Intelligent Feedback Linearization for Active Vehicle Suspension Control

Abstract

Effective control of ride quality and handling performance are challenges for active vehicle suspension systems, particularly for off-road applications. Off-road vehicles experience large suspension displacements, where the nonlinear kinematics and damping characteristics of suspension elements are significant. These nonlinearities tend to degrade the performance of active suspension systems, introducing harshness to the ride quality and reducing off-road mobility. Typical control strategies rely on linear, time-invariant models of the suspension dynamics. While these models are convenient, nominally accurate, and tractable due to the abundance of linear control techniques, they neglect the nonlinearities and time-varying dynamics present in real suspension systems. One approach to improving the effectiveness of active vehicle suspension systems, while preserving the benefits of linear control techniques, is to identify and cancel these nonlinearities using Feedback Linearization. In this paper the authors demonstrate an intelligent parameter estimation approach using structured artificial neural networks that continually “learns” the nonlinear parameter variations of a quarter-car suspension model. This estimation algorithm becomes the foundation for an Intelligent Feedback Linearization (IFL) controller for active vehicle suspensions. Results are presented for computer simulations, real-time experimental tests, and field evaluations using an off-road vehicle (a military HMMWV). Experimental results for a quarter-car test rig demonstrate 60% improvements in ride quality relative to baseline (non-adapting) control algorithms. Field trial results reveal 95% reductions in absorbed power and 65% reductions in peak sprung mass acceleration using this IFL approach versus conventional passive suspensions.