initialized adaptive model. The fixed models are used to provide initial control to the process. The re-initialized adaptive model can be reinitialized as the selected model to improve the adaptation speed. The free-running adaptive controller is added to guarantee the overall system stability. At each instant, the best system model is selected according to the switching index and the corresponding controller is designed. During the controller design, the interaction is viewed as the measurable disturbance and eliminated by the choice of the weighting polynomial matrix. It not only eliminates the steady-state error but also decouples the system dynamically. The gtobel convergence is obtained and several simulation examples are presented to illustrate the effectiveness of the proposed controller.展开更多
We present a novel indirect adaptive fuzzy-regulated optimal control scheme for continuous-time nonlinear systems with unknown dynamics,mismatches,and disturbances.Initially,the Hamilton-Jacobi-Bellman(HJB)equation as...We present a novel indirect adaptive fuzzy-regulated optimal control scheme for continuous-time nonlinear systems with unknown dynamics,mismatches,and disturbances.Initially,the Hamilton-Jacobi-Bellman(HJB)equation associated with its performance function is derived for the original nonlinear systems.Unlike existing adaptive dynamic programming(ADP)approaches,this scheme uses a special non-quadratic variable performance function as the reinforcement medium in the actor-critic architecture.An adaptive fuzzy-regulated critic structure is correspondingly constructed to configure the weighting matrix of the performance function for the purpose of approximating and balancing the HJB equation.A concurrent self-organizing learning technique is designed to adaptively update the critic weights.Based on this particular critic,an adaptive optimal feedback controller is developed as the actor with a new form of augmented Riccati equation to optimize the fuzzy-regulated variable performance function in real time.The result is an online indirect adaptive optimal control mechanism implemented as an actor-critic structure,which involves continuous-time adaptation of both the optimal cost and the optimal control policy.The convergence and closed-loop stability of the proposed system are proved and guaranteed.Simulation examples and comparisons show the effectiveness and advantages of the proposed method.展开更多
基金supported by Plan of Excellent Leaders in Their Science in Shanghai, China (No.06XD14201).
文摘According to a type of normal nonlinear system, an indirect adaptive fuzzy （IAF） controller has been applied to those systems where no accurate mathematical models of the systems under control are available. To satisfy with system performance, an indirect accelerated adaptive fuzzy （IAAF） controller is proposed, and its general form is presented. The general form IAAF controller ensures necessary control criteria and system＇s global stability using Lyapunov Theorem. It has been proved that the close-loop system error converges to a small neighborhood of equilibrium point. The optimal IAAF controller is derived to guarantee the process＇s shortest settling time. Simulation results indicate the IAAF controller make the system more stable, accurate, and fast.
文摘When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of multiple fixed models, one free-running adaptive model and one re-initialized adaptive model. The fixed models are used to provide initial control to the process. The re-initialized adaptive model can be reinitialized as the selected model to improve the adaptation speed. The free-running adaptive controller is added to guarantee the overall system stability. At each instant, the best system model is selected according to the switching index and the corresponding controller is designed. During the controller design, the interaction is viewed as the measurable disturbance and eliminated by the choice of the weighting polynomial matrix. It not only eliminates the steady-state error but also decouples the system dynamically. The gtobel convergence is obtained and several simulation examples are presented to illustrate the effectiveness of the proposed controller.
基金Project supported by the National Natural Science Foundation of China(Nos.51805531 and 51675470)the Natural Science Foundation of Jiangsu Province,China(No.BK20150200)+1 种基金the Key R&D Program of Zhejiang Province,China(No.2020C01026)the China Postdoctoral Science Foundation(No.2020M671706)。
文摘We present a novel indirect adaptive fuzzy-regulated optimal control scheme for continuous-time nonlinear systems with unknown dynamics,mismatches,and disturbances.Initially,the Hamilton-Jacobi-Bellman(HJB)equation associated with its performance function is derived for the original nonlinear systems.Unlike existing adaptive dynamic programming(ADP)approaches,this scheme uses a special non-quadratic variable performance function as the reinforcement medium in the actor-critic architecture.An adaptive fuzzy-regulated critic structure is correspondingly constructed to configure the weighting matrix of the performance function for the purpose of approximating and balancing the HJB equation.A concurrent self-organizing learning technique is designed to adaptively update the critic weights.Based on this particular critic,an adaptive optimal feedback controller is developed as the actor with a new form of augmented Riccati equation to optimize the fuzzy-regulated variable performance function in real time.The result is an online indirect adaptive optimal control mechanism implemented as an actor-critic structure,which involves continuous-time adaptation of both the optimal cost and the optimal control policy.The convergence and closed-loop stability of the proposed system are proved and guaranteed.Simulation examples and comparisons show the effectiveness and advantages of the proposed method.
