This paper proposes a dual decomposition method for solving distributed model predictive control. This controller is designed for systems subject to communication constraints, in which nonlinear subsystems interconnected via dynamics and by constraints. The interconnections are relaxed by using gradient method, accelerated gradient and alternating direction methods of multipliers. Also, an event-based communication is proposed to handle the issue of communication constraints especially in embedded systems. In the proposed event-based communication strategy, each controller solves the optimization problem and communicate only if the prices are updated significantly, which can reduce the computation load and release the burden of the network while achieving global performance. Finally, the simulations study of the four-tank benchmark is presented to demonstrate the effectiveness of the proposed schemes.
Accelerated gradient methods and dual decomposition in distributed model predictive control
