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针对一类含有非匹配不确定性的块控型多输入多输出非线性系统,提出一种基于反演技术和RBF神经网络的控制系统设计方案.通过引入一种改进型的Lyapunov函数,避免了控制矩阵未知情况下可能出现的奇异问题.在控制系统设计过程中,充分应用鲁棒自适应控制技术,解决了多输入多输出结构不确定性所带来的设计难题,得到了系统所有状态量将全局指数收敛至原点附近一个邻域的结论.最后的仿真结果表明了设计方案的正确性.
Aiming at a class of block control MIMO systems with non-matching uncertainties, a control system design scheme based on inversion technique and RBF neural network is proposed. By introducing an improved Lyapunov function, Control matrix unknown circumstances may arise singular problems.In the control system design process, the full application of robust adaptive control technology to solve the design problems caused by the uncertainty of multi-input multi-output structure, all the state quantities The global index converges to a neighborhood near the origin. The final simulation results show the correctness of the design scheme.