Neural Network-Based Model Predictive Trajectory Tracking Control for Dual-Motor-Driven a Tracked Unmanned Vehicle

Trajectory tracking is a key technology for electrical-driven tracked unmanned vehicles (TUVs), while the control model has a significant impact on tracking performance. To improve trajectory tracking accuracy for a dual-motor-driven TUV, a data-driven model-based predictive control scheme is proposed in this article. First, a vehicle dynamics model based on the Long Short-Term Memory (LSTM) network is developed for a TUV. The vehicle's motion states in a subsequent time step are predicted using a sequence of history states and control inputs, while the multi-body dynamics model in the TUV platform are utilized for training and validation. Then, a neural network-based model predictive control (NN-MPC) strategy is designed, employing the trained LSTM model as the prediction model within a receding horizon framework to compute the optimal motor torques for trajectory tracking. Unlike existing learning-based MPC approaches that mainly focus on wheeled vehicles, this work investigates a neural network-enhanced MPC for tracked unmanned vehicles with coupled longitudinal-lateral dynamics. The simulation results demonstrate that, compared to a physics-model based MPC strategy, the proposed NN-MPC reduces the root mean square (RMS) values of lateral error and heading error by 12.1% and 7.9% in a medium-speed scenario and by 80% and 14.0% in a high-speed scenario. The field experiment further verifies the practical feasibility of the proposed control scheme.