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
This paper considers the output tracking problem for more general classes of stochastic nonlinear systems with unknown control coefficients and driven by noise of unknown covariance. By utilizing the radial basis func...This paper considers the output tracking problem for more general classes of stochastic nonlinear systems with unknown control coefficients and driven by noise of unknown covariance. By utilizing the radial basis function neural network approximation method and backstepping technique, we successfully construct a controller to guarantee the solution process to be bounded in probability.The tracking error signal is 4th-moment semi-globally uniformly ultimately bounded（SGUUB） and can be regulated into a small neighborhood of the origin in probability. A simulation example is given to demonstrate the effectiveness of the control scheme.展开更多
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.
基金supported by National Natural Science Foundation of China(Nos.61573172,61305149 and 61403174)333 High-level Talents Training Program in Jiangsu Province(No.BRA2015352)Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province(No.15KJB510011)
文摘This paper considers the output tracking problem for more general classes of stochastic nonlinear systems with unknown control coefficients and driven by noise of unknown covariance. By utilizing the radial basis function neural network approximation method and backstepping technique, we successfully construct a controller to guarantee the solution process to be bounded in probability.The tracking error signal is 4th-moment semi-globally uniformly ultimately bounded（SGUUB） and can be regulated into a small neighborhood of the origin in probability. A simulation example is given to demonstrate the effectiveness of the control scheme.
