Accelerated Performance and Accelerated Learning with Discrete-Time High-Order Tuners

Cui, Yingnan; Annaswamy, Anuradha M.

doi:10.48550/arxiv.2203.16438

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…To the authors' knowledge, ours is the first paper to apply a high-order tuner to adaptive control in discrete time. Our paper complements [19], which establishes parameter learning for identification problems in discrete-time dynamical systems with persistent excitation.…”

Section: Introductionmentioning

confidence: 91%

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Discrete-Time Adaptive Control of a Class of Nonlinear Systems Using High-Order Tuners

Fisher¹,

Annaswamy²

2022

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This paper concerns the adaptive control of a class of discrete-time nonlinear systems with all states accessible. Recently, a high-order tuner algorithm was developed for the minimization of convex loss functions with time-varying regressors in the context of an identification problem. Based on Nesterov's algorithm, the high-order tuner was shown to guarantee bounded parameter estimation when regressors vary with time, and to lead to accelerated convergence of the tracking error when regressors are constant. In this paper, we apply the high-order tuner to the adaptive control of a particular class of discrete-time nonlinear dynamical systems. First, we show that for plants of this class, the underlying dynamical error model can be causally converted to an algebraic error model. Second, we show that using this algebraic error model, the high-order tuner can be applied to provably stabilize the class of dynamical systems around a reference trajectory.

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Section: Introductionmentioning

confidence: 91%

“…In the remainder of this section, we propose a new general algorithm for this adaptive control problem, based on results in [13] for system identification. In Section IV, we then propose the addition of the high-order tuner developed in [8], [19] as a particular adaptive law.…”

Section: Problem Settingmentioning

confidence: 99%