严格反馈系统的事件触发学习控制

本文针对一类严格反馈非线性系统,提出了基于确定学习的事件触发控制方案.首先,在本地控制测试端设计自适应神经网络控制,并在控制过程中实现系统未知动态的知识获取和存储.随后,基于常值权值,设计了新颖的事件触发控制器和事件触发条件.结合李雅普诺夫稳定性分析和非线性脉冲动态系统原理,验证了所提方案能够保证跟踪误差收敛到零的小邻域内以及所有闭环信号是最终一致有界的.此外,本文所提方案采用常值权值代替了估计权值,使得所提方案易于实现,暂态性能好和网络资源占用少.最后,通过对比仿真结果证明了所提方案的有效性.

Event-triggered learning control for strict-feedback systems

This paper proposes a novel event-triggered tracking control scheme for a class of strict-feedback nonlinear systems based on deterministic learning. Firstly, this paper designs an adaptive neural network control for strict-feedback systems on the local control test side and realizes the knowledge acquirement of unknown dynamics in the control process. Then, based on stored constant weights, this paper designs a novel event-triggered controller and a triggering condition. By combining Lyapunov stability analysis with nonlinear impulse dynamic system theory, the proposed control scheme can be verified to guarantee that the tracking error converges to a small neighborhood of zero and all the signals in the closedloop system are uniformly ultimately bounded. Moreover, the proposed control scheme uses constant weights instead of estimated weights, which makes the proposed scheme possess some good features including the easy-to-implement triggered condition, the improved transient control performance, and the less network resource occupancy. A comparative simulation is given to illustrate the effectiveness of the proposed scheme.