Explicit model predictive control of gas-liquid separation plant via orthogonal search tree partitioning

Solutions to constrained linear model predictive control (MPC) problems can be pre-computed off-line in an explicit form as a piecewise linear (PWL) state feedback defined on a polyhedral partition of the state space. This admits implementation at high sampling frequencies in real-time systems with high reliability and low software complexity. Recently, algorithms that determine an approximate explicit PWL state feedback solution by imposing an orthogonal search tree structure on the partition, have been developed, and it has been shown that they may offer computational advantages. This paper considers the application of an approximate approach to the design of an explicit model predictive controller for a two-input two-output laboratory gas-liquid separation plant, including experimental evaluation. The approximate explicit MPC controller achieves performance close to that of the conventional MPC, but requires only a fraction of the real-time computational machinery, thus leading to fast and reliable computations.