论文部分内容阅读
针对含有参数化和非参数化的高阶非线性系统,设计了一种重复学习控制方案.假设未知时变参数和参考信号的共同周期是已知的,通过参数重组技巧,将所有未知时变项合并为一个周期时变向量.将改进Backstepping方法与分段积分机制相结合,构造了微分-差分参数自适应律和重复学习控制律,使跟踪误差在误差平方范数意义下渐近收敛于零.利用Lyapunov泛函,给出了闭环系统收敛的充分条件.仿真结果验证了该方法的有效性.
A repetitive learning control scheme is designed for high-order nonlinear systems with parameterization and non-parametrization. Assuming that the common period of unknown time-varying parameters and reference signals is known, all unknown time-varying Is combined into a periodic time-varying vector.By combining the improved backstepping method with the piecewise integral mechanism, a differential-difference parameter adaptive law and a repetitive learning control law are constructed so that the tracking error converges asymptotically to the square error square The sufficient conditions for the closed-loop system to converge are given by Lyapunov functional.The simulation results show the effectiveness of the proposed method.