GPU-Based Parameterized NMPC Scheme for Control of Half Car Vehicle With Semi-Active Suspension System

Abstract

In this letter, we propose a black-box compatible simulation-based approach for solving nonlinear model predictive control (NMPC) problem via a parameterized technique to control the vertical dynamics of a half car vehicle equipped with semi-active (SA) suspension system. The method taps the potential of the graphic processing unit (GPU) to simulate the system parallelly for several combinations of control inputs and the optimal input is elicited which minimizes the objective function and satisfies the constraints. The method was tested in MATLAB/Simulink environment by means of simulations and a comparative study was conducted with ACADO-qpOASES NMPC framework. The simulation results display better performance of the proposed approach in terms of computation time, closed loop objective, and constraint satisfaction when juxtaposed to the ACADO-qpOASES NMPC controller.