A weighted local steady‐state determination approach based on the globally optimal economic steady‐states

Decomposition-Coordination of Double-Layer MPC for Constrained Systems

Large-scale industrial processes usually adopt centralized control and optimization methods. However, with the growth of the scale of industrial processes leading to increasing computational complexity, the online optimization capability of the double-layer model predictive control algorithm is challenged, exacerbating the difficulty of the widespread implementation of this algorithm in the industry. This paper proposes a distributed double-layer model predictive control algorithm based on dual decomposition for multivariate constrained systems to reduce the computational complexity of process control. Firstly, to solve the problem that the original dual decomposition method does not apply to constrained systems, two improved dual decomposition model prediction control methods are proposed: the dual decomposition method based on the quadratic programming in the subsystem and the dual decomposition method based on constraint zones, respectively. It is proved that the latter will certainly converge to the constraint boundaries with appropriate convergence factors for the controlled variables. The online optimization ability of the proposed two methods is compared in discussion and simulation, concluding that the dual decomposition method based on the constraint zones exhibits superior online optimization ability. Further, a distributed double-layer model predictive control algorithm with dual decomposition based on constraint zones is proposed. Different from the objective function of the original dual decomposition model predictive control, the proposed algorithm's dynamic control-layer objective function simultaneously tracks the steady-state optimization values of the controlled and manipulated variables, giving the optimal solution formulation of the optimization problem consisting of this objective function and the constraints. The algorithm proposed in this paper achieves the control goals while significantly reducing the computational complexity and has research significance for promoting the industrial implementation of double-layer model predictive control.

Steady-state sequence optimization with incremental input constraints in two-layer model predictive control

Steady-state optimization is of vital importance in two-layer model predictive control for bringing better steady-state and dynamic performance. However, the global optimality of steady-state sequences provided by local steady-state optimization cannot be guaranteed. Therefore, a new steady-state sequence optimization approach is proposed in the paper, to improve the global optimality of steady-state sequences. First, the non-global optimality of local steady-state sequences is discussed using an example. Subsequently, aiming at improving the global optimality, a novel sequence optimization strategy designed for steady-state optimization is proposed. Its basic formulation is given and the lower bound of the introduced parameter is analyzed. Then, the relation and difference between the proposed steady-state sequence optimization and the existing global steady-state optimization and local steady-state optimization are discussed. Finally, the steady-state performance, dynamic performance, and computational burden of the proposed approach are studied. The proposed approach provides engineers a brand-new way to realize steady-state optimization and effectively improves the global optimality of calculated steady-state sequences. Extensive simulations verify the effectiveness and reliability of the proposed method.