L 1 adaptive output‐feedback control of multivariable nonlinear systems subject to constraints using online optimization

In this paper, an L 1 adaptive output‐feedback controller is developed for multivariable nonlinear systems subject to constraints using online optimization. In the L 1 adaptive architecture, an adaptive law will update the adaptive parameters that represent the nonlinear uncertainties such that the...

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Published inInternational journal of robust and nonlinear control Vol. 29; no. 12; pp. 4116 - 4134
Main Authors Ma, Tong, Cao, Chengyu
Format Journal Article
LanguageEnglish
Published 01.08.2019
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Abstract In this paper, an L 1 adaptive output‐feedback controller is developed for multivariable nonlinear systems subject to constraints using online optimization. In the L 1 adaptive architecture, an adaptive law will update the adaptive parameters that represent the nonlinear uncertainties such that the estimation error between the predicted state and the real state is driven to zero at every integration time step. Of course, neglection of the unknowns for solving the error dynamic equations will introduce an estimation error in the adaptive parameters. The magnitude of this error can be lessened by choosing a proper sampling time step. A control law is designed to compensate the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. Model predictive control is introduced to solve a receding horizon optimization problem with various constraints maintained. Numerical examples are given to illustrate the design procedures, and the simulation results demonstrate the availability and feasibility of the developed framework.
AbstractList In this paper, an L 1 adaptive output‐feedback controller is developed for multivariable nonlinear systems subject to constraints using online optimization. In the L 1 adaptive architecture, an adaptive law will update the adaptive parameters that represent the nonlinear uncertainties such that the estimation error between the predicted state and the real state is driven to zero at every integration time step. Of course, neglection of the unknowns for solving the error dynamic equations will introduce an estimation error in the adaptive parameters. The magnitude of this error can be lessened by choosing a proper sampling time step. A control law is designed to compensate the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. Model predictive control is introduced to solve a receding horizon optimization problem with various constraints maintained. Numerical examples are given to illustrate the design procedures, and the simulation results demonstrate the availability and feasibility of the developed framework.
Author Ma, Tong
Cao, Chengyu
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