Fast gradient‐based distributed optimisation approach for model predictive control and application in four‐tank benchmark

Abstract

By taking both control and state vectors as decision variables, the subproblems of model predictive control scheme can be considered as a class of separable convex optimisation problems with coupling linear constraints. A Lagrangian dual method is introduced to deal with the optimisation problem, in which, the primal problem is solved by a parallel coordinate descent method, and a fast dual ascend method is adopted to solve the dual problem iteratively. The proposed approach is applied to the well-known hierarchical and distributed model predictive control four-tank benchmark. Experimental results have testified the effectiveness of the proposed approach and shown that the benchmark problem can be well stabilised.