Active suspension with model predictive control

Abstract

This paper examines the performance of Model Predictive Control (MPC) scheme for an Active suspension. A vehicle suspension is designed to provide superior ride comfort and road handling characteristics. Unlike passive suspensions, the Active suspension can change the dynamic of suspension in real-time by injecting force into the system. MPC allows the active suspension to provide better and consistent passenger comfort and road handling capabilities for different road profile. Even though long back, the idea of active suspension conceived, the prohibitive cost and complexity restricted its usage. In recent years active suspension is receiving more and more attention with users preferring a high-end car. In an active suspension for the real-time adjustment of the control force, need a design of a controller. In literature, many controllers used such as Proportional Integral Derivative (PID), Linear Quadratic regulators (LQR), Fuzzy logic controller, Artificial Neural Networks (ANN). In this paper, revealed a model predictive control arrangement for Active suspension model. MPC is an optimal control scheme which uses a model of plant for predicting the future output. The control inputs are optimized such that these predicted outputs meet the desired level of performance. Tested the MPC control scheme is using a bench-scale replica of Quarter active suspension model from QUANSER. To better appreciate the capabilities of the MPC Control Scheme, compared the performance of the active suspension with that of an LQR control scheme, and passive suspension.