国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In order to overcome the limitations of the full-state symmetry constraints and the frequent update of the control policy, and to make the infinite horizon cost function optimal, a controller design method with event-triggered integral reinforcement learning with state constraints is proposed for a class of affine nonlinear continuous systems with partial unknown dynamics. It is a data-based online policy iteration approach. Firstly, system transformation is introduced to transform a constrained system into an unconstrained system. Next, based on the event triggering mechanism and integral reinforcement learning algorithm, by alternating system transformation, policy evaluation, and policy improvement, the system will satisfy the full-state constraints, the cost function and control policy will converge make optimal. At the same time, it can reduce the update frequency of the control policy. In addition, the stability of the system is strictly analyzed by constructing the Lyapunov function. The simulation experiment of the single-link robotic arm is given to verify the effectiveness of the proposed approach.