Towards Real-Time Networked Embedded Generalized Predictive Control for Automotive Active Suspension Systems

Abstract

This paper emphasizes the implementation challenge of real-time embedded generalized predictive control (GPC) when applied to a class of automotive active suspension systems. A real-time experimental environment is specially designed in order to verify parameters tuning in closed-loop with the virtual process environment. Real-time experimentation carried on this environment shows the necessity of high prediction and control horizons setting to achieve acceptable comfort performance. A control algorithm is decomposed into parallel real-time computations using a set of systolic array based processors. A systemC model is constructed to verify the proposed implementation. Simulation and profiling results show the feasibility and the efficiency of GPC in future hardware implementation.