一类非线性参数化系统的自适应学习控制

针对控制增益是未知时变的并含有混合未知参数的非线性参数化系统,利用将整个区间分段与反馈线性化相结合,提出了一种新的自适应学习控制方法。该方法可以处理参数在一个未知紧集内周期性快时变的非线性系统。通过引进新颖的微分-差分混合型参数自适应律,使广义跟踪误差在误差平方范数意义下渐近收敛于零。通过构造Lyapunov泛函,给出了广义跟踪误差收敛的充分条件。实例仿真结果说明了该方法的可行性和有效性。

Adaptive learning control for a class of nonlinearly parameterized systems

A novel adaptive learning control method is proposed for a class of nonlinearly parameterized systems with unknown time-varying control gain and mixed parameters by combining a pointwise manner over one entire period and the feedback linearization approach.It can be applied to the time-varying parametric uncertainty system with unknown compact set,rapid time-varying,periodic and where the prior knowledge is the periodicity only.A differential-difference mixed-type adaptive law and an adaptive learning control method are constructed to ensure the asymptotic convergence of the extended tracking error in the sense of square error norm.Also,a sufficient condition of the convergence of the method is given.A simulation example illustrates the feasibility and effectiveness of the proposed method.