Event-Triggered Data-Driven Control of Nonlinear Systems via Q-Learning
研究了基于Q学习的非线性系统事件触发数据驱动控制方法，通过伪偏导数描述输入输出映射，利用动态惩罚因子加速误差收敛，并提出新的触发规则以节省通信资源，仿真验证了有效性。
This article aims to study event-triggered data-driven control of nonlinear systems via Q-learning. An input-output mapping is described by a pseudo-partial derivatives form. A Q-learning-based optimization criterion is provided to establish a data-driven control law. A dynamic penalty factor composed of tracking errors is supplied to accelerate errors convergence. Consequently, a novel triggering rule related to this factor and performance cost is proposed to save communication resources. Sufficient conditions are developed for guaranteeing the ultimately uniform boundedness of the resultant tracking errors system. Two simulation studies are executed to verify the effectiveness of the presented scheme.