Robust Integral of Neural Network and Error Sign Control of MIMO Nonlinear Systems
Robust Integral of Neural Network and Error Sign Control of MIMO Nonlinear Systems
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DOI：
10.1109/tnnls.2015.2470175
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发表时间：
2015-09
影响因子：
10.4
通讯作者：
Qinmin Yang;S. Jagannathan;Youxian Sun
中科院分区：
文献类型：
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作者：
Qinmin Yang;S. Jagannathan;Youxian Sun
针对一类具有未知动态和有界扰动的多输入多输出连续非线性系统，提出了一种新的状态反馈控制方案。首先，介绍了由神经网络输出鲁棒积分加上跟踪误差反馈的符号乘以自适应增益组成的控制律。控制律中的神经网络以在线方式学习系统动力学，而神经网络残差重构误差和有界干扰由误差符号信号克服。由于在积分中同时包含了神经网络输出和误差符号信号，保证了控制输入的连续性。该控制器结构和神经网络权值更新律与以往的方法相比是新颖的，并且利用Lyapunov分析仍然保证了半全局渐近跟踪性能。此外，还证明了神经网络权值和所有其他信号同时是有界的。所提出的方法还放宽了对某些项的上界的需要，这在以前的设计中通常是必需的。最后，通过仿真验证了理论结果。
This paper presents a novel state-feedback control scheme for the tracking control of a class of multi-input multioutput continuous-time nonlinear systems with unknown dynamics and bounded disturbances. First, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback multiplied with an adaptive gain is introduced. The NN in the control law learns the system dynamics in an online manner, while the NN residual reconstruction errors and the bounded disturbances are overcome by the error sign signal. Since both of the NN output and the error sign signal are included in the integral, the continuity of the control input is ensured. The controller structure and the NN weight update law are novel in contrast with the previous effort, and the semiglobal asymptotic tracking performance is still guaranteed by using the Lyapunov analysis. In addition, the NN weights and all other signals are proved to be bounded simultaneously. The proposed approach also relaxes the need for the upper bounds of certain terms, which are usually required in the previous designs. Finally, the theoretical results are substantiated with simulations.