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Concurrent Learning Adaptive Control With Directional Forgetting
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DOI：10.1109/TAC.2019.2911863.png)
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Abstract
En 中文
This paper proposes a new concurrent learning-based adaptive control algorithm. The main objective behind our proposition is to relax the persistent excitation requirement for the stability guarantee, while providing the ability to identify time-varying parameters. To achieve the objective, this paper designs a directional forgetting algorithm, which is then integrated with the adaptive law. The theoretical stability analysis shows that the tracking and parameter estimation error is exponentially stable with the signal only finitely excited, not persistently excited. The analysis also shows that the proposed algorithm can guarantee the stability under time-varying parameters. Moreover, the necessary and sufficient conditions for the stability given the time-varying parameters are derived. The results of numerical simulations confirm the validity of the theoretical analysis results and demonstrate the performance of the proposed algorithm.
Keywords:
Parameter estimation
Adaptive control
Stability analysis
Uncertainty
Convergence
Numerical stability
Adaptation models
Concurrent learning (CL)
directional forgetting (DF)
model reference adaptive control (MRAC)
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