Optimized, Energy Efficient Path Planning for an Autonomous EV Under Dynamic Constraints

Abstract

With the advent of robotics, a major focus has shifted to the au¬tonomous movement of cyber physical systems, e.g., B. UAV, USV, UGV, etc. While focusing on your autonomous movement, the underlying limi¬tations are usually not explored in detail. We tried to develop a control strategy that optimizes the route driven while minimizing dynamic con-straints such as battery, oil consumption, engine consumption, etc. Such work has the advantage of being applicable in real-time systems and not just academic activities for research purposes. This will translate our prob-lem into a multi-objective problem where the optimization of the distance travelled is achieved together with the parameters mentioned above. First, the non-linear control-oriented model is built, which includes the track The single-track vehicle model, as well as the magic formula tyre model, are all examples of models. The path following issue uses the model predictive control (MPC) technique to readily address stability restrictions. The ve¬hicle side slip angle, yaw rate, steering angle. lateral position error, and Layups stability constrain the MPC control problem here. The generalised minimal remainder/continuation algorithm (C/GMRES) is used to reduce on-line computing load. Inequality restrictions are handled with the use of dead zone penalty functions, which keep the solution smooth. This article also employs a numerical approach with a variable prediction period to obtain a decent initial answer rapidly. Finally, simulated validations reveal that, as compared to controllers MPC employing an active set method or an inner point algorithm, the proposed strategy may accomplish the nec¬essary path following and vehicle stability effectiveness while considerably reducing the computational load.