Abstract
A novel distributed model predictive control (DMPC) strategy with time-varying terminal set for linear constrained systems is presented in this paper. To decrease the load of computation of DMPC while ensuring the global optimization, the nominal system is introduced by treating the influence of neighboring subsystems as a bounded disturbance. Then, under the distributed control structure, a distributed predictive control optimization problem containing the nominal state and input can be designed for each subsystem. Furthermore, different from most DMPC approaches, a novel approach to design a terminal constraint set that can be updated in every update time based on the predicted state of the system is proposed. Additionally, the analysis of feasibility and the stability of the proposed DMPC algorithm are described under kinds of the system constraints. Finally, experimental simulation is shown to prove validity by the control scheme in this paper.
Highlights
I N recent years, model predictive control (MPC) is one of the advanced control technology in complex industrial fields[1]
Some constraints in the complex industrial system[2]-[4] can be built in optimization problems through this control method, and further processed online depend on the MPC algorithm
To reduce the load of online calculations and the conservative brought by the fixed terminal constraint set in DMPC, a novel distributed model predictive control strategy is studied in this paper
Summary
I N recent years, model predictive control (MPC) is one of the advanced control technology in complex industrial fields[1]. Some constraints in the complex industrial system[2]-[4] can be built in optimization problems through this control method, and further processed online depend on the MPC algorithm. Nikou et al [16] offered a decentralized model predictive control method that can deal with the problem of robust navigation of the system to the working area state when only use local information was created. In the decentralized predictive control mentioned above, the calculation load of the online optimization is reduced. To reduce the load of online calculations and the conservative brought by the fixed terminal constraint set in DMPC, a novel distributed model predictive control strategy is studied in this paper.
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