A generalized integrated control strategy for vehicle dynamics using an optimal torque vectoring control approach is extended in this paper. The central objective of this approach is to generate optimal additional tire forces and yaw moment over the vehicle through the application of individual wheel torque to keep the vehicle on a target path. This is achieved by minimizing the error between the actual and target forces and moment at the center of gravity ( CG). In this paper, this methodology is extended to a constrained optimal control approach that handles additional real-time constraints, which has several vehicle control applications. An online optimization strategy is used to solve the resulting constrained optimization problem that gives the necessary tire force adjustments at the tire level. Some typical applications are 1) differential braking on all wheels, which is applicable to both electric and conventional cars and 2) hybrid torque vectoring on the front wheels and differential braking on the rear wheels. Both simulations and experimental results show the usefulness of this approach of handling constraints with this optimal torque vectoring control.

An integrated vehicle control framework is presented, which uses torque vectoring across independently driven wheels for control. The approach is general in nature, but is particularly well suited for electric vehicles due to increased control bandwidth. The novel algorithm optimizes wheel torque outputs in real time, constraining against power management, traction control, chassis configuration, actuator limits, and fault-case limitations. The structure is modular, and designed to adapt for differing vehicles with minimal re-tuning. Simulation and experimental results are provided for a modified electric SUV platform, under a range of dynamic maneuvers in 4WD, FWD, and RWD modes.

An autonomous vehicle controller is presented for the purposes of improving vehicle path tracking at high speeds and near tire traction limits. Improvements to the state of the art are made by reducing the number of sensors and feedback variables needed to achieve dynamic control, and by tightly integrating traction control with a steering output controller to correct for modeling errors and actual disturbances. The controller is designed to interface with a path generation module, from which maximum attainable speeds are received and tracked by the controller. Simulation results using CarSim are provided for a vehicle traveling over a technical race course, encompassing many turn variations. The proposed system is shown to outperform a reference PID control system by achieving higher speeds around the race track without losing control.

A novel method is proposed and described, which increases the efficiency of an electric 4WD vehicle by dynamically redistributing torque to shift motor operating points toward more efficient locations. This method operates in parallel with active stability controllers, by considering tire capacity and vehicle dynamics within the energy optimization loop. Simulations are performed for an electric vehicle with four independent AC induction motors, and 5-7% improvements to energy usage are shown in standard EPA drive cycles.

This paper presents algorithmic advances and field trial results for autonomous exploration and proposes a solution to perform simultaneous localization and mapping (SLAM), complete coverage, and object detection without relying on GPS or magnetometer data. We demonstrate an integrated approach to the exploration problem, and we make specific contributions in terms of mapping, planning, and sample detection strategies that run in real‐time on our custom platform. Field tests demonstrate reliable performance for each of these three main components of the system individually, and high‐fidelity simulation based on recorded data playback demonstrates the viability of the complete solution as applied to the 2013 NASA Sample Return Robot Challenge.