Stochastic Model Reference Predictive Temperature Control With Integral Action For An Industrial Oil-cooling Process

摘要

This paper presents a stochastic model reference predictive control (SMRPC) approach to achieving accurate temperature control for an industrial oil-cooling process, which is experimentally modeled as a simple first-order system model with given long time delay. Based on this model, the stochastic model reference predictive controller with control weighting and integral action is derived based on the minimization of an expected generalized predictive control (GPC) performance criteria. A real-time adaptive SMRPC algorithm is proposed and then implemented into a stand-alone digital signal processor (DSP). Experimental results show that the proposed control method is capable of giving accurate and satisfactory control performance under set-point changes, fixed load and load changes.