Finite-Time Adaptive Tracking Control for Output-Constrained Nonlinear Systems: An Improved Command Filter Approach
针对输出受限的非线性系统，提出一种改进的命令滤波方法，实现有限时间自适应神经跟踪控制，并引入无芝诺事件触发条件降低通信负担。
This study explores finite-time adaptive neural tracking control for output-constrained nonlinear systems. An improved command filter was utilized to simplify the controller, and a compensation system ensured that the filter error converged in finite time. To avoid singularities during the controller design process, a novel switch function was employed in the command filter, including a compensation system and virtual controller, which guaranteed the second-order derivability of the virtual controller. Furthermore, to reduce the communication burden, an improved Zeno-free event-triggered condition was introduced. The control strategy ensured that all the closed-loop system variables remained bounded and that the reference trajectory could be well-tracked in finite time. Finally, a simulation example was given to support our control strategy.