Model predictive control of discrete-time linear systems by ADMM with applications to turbofan engine control problems

In this paper, we consider optimal control problems with linear discrete state space model, which originate from a class of turbofan engines. The optimization problem associated with each moving horizon estimation (MHE) in classical model predictive control (MPC) is a quadratic programming (QP) problem, and the general QP algorithms does not exploit the structural features of the turbofan engine itself to improve the computational efficiency of the algorithm. In the framework of model predictive control, the turbofan engine model makes the rolling optimization subproblem exhibit a sparse structure. Based on this feature, the alternating direction method of multipliers (ADMM) is employed to solve each optimization sub-problem and design an improved MPC-ADMM algorithm for solving this class of optimal control problems. The simulation results are compared with the MPC-QP algorithm by numerical examples to show the effectiveness and superiority of the novel algorithm.