Robust model predictive control framework for multi-motor driving cutterhead system with model errors in hard rock TBMs

Tunnel boring machine (TBM) is a safe and effective equipment for excavating tunnels. The advance control of the driving cutterhead system plays an important role in hard rock TBM excavation. This work presents a robust model predictive control (MPC) for optimizing the torques of driving motors. First, a model of TBM cutterhead system is established with state-space representation subject to constraints and additional disturbances. Based on real operating data, the parameters of cutterhead system are identified by using the prediction error method. To address the robustness issue, an output disturbance model is constructed. The system states are estimated for the state feedback control design by using Kalman filter. Based on the estimated state, a robust MPC is designed by presenting a compensation strategy and the constraints are considered together. Extensive simulations based on the identified system are given to test the performance under tracking control and disturbance rejection of cutterhead system.