期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 67, 期 10, 页码 5440-5447出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3126243
关键词
Adaptive control; finite excitation; parameter convergence; time-varying learning rates
资金
- Boeing Strategic University Initiative [88ABW-2019-4571]
- Air Force Research Laboratory, Collaborative Research and Development for Innovative Aerospace Leadership (CRDInAL), Thrust 3-Control Automation and Mechanization [FA 8650-16-C-2642]
This article presents a new parameter estimation algorithm for the adaptive control of time-varying plants. The algorithm utilizes a matrix of time-varying learning rates to ensure fast convergence of parameter estimation error trajectories. It is applicable to problems with unknown time-varying parameters, and guarantees global boundedness of system state and parameter errors.
This article presents a new parameter estimation algorithm for the adaptive control of a class of time-varying plants. The main feature of this algorithm is a matrix of time-varying learning rates, which enables parameter estimation error trajectories to tend exponentially fast toward a compact set whenever excitation conditions are satisfied. This algorithm is employed in a large class of problems where unknown parameters are present and are time-varying. It is shown that this algorithm guarantees global boundedness of the state and parameter errors of the system, and avoids an often used filtering approach for constructing key regressor signals. In addition, intervals of time over which these errors tend exponentially fast toward a compact set are provided, both in the presence of finite and persistent excitation. A projection operator is used to ensure the boundedness of the learning rate matrix, as compared to a time-varying forgetting factor. Numerical simulations are provided to complement the theoretical analysis.
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