Momentum-Based Adaptive Laws for Identification and Control

Abstract

In this paper, we develop momentum-based adaptive update laws for parameter identification and control to improve parameter estimation error convergence and control system performance for uncertain dynamical systems. Specifically, we introduce three novel continuous-time, momentum-based adaptive estimation and control algorithms and evaluate their effectiveness via several numerical examples. Our proposed adaptive architectures show faster parameter convergence rates as compared to the classical gradient descent and model reference adaptive control methods.

Keywords

• parameter identification
• online learning
• MRAC
• momentum-based learning
• high-order tuners
• exponential convergence