No abstract

Autonomous vehicle path tracking accuracy and vehicle stability can hardly be accomplished by one fixed control frame in various conditions due to the changing vehicle dynamics. This paper presents a model predictive control (MPC) path-tracking controller with switched tracking error, which reduces the lateral tracking deviation and maintains vehicle stability for both normal and high-speed conditions. The design begins by comparing the performance of three MPC controllers with different tracking error. The analyzing results indicate that in the steady-state condition the controller with the velocity heading deviation as the tracking error significantly improves the tracking accuracy. Meanwhile, in the transient condition, by substituting the steady-state sideslip for real-time sideslip to compute the velocity heading deviation, the tracking overshoot can be reduced. To combine the strengths of these two methods, an MPC controller with switched tracking error is designed to improve the performance in both steady-state and transient conditions. The regime condition of a vehicle maneuver and the switching instant are determined by a fuzzy-logic-based condition classifier. Both normal and aggressive driving scenarios with the vehicle lateral and longitudinal acceleration combination of 5 m/s 2 and 8 m/s 2 are designed to test the proposed controller through CarSim-Simulink platform. The simulation results show the improved performance of the MPC controller with switched tracking error both in tracking accuracy and vehicle stability in both scenarios.INDEX TERMS Autonomous vehicles, path tracking, predictive control, switched tracking error, condition classifier.

This paper presents a linearization method for the vehicle and tire models under the model predictive control (MPC) scheme, and proposes a linear model-based MPC path-tracking steering controller for autonomous vehicles. The steering controller is designed to minimize lateral path-tracking deviation at high speeds. The vehicle model is linearized by a sequence of supposed steering angles, which are obtained by assuming the vehicle can reach the desired path at the end of the MPC prediction horizon and stay in a steady-state condition. The lateral force of the front tire is directly used as the control input of the model, and the rear tire's lateral force is linearized by an equivalent cornering stiffness. The course-direction deviation, which is the angle between the velocity vector and the path heading, is chosen as a control reference state. The linearization model is validated through the simulation, and the results show high prediction accuracy even in regions of large steering angle. This steering controller is tested through simulations on the CarSim-Simulink platform (R2013b, MathWorks, Natick, MA, USA), showing the improved performance of the present controller at high speeds.

Abstract:The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN) is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS) algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.