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Recursive Filtering Adaptive Neural Fault-tolerant Control for Uncertain Multivariable Nonlinear Systems
European Journal of Control ( IF 3.4 ) Pub Date : 2020-11-07 , DOI: 10.1016/j.ejcon.2020.10.003
Tong Ma

This paper synthesizes a recursive filtering adaptive neural fault-tolerant controller for uncertain multivariable nonlinear systems. The proposed control scheme adopts an estimation/cancellation strategy to deal with uncertainties and/or disturbances. The nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF)-based neural network incorporated with a piecewise constant adaptive law, where the adaptive law will generate adaptive parameters by solving the error dynamics between the real system and the state predictor with the neglection of unknowns, and recursive least squares (RLS) is applied to distribute the total uncertainty estimates into matched and mismatched components. The cooperation of GRBF learning method and piecewise constant adaptive law relaxes the stringent constraint on the hardware CPU speed and achieves fast adaptation. The filtering control law delivers a satisfactory performance with guaranteed robustness. Two numerical examples are provided to illustrate the effectiveness of the proposed control architecture via comparisons.



中文翻译:

不确定多变量非线性系统的递归滤波自适应神经容错控制

本文针对不确定的多元非线性系统综合了一种递归滤波自适应神经容错控制器。所提出的控制方案采用估计/取消策略来处理不确定性和/或干扰。非线性不确定性是通过基于高斯径向基函数(GRBF)的神经网络与分段常量自适应定律相结合来近似的,其中自适应定律将通过解决实际系统与状态预测器之间的误差动态而忽略自适应来生成自适应参数。未知数,然后应用递归最小二乘(RLS)将总不确定性估计值分布到匹配和不匹配的分量中。GRBF学习方法和分段常数自适应定律的配合,缓解了对硬件CPU速度的严格约束,实现了快速自适应。滤波控制法则可提供令人满意的性能,并保证鲁棒性。提供了两个数字示例,以通过比较说明所提出的控制体系结构的有效性。

更新日期:2020-11-09
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