Parallelizable Real-Time Algorithm for Integrated Experiment Design MPC

This paper proposes a parallelizable real-time algorithm for integrated experiment-design model predictive control (MPC). Integrated experiment design MPC is needed if a system is not observable at a tracking reference and needs to be excited on purpose in order to be able to estimate the system’s states and parameters. The contribution of this paper is a real-time MPC algorithm using two processors. On the first processor an extended Kalman filter (EKF) as well as a parametric certainty-equivalent MPC controller are implemented, which can provide immediate feedback at high sampling rates. On the second processor, optimal experiment design (OED) problems are solved in parallel in order to perturb the certainty-equivalent MPC control loop improving the accuracy of the state estimator at a lower sampling rate. We show that this framework can achieve optimal tradeoffs between OED and control objectives. The approach is applied to a biochemical process in order to illustrate that the proposed controller can achieve superior control performance when compared to certainty-equivalent MPC.