Journal Article10.1049/IET-CTA.2016.0032
Model reference composite learning control without persistency of excitation
TL;DR: In this article, a model reference composite learning control strategy was proposed to guarantee parameter convergence without the PE condition, where an integral at a moving-time window is applied to construct a prediction error, an integral transformation is derived for avoiding the time derivation of plant states in the calculation of the prediction error and both the tracking error and the prediction errors are applied to update parametric estimates.
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Abstract: Parameter convergence is desirable in adaptive control as it brings several attractive features, including accurate online modelling, exponential tracking, and robust adaptation without parameter drift. However, a strong persistent-excitation (PE) condition must be satisfied to guarantee parameter convergence in the conventional adaptive control. This study proposes a model reference composite learning control strategy to guarantee parameter convergence without the PE condition. In the composite learning, an integral at a moving-time window is applied to construct a prediction error, an integral transformation is derived for avoiding the time derivation of plant states in the calculation of the prediction error, and both the tracking error and the prediction error are applied to update parametric estimates. Global exponential stability of the closed-loop system is established under an interval-excitation condition which is much weaker than the PE condition. Compared with a concurrent learning technique that has the same aim as this study, the proposed composite learning technique avoids the usage of singular value maximisation and fixed-point smoothing resulting in a considerable reduction of computational cost. Numerical results have verified effectiveness and superiority of the proposed control strategy.
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Citations
Composite learning robot control with guaranteed parameter convergence
Yongping Pan,Haoyong Yu +1 more
TL;DR: This paper provides the first result of parameter convergence without the PE condition for adaptive control of a general class of robotic systems by developing a composite learning robot control (CLRC) strategy to achieve fast and accurate parameter estimation under a condition termed interval excitation (IE) which is much weaker than thePE condition.
186
Robust feedback linearization for nonlinear processes control
TL;DR: A theorem based on Lyapunov theory is proposed to prove that if a linearized controlled process is stable, then nonlinear process states are uniformly stable.
147
Integral concurrent learning: Adaptive control with parameter convergence using finite excitation
TL;DR: In this article, a novel integral concurrent learning method is developed that removes the need to estimate state derivatives while maintaining parameter convergence properties, and a Monte Carlo simulation illustrates improved robustness to noise compared to the traditional derivative formulation.
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Integral Concurrent Learning: Adaptive Control with Parameter Convergence without PE or State Derivatives.
TL;DR: A novel integral concurrent learning method is developed in this paper that removes the need to estimate state derivatives while maintaining parameter convergence properties.
119
Composite learning from adaptive backstepping neural network control.
TL;DR: This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition.
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