Prescribed Performance Tracking Control Under Uncertain Initial Conditions: A Neuroadaptive Output Feedback Approach
针对非线性非三角结构系统，提出一种结合神经网络和变量分离技术的状态观测器，实现输出反馈有限时间跟踪控制，消除传统方法对初始条件的依赖，并保证预设精度和收敛时间。
This work is concerned with the prescribed performance tracking control for a family of nonlinear nontriangular structure systems under uncertain initial conditions and partial measurable states. By combining neural network and variable separation technique, a state observer with a simple structure is constructed for output-based finite-time tracking control, wherein the issue of algebraic loop arising from a nontriangular structure is circumvented. Meanwhile, by using an error transformation, the developed control scheme is able to ensure tracking with a prescribed accuracy within a pregiven time at a preassigned convergence rate under any bounded initial condition, eliminating the long-standing initial condition dependence issue inherited with conventional prescribed performance control methods, and guaranteeing the predeterminability of convergence time simultaneously. Two simulation examples also demonstrate the effectiveness of the presented control strategy.