Chapter 8 - Robust adaptive parameter estimation and control for vehicle active suspension systems

Abstract

Adaptive control has been widely studied for vehicle active suspension systems due to its online learning ability for coping with the unknown system nonlinear dynamics. However, the conventional adaptive laws cannot guarantee the convergence of estimation parameter in general, such that the system performance may be degraded or even instability may be triggered. In this chapter, we propose a novel robust adaptive parameter estimation algorithm for adaptive control of nonlinear vehicle active suspension systems. The basic idea of this proposed method is to develop a novel leakage term by introducing a set of auxiliary variables, and then a new adaptive law with parameter estimation error information can be derived from online estimate the essential vehicle parameters (e.g., mass of vehicle body and moment of inertia for pitch motion). With this proposed parameter estimation algorithm, both the suspension control error and parameter estimation error can retain exponential convergence simultaneously. Moreover, theoretical studies of the suggested adaptive law concerning the robustness and convergence are investigated in comparison with several conventional adaptive laws (e.g., gradient descent and σ-modification method). Finally, extensive comparative simulation results are provided to demonstrate the efficiency of the proposed control scheme and the improved parameter estimation performance with the new adaptive law.