Improved High-Order Model Free Adaptive Control
Jian Xu, Na Lin, Ronghu Chi
Abstract
In this paper, an improved high-order model free adaptive control (IHOMFAC) method is proposed. Considering more information of the previous time, a high-order model free adaptive control parameter estimation algorithm is designed, which is different from the traditional high-order MFAC. The design of the control algorithm is based on the principle of symmetric similarity, which not only considers more control knowledge of the previous time in the control law, but also uses more information of the previous time in the estimation algorithm, which is conducive to enhancing the control performance. Simulation results declare that IHOMFAC has superior control performance than the traditional high-order MFAC.
Topics & Concepts
Adaptive controlControl (management)Computer scienceOrder (exchange)Similarity (geometry)Control theory (sociology)AlgorithmArtificial intelligenceImage (mathematics)EconomicsFinanceIterative Learning Control SystemsAdaptive Control of Nonlinear SystemsAdvanced Control Systems Optimization