Path Following of Tracked Vehicle via Model Predictive Control with Identified Long Short-Term Memory Dynamic Models

Model predictive control (MPC), with prediction and control horizons under multivariable constraints, is an advanced form of model-based control and is commonly used to implement the path following of autonomous vehicles. Conventionally, MPC requires a kinematic or dynamic model of the vehicle to optimize the controller. When a nonlinear kinematic model is utilized, the model should be linearized prior to use by the MPC. However, the computational cost of model linearization is rather high, and hence implementing the MPC in real-time is extremely difficult. Furthermore, estimating the parameters of a classic dynamic model is difficult. Accordingly, the present study first uses a data-driven system identification (system ID) approach to estimate the dynamic model of the considered vehicle (a tracked unmanned ground vehicle (UGV)) as a state-space linear dynamic model. It is shown that the identified model with two-channel inputs and three-channel outputs achieves a fitting of more than 45% between the predicted and measured position and posture of the vehicle. The S-curve and L-shape path-following performance of the tracked vehicle based on MPC with the identified state-space dynamic model is significantly improved. Furthermore, a nonlinear MPC with a long short-term memory (LSTM) model is utilized to adapt different kinds of working environments such as on sand land or in rainy day. According to different system input and output, a suitable model via the LSTM network is estimated in real time and utilized in the nonlinear MPC to enforce the tracked vehicle to follow the path accurately.