Intermittent Feedback Optimal Control of Saturated-Input Nonlinear Systems via Adaptive Dynamic Programming
针对非对称输入饱和的非线性系统，提出一种基于动态事件触发机制的间歇反馈最优控制方案，通过自适应动态规划中的批评神经网络学习最优控制律，降低计算资源消耗，并保证系统稳定性。
This article develops an intermittent feedback optimal control scheme for nonlinear systems with asymmetric input saturation using a dynamic event-triggering mechanism. First, an infinite horizon nonquadratic value function with a novel integrand is formulated for the studied system to evaluate the performance, tackle the asymmetric input saturation, and remove certain rigorous assumptions in prior related studies. Second, a critic neural network (CNN) in the adaptive dynamic programming framework is constructed to obtain the optimal event-triggered control (ETC). An improved concurrent learning technique is then developed to update the CNN’s weights without requiring the persistence of excitation condition. Compared with the static ETC scheme, the present dynamic ETC strategy consumes fewer computational resources. Third, the uniform ultimate boundedness of the state, the weight estimation error, and the internal dynamic variable are assured, and the Zeno behavior is excluded. Finally, a rotational-translational actuator system is given to validate the developed intermittent feedback optimal control scheme.