Neural Network Predictive Control Approach Design for Adaptive Cruise Control

As one part of the advanced driver assistance systems (ADAS), adaptive cruise control (ACC) is introduced to reduce the possibility of traffic accidents by controlling the throttle and the pressure on the brakes to maintain a safe distance from the vehicle in front. Generally, linearized model-based controllers are used in the ACC. In this paper, a new approach to ACC's inner loop is developed by designing the controller using neural network predictive control (NNPC) which integrates the capability of artificial neural networks (ANN) to imitate vehicle characteristics and model predictive control (MPC) to obtain the minimized quadratic error between future reference trajectories and predicted outputs. Two separate control loops will be used: an outer loop based on a decision algorithm, and the PI controller, which will give the inner loop a speed reference to maintain the safe distance from the vehicle in front. NNPC is used in the inner loop to manipulate throttle and brake pressure on the brakes in order to control the speed of the following vehicle. Simulations will be carried out using software-in-the-loop (SIL) between CarSim and Simulink. The ANN model is identified and verified to mimic the nonlinearity behavior of the vehicle model using the mean square error (MSE) parameter. The results of this study are that the ANN model is able to imitate the vehicle dynamic with MSE equal to 0.0095, and the controller can maintain a safe distance while having a smooth response.