A Zonotope-based Big Data-driven Predictive Control Approach for Nonlinear Processes

A zonotope-based big data-driven predictive control (BDPC) approach is developed to partition the nonlinear process behaviour (represented by an input-output trajectory set) into multiple linear sub-behaviours using a two-step hierarchical clustering: Euclidean distance-based clustering and linear subspace distance-based clustering. By approximating every linear sub-behaviour as a zonotope, a data-driven interpolation is developed based on the convex combination of zonotopes. During online control, a BDPC controller is designed by determining an interpolated zonotope where its centre trajectory is closest to the online trajectory and computing the control action subject to an optimisation problem. The proposed BDPC approach is illustrated using a case study on controlling an aluminium smelting process.