Integral Concurrent Learning: Adaptive Control with Parameter Convergence without PE or State Derivatives

Abstract

Concurrent learning is a recently developed adaptive update scheme that can be used to guarantee parameter convergence without requiring persistent excitation. However, this technique requires knowledge of state derivatives, which are usually not directly sensed and therefore must be estimated. A novel integral concurrent learning method is developed in this paper that removes the need to estimate state derivatives while maintaining parameter convergence properties. A Monte Carlo simulation illustrates improved robustness to noise compared to the traditional derivative formulation.

4 pages, 2 figures

References in corpus (2)

• Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing
• Concurrent learning for parameter estimation using dynamic state-derivative estimators

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