Experimental Regularization Parameter Search for Polynomial Approximation of Nonlinear PAM Controller
Experimental Regularization Parameter Search for Polynomial Approximation of Nonlinear PAM Controller
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DOI：
10.1109/sii55687.2023.10039357
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Y. Takeda;K. Kogiso
中科院分区：
文献类型：
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作者：
Y. Takeda;K. Kogiso
研究了用多项式函数逼近气动人工肌肉执行器的非线性控制器。控制器的逼近用最小绝对收缩和选择算子来描述，这是用于获得稀疏解的最小二乘方法。非线性控制器包含PAM模型，因此模型不确定性会影响控制器的性能。因此，找到一个合理的正则化参数来逼近控制器在数值和实验设置中都是很重要的。数值和实验结果表明，所得到的多项式控制器在保持与原非线性控制器相当的控制性能的同时，减少了运算量。
This study addresses the approximation of the nonlinear controller for a pneumatic artificial muscle actuator using polynomial functions. The controller approximation is described by the least absolute shrinkage and selection operator, which is the least-squares method used to obtain a sparse solution. The nonlinear controller includes the PAM model; thus, model uncertainties affect the performance of the controller. Therefore, it is important to find a reasonable regularization parameter for approximating the controller in both numerical and experimental settings. The numerical and experimental results confirm that the resulting polynomial controller helps reduce the computational cost and maintain the control performance comparable with the original nonlinear controller.