Taylor Expansion Linearization-Based Partial-Form Model-Free Adaptive Control

Xiaolin Guo, R. Chi, Na Lin, Yang Liu
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Abstract

In this paper, a Taylor expansion linearization-based partial-form model-free adaptive control (TELPF-MFAC) method is proposed, which provides a new way to solve complex nonlinear nonaffine systems. The unknown nonlinear nonaffine system is transformed into a new linear data model (LDM) with a nonlinear residual term. Unknown parameters in LDM are estimated by an adaptive updating mechanism. By utilizing ad-ditional control knowledge in both the control and the parameter updating law, the performance of the proposed method can be improved consequently. Simulation study shows the effectiveness of the proposed TELPF-MFAC.
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基于泰勒展开线性化的部分形式无模型自适应控制
提出了一种基于Taylor展开线性化的部分形式无模型自适应控制(TELPF-MFAC)方法,为求解复杂非线性非仿射系统提供了一种新的方法。将未知的非线性非仿射系统转化为具有非线性残差项的线性数据模型(LDM)。采用自适应更新机制对LDM中的未知参数进行估计。通过在控制律和参数更新律中引入额外的控制知识,可以提高该方法的性能。仿真研究表明了所提出的TELPF-MFAC的有效性。
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