A game theory approach to distributed multi-rate model predictive control

In this paper, we study the effect of tuning factors on our formerly proposed method [1] for control of distributed multi-rate systems with linear input coupled dynamics. These systems are multi-rate in the sense that neither output measurements nor input updates are available at certain sampling times. Such systems can arise when the number of sensors is less than the number of variables to be controlled. Or when measurements of outputs cannot be completed simultaneously because of application limitations. The multi-rate nature gives rise to lack of information which will cause uncertainty in the system’s performance. A distributed model predictive control (MPC) approach based on Nash game theory was proposed earlier in [1] to control distributed multi-rate systems in which multiple control agents each determined actions for their own parts of the system. Via communication, the agents can take one another’s actions into account. To compensate for the information loss due to the multi-rate nature of the system in this study, a distributed Kalman Filter is proposed to provide optimal estimation of the missing information. The effect of changing control and prediction horizons as control tuning parameters on performance is studied. Using simulation studies on a distillation column the effect of tuning parameters on the proposed controller is illustrated.