Adaptive Emotional-Learning-Based Controller: A Practical Design Approach for Helicopters With Variable Speed Rotors

Abstract

As a nonparametric control strategy with a signal matching based design approach, the emotional-learning-based controller (ELBC) facilitates easy and quick deployment of an adaptive strategy in a real-time environment. However, the existing emotional-learning-based adaptive methods are fraught with multiple feasibility issues due to computational complexities, lack of robustness, and jittery transient behavior, making it undesirable for controlling systems with significant uncertainties. This article attempts to bridge this gap by proposing a closed-loop reference model based adaptive stimulus with a contour-bounded robust adaptation mechanism making it practically suitable for systems with significant parametric uncertainties. The reliability, applicability, and efficacy of the proposed strategy are demonstrated through comprehensive validation experiments on a laboratory scale helicopter with variable speed rotors. Comparisons with a nominal ELBC and a Pareto-optimized linear quadratic Gaussian (LQG) strategy portray its superior performance when significant displacements are induced.