Trajectory Tracking of Autonomous Vehicles Using Data-Driven Modeling and Neural Predictive Controller (NPC)

The technology of autonomous vehicles is advancing rapidly, relying heavily on high-precision simulations and high-performance controllers. This study uses actual driving data to model coupled lateral and longitudinal vehicle dynamics with two neural network (NN) structures: a time-distributed multilayer perceptron (TD-MLP) and a long short-term memory (LSTM). Inputs include accelerator pedal position, brake pressure, steering wheel angle, gear number, and road slope, while outputs are acceleration and yaw rate. In addition, a bidirectional LSTM (Bi-LSTM) predicts vehicle control inputs based on expert driver data. A resolution of the path to be tracked is provided to the NN driver, which generates control commands that are subsequently fed into vehicle NN models. The vehicle’s output states are used to compute the quadratic cost function for trajectory tracking. The minimum cost is identified, and the corresponding control inputs are selected as the optimal inputs for each prediction horizon. A comparative analysis was performed between the neural predictive controller (NPC) algorithm and the traditional nonlinear model predictive controller (NMPC), utilizing the interior point optimizer (IPOPT). Incorporating high-fidelity models within NMPC leads to substantial computational overhead. The NPC method maintains both computational efficiency and is adaptive to road slope disturbances, without the need for a classical adaptive control design, making it suitable for real-time control applications.