Model Predictive Control for Dual-Motor Driving System With Unknown Backlash Nonlinearity

The unmeasurable gear backlash nonlinearity caused by mechanical wear in multimotor driving systems (MDSs) is inevitable, imposing challenges for the modeling and controller design. As a remedy, this work proposes a model predictive controller (MPC) to handle the unmeasurable backlash often encountered in dual-motor driving systems (DDSs). First, a control-oriented model with an observer is established to estimate the backlash torque. Accordingly, a constrained MPC is developed to minimize both the tracking error and vibrations. Therein, delay compensation is leveraged to decrease prediction lag. PI feedback is introduced to compensate for the steady-state error caused by the offset of the external observer. Based on the output of the proposed MPC, which is the sum of the torques for two motors, a time-varying-bias-torque (TVBT) scheme to save energy consumption is designed to compute the torque for each motor. Finally, experiments are conducted on DDSs to verify the effectiveness and superiority of the present MPC-TVBT.