非周期时变非线性系统自适应迭代学习控制

摘要

对一类含有非周期时变不确定性的非线性系统的控制问题进行了研究。该系统具有严格反馈形式且控制增益未知。在控制器设计中,用一种具有迭代特性的神经网络消除了非周期时变不确定性的影响,并综合应用反演技术和鲁棒自适应控制技术消除了严格反馈结构和未知控制增益带来的设计问题。稳定性分析结果表明：系统所有状态量有界且输出量在积分意义下收敛到期望轨迹。仿真试验证明了所设计控制器的有效性。%The control issue of strict-feedback nonlinear systems with non-periodical time-varying uncertainties and unknown control gain function is investigated.An adaptive iterative learning controller is developed.In the controller design,an iterative neural network is proposed to eliminate the influence of non-periodical time-varying uncertainties.The backstepping and robust adaptive control techniques are combined to solve the design difficulties brought by strict-feedback structure and unknown control functions.Stability analysis indicates that all state variables are bounded and the output tracks the desired trajectory in integral sense.Simulation results verify the effectiveness of the proposed scheme.