基于数据ADP算法的一类带有执行器饱和的未知离散时间系统最优跟踪控制

针对一类带有执行器饱和的未知动态离散时间非线性系统, 提出了一种新的最优跟踪控制方案. 该方案基于迭代自适应动态规划算法, 为了实现最优控制, 首先建立了未知系统动态的数据辨识器. 通过引入M网络, 获得了稳态控制的精确表达式. 为了消除执行器饱和的影响, 提出了一个非二次的性能指标函数. 然后提出了一种迭代自适应动态规划算法获得最优跟踪控制的解, 并给出了收敛性分析. 为了实现最优控制方案, 神经网络被用来构建数据辨识器、计算性能指标函数、近似最优控制策略和求解稳态控制. 仿真结果验证了本文所提出的最优跟踪控制方法的有效性.

Optimal Tracking Control for a Class of Unknown Discrete-time Systems with Actuator Saturation via Data-based ADP Algorithm

A novel optimal tracking control method for a class of discrete-time systems with actuator saturation and unknown dynamics is proposed in this paper. The scheme is based on the iterative adaptive dynamic programming (ADP) algorithm. In order to implement the control scheme, a data-based identifier is first constructed for the unknown system dynamics. By introducing the M network, the explicit formula of the steady control is achieved. In order to eliminate the effect of the actuator saturation, a nonquadratic performance functional is presented, and then an iterative ADP algorithm is established to achieve the optimal tracking control solution with convergence analysis. For implementing the optimal control method, neural networks are used to establish the data-based identifier, compute the performance index functional, approximate the optimal control policy and solve the steady control, respectively. Simulation example is provided to verify the effectiveness of the presented optimal tracking control scheme.