Adaptive Estimation and Control With Online Data Memory: A Historical Perspective

Abstract

Online data memory is essential for adaptive estimation and control as it can enhance the performance and robustness of adaptive systems compared to adaptive systems without data memory. We provide an overview of four data memory-driven parameter estimation schemes for adaptive systems from a historical perspective, including forgetting-data memory regression extension (MRE), full-data MRE, discrete-data MRE, and interval-data MRE. For clear presentation and better understanding, a general class of nonlinear systems with linear-in-the-parameter uncertainties is applied as a unifying framework to demonstrate the motivation, synthesis, and characteristics of each MRE scheme for parameter estimation in adaptive control. Intensive comparisons of the four MRE schemes are provided to reveal their technical natures, and real-world applications are discussed to show their practicability. It is concluded that all the MRE schemes can achieve exponential parameter convergence under relaxed excitation conditions rather than the classical condition of persistent excitation which is too stringent to satisfy in practice. The distinctive features of intervaldata MRE termed composite learning are highlighted with respect to computational simplicity, estimation accuracy, robustness against perturbations, and widespread real-world applications to robot learning and control. Possible directions for future research in this area are suggested to conclude this survey.