International Scientific Conference on Aeronautics, Automotive and Railway Engineering and Technologies (BulTrans - 2023 ), Burgas, Bulgaristan, 10 - 13 Eylül 2023, cilt.3129, sa.1, ss.1-10
Model predictive control is one of the advanced methods to solve
trajectory following problems of autonomous ground vehicles due to its
predictive capability. It can deal with multiple inputs, outputs, and
set-point signals. However, this technique causes a heavy computational
burden to the controller due to its multi-objective optimization
approach, limiting its real-time applications. In this study, an
explicit nonlinear model predictive controller (E-NMPC) is proposed to
control front steering angle and rear wheel tractive torques
simultaneously to provide trajectory tracking of a vehicle in NATO
double lane change (DLC) maneuvers with consideration of reduced
computation time. Artificial neural networks enabling offline learning
processes are used to generate a computationally-efficient control law
without going through extensive optimization methods. Namely, main
contribution of this study is establishment of a neural-network based
E-NMPC technique for the considered control problem that removes the
need of online optimization and replace the classical NMPC that requires
online-optimization with high computational load. The simulated results
revealed that time elapsed during one execution cycle can be
significantly reduced with the proposed method for various cases,
compared to the classical online NMPC method.