An autonomous vehicle controller based on BP-PID (Back-Propagation - Proportion Integration Differentiation) is designed. Autonomous control is one of the most significant parts for self-driving. Traditional PID control is supposed to take conducive effects on the longitudinal control; nevertheless, it fails to ensure the lateral control stability due to the inflexible parameters setting. To cover the limitation of in-system parameters adjustment issue, the BP (Back-Propagation) neural network is adhibited in traditional PID lateral control. The BP-PID control module updates the incremental PID parameters through self-learning and makes the vehicle operates more smoothly. The learning algorithm flowchart and calculation method of parameters are provided. Moreover, dual mode (manual mode and autonomous mode) control will continue for a comparatively long period. Consequently, the dual mode switch algorithm is presented. A typical measurement is conducted which were then compared with the ordinary PID control results that verified the potential of the proposed method.

This paper proposes a compensation technique for the global navigation satellite system (GNSS)/real-time kinematic (RTK) course angle data using an electronic compass for an unmanned system. Additionally, the proportion, integral, and derivative control based on a back-propagation neural network (BP-PID) is introduced to improve the steering safety and riding comfort. The course angle jitter was determined. Because the GNSS/RTK receiver cannot offer stable heading data under specific conditions, including but not limited to susceptibility to obstacles, complex electromagnetic environment, and fewer satellites. The compensation algorithm is based on the determination of the GNSS course angle variance ratio and the asynchronous characteristic between the GNSS and an electronic compass. The combined data provide accurate and robust navigation information for an outdoor unmanned system. To address the limitation of the in-system parameter adjustment, a back-propagation (BP) neural network is adhibited to a conventional proportion, integral, and derivative (PID) lateral control system. The BP-PID control module updates the incremental PID parameters through self-learning, and results in the smoother operation of the vehicle. The flowchart of the learning algorithm and method of calculating the parameters are presented. A typical measurement was conducted and the obtained results were compared with typical RTK navigation results. Thus, the effectiveness of the proposed compensation method was confirmed.