Active suspension using preview information and model predictive control

TLDR: Model predictive control (MPC) as discussed by the authors is a design of choice for control of active suspension systems utilizing previewed road information, which generalizes the approaches based on feedback linearization and dynamic inversion from single step control to multiple step control over a receding prediction horizon.

Abstract: The objective of a suspension system is to maximize the passenger ride comfort and vehicle road holding quality. Passive systems present a trade-off between these objectives and the required suspension travel. An appropriate active suspension control overcomes this tradeoff and provides maximum ride comfort and road holding quality within the available suspension travel. In this paper, we show model predictive control (MPC) to be a design of choice for control of active suspension systems utilizing previewed road information. MPC design explicitly incorporates all hard constraints on state, control and output variables. It generalizes the approaches based on feedback linearization and dynamic inversion from single step control to multiple step control over a receding prediction horizon. MPC is shown to provide excellent improvements in the ride and road handling qualities of the vehicle over realistic terrain profiles. MPC works well even in the presence of noise in the previewed information. Implementation of MPC on the UCB active suspension test rig also shows the feasibility of the MPC algorithm in real-time application.