Sequential and Iterative Distributed Model Predictive Control of Multi-Motor Driving Cutterhead System for TBM

Abstract

In this paper, we investigate the robust control of multi-motor driving cutterhead system for tunnel boring machines (TBMs) with distributed model predictive control (MPC). By analyzing the working procedures of the induction motor, coupling, reduction gear box, and spur gear, a dynamical model of cutterhead system is established. The model is then represented into a state-space model with additional loads. A sequential and iterative distributed MPC algorithm is established for optimizing the input torques for the driving motors. To design the distributed MPC, the whole system is decomposed into several subsystems according to the distribution of input torques. The future system outputs are predicted by using the system model and the distributed MPC optimizes the input sequence at each time instant in a recursive fashion. The model errors are corrected by comparing the actual outputs and predicted outputs. A sequential and iterative algorithm is proposed to coordinate the distributed MPC controllers. Finally, simulations are carried out to validate the distributed MPC algorithm on torque optimization of cutterhead system in TBMs.