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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Smooth Neuroadaptive PI Tracking Control of Nonlinear Systems With Unknown and Nonsmooth Actuation Characteristics
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Smooth Neuroadaptive PI Tracking Control of Nonlinear Systems With Unknown and Nonsmooth Actuation Characteristics

机译：具有未知和非平稳驱动特性的非线性系统的平滑神经自适应PI跟踪控制

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摘要

This paper considers the tracking control problem for a class of multi-input multi-output nonlinear systems subject to unknown actuation characteristics and external disturbances. Neuroadaptive proportional–integral (PI) control with self-tuning gains is proposed, which is structurally simple and computationally inexpensive. Different from traditional PI control, the proposed one is able to online adjust its PI gains using stability-guaranteed analytic algorithms without involving manual tuning or trial and error process. It is shown that the proposed neuroadaptive PI control is continuous and smooth everywhere and ensures the uniformly ultimately boundedness of all the signals of the closed-loop system. Furthermore, the crucial compact set precondition for a neural network (NN) to function properly is guaranteed with the barrier Lyapunov function, allowing the NN unit to play its learning/approximating role during the entire system operation. The salient feature also lies in its low complexity in computation and effectiveness in dealing with modeling uncertainties and nonlinearities. Both square and nonsquare nonlinear systems are addressed. The benefits and the feasibility of the developed control are also confirmed by simulations.
机译：本文考虑一类多输入多输出非线性系统在未知致动特性和外部干扰的情况下的跟踪控制问题。提出了具有自整定增益的神经自适应比例积分（PI）控制，该控制结构简单且计算便宜。与传统的PI控制不同，拟议的PI控制能够使用稳定性保证的分析算法在线调整其PI增益，而无需手动调整或反复试验。结果表明，所提出的神经自适应PI控制在任何地方都是连续且平滑的，并确保了闭环系统所有信号的最终一致有界。此外，使用障碍Lyapunov函数可以保证神经网络（NN）正常运行的关键紧要前提，这使NN单元在整个系统运行中发挥其学习/逼近的作用。该显着特征还在于其计算的低复杂性以及处理模型不确定性和非线性的有效性。平方和非平方非线性系统都可以解决。仿真也证实了开发控制系统的好处和可行性。

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