Time-stamped model predictive control: an algorithm for control of processes with random delays

Abstract

This paper introduces an enhancement to the model predictive control (MPC) algorithm to address to address variable time delays that may occur in the control loop. These variable delays can arise from various sources such as measurement delays, human-in-the-loop, and communication delays. The specific focus of this paper is to investigate the effect of random communication delays on network-based process control systems. The process industry is moving towards sensor and control systems that are accessed over networks rather than hardwired. For fast responding processes, the communication delay introduced by the networks can lead to performance degradation and even instability. Our experimental characterization of network communication delays revealed that they were mostly white, had a baseline minimum and approached wide-tailed distributions. We propose the time-stamped model predictive control (TSMPC) algorithm, an extension to MPC that uses a communication delay model, along with time-stamping and buffering to improve reliability over networked control systems. Experimental validation of this new algorithm resulted in improved performance over traditional MPC. Where time-stamping is not possible, accounting just for the mean/median communication delay can still result in better performance, and this simplification to TSMPC is termed as the mean/median delay model predictive control (MDMPC). Future enhancements that are possible include addressing non-linearity, input constraints, disturbance rejection, and model mismatch within the algorithm as well as to characterize and tune for delays from other control loop elements.