Smooth Neuroadaptive PI Tracking Control of Nonlinear Systems With Unknown and Nonsmooth Actuation Characteristics
Smooth Neuroadaptive PI Tracking Control of Nonlinear Systems With Unknown and Nonsmooth Actuation Characteristics
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DOI：
10.1109/tnnls.2016.2575078
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发表时间：
2017-09
影响因子：
10.4
通讯作者：
Yongduan Song;Junxia Guo;Xiucai Huang
中科院分区：
文献类型：
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作者：
Yongduan Song;Junxia Guo;Xiucai Huang
研究一类具有未知驱动特性和外部干扰的多输入多输出非线性系统的跟踪控制问题。提出了一种具有自整定增益的神经自适应比例积分（PI）控制方法，该方法结构简单，计算成本低。与传统的PI控制不同，本文提出的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，而无需手动调整或试错过程。结果表明，所提出的神经自适应PI控制是连续的、处处平滑的，并保证了闭环系统所有信号的最终有界性是一致的。此外，屏障Lyapunov函数保证了神经网络（NN）正常运行的关键紧集前提条件，使神经网络单元在整个系统运行过程中发挥其学习/近似作用。该方法的另一个显著特点是计算复杂度低，能有效地处理建模的不确定性和非线性。讨论了平方和非平方非线性系统。通过仿真验证了该控制方法的有效性和可行性。
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.