Juyong Kang

Driving Control Algorithm for Maneuverability, Lateral Stability, and Rollover Prevention of 4WD Electric Vehicles With Independently Driven Front and Rear Wheels

This paper describes a driving control algorithm for four-wheel-drive (4WD) electric vehicles equipped with two motors at front and rear driving shafts to improve vehicle maneuverability, lateral stability, and rollover prevention. The driving control algorithm consists of the following three parts: 1) a supervisory controller that determines the control mode, the admissible control region, and the desired dynamics, such as the desired speed and yaw rate; 2) an upper level controller that computes the traction force input and the yaw moment input to track the desired dynamics; and 3) a lower level controller that determines actual actuator commands, such as the front/rear driving motor torques and independent brake torques. The supervisory controller computes the admissible control region, namely, the relationship between the vehicle speed and the maximum curvature of the vehicle considering the maximum steering angle, lateral stability, and rollover prevention. In the lower level controller, a wheel slip controller is designed to keep the slip ratio at each wheel below a limit value. In addition, an optimization-based control allocation strategy is used to map the upper level and wheel slip control inputs to actual actuator commands, taking into account the actuator constraints. Numerical simulation studies have been conducted to evaluate the proposed driving control algorithm. It has been shown from simulation studies that vehicle maneuverability, lateral stability, and rollover mitigation performance can be significantly improved by the proposed driving controller.

Design and Testing of a Controller for Autonomous Vehicle Path Tracking Using GPS/INS Sensors

Abstract This paper describes a steering controller integrated with speed controller for autonomous path tracking using GPS and INS sensors. The steering controller for path tracking is developed based on the finite preview optimal control method. The steering control input is computed using the road information within preview distance. The speed controller determines the speed command necessary to maintain a lateral acceleration limit and improve vehicle safety. The vehicle model for simulation study is validated using vehicle test data. Finally, the controller is implemented on a by-wire vehicle, P1, to validate the performance of the steering controller integrated with speed controller.

Skid Steering-Based Control of a Robotic Vehicle with Six in-Wheel Drives

This paper describes a driving control algorithm based on a skid steering for a robotic vehicle with articulated suspension (RVAS). The RVAS is a kind of unmanned ground vehicle based on a skid steering using an independent in-wheel drive at each wheel. The driving control algorithm consists of four parts: a speed controller for following a desired speed, a lateral motion controller that computes a yaw moment input to track a desired yaw rate or a desired trajectory according to the control mode, a longitudinal tyre force distribution algorithm that determines an optimal desired longitudinal tyre force, and a wheel torque controller that determines a wheel torque command at each wheel in order to keep the slip ratio at each wheel below a limit value as well as to track the desired tyre force. Longitudinal and vertical tyre force estimators are required for the optimal tyre force distribution and wheel slip control. A dynamic model of the RVAS for simulation study is developed and validated using the vehicle test data. Simulation and vehicle tests are conducted in order to evaluate the proposed driving controller. It is found from simulation and vehicle test results that the proposed driving controller provides a satisfactory motion control performance according to the control mode.

Driving Control Algorithm for Maneuverability and Lateral Stability for Application to 4WD Series Hybrid Vehicle

Abstract This paper describes a driving control algorithm for improved maneuverability, lateral stability and rollover prevention. The driving control algorithm is developed for 4WD series hybrid vehicle equipped with two motors in front and rear driving shafts. The driving control algorithm consists of three parts: a supervisory controller that determines control mode and desired dynamics, upper level controller that computes a traction force input and a yaw moment input to track the desired dynamics, lower level controller that determines actual actuator commands, front/rear driving motor torques and independent brake torques. In the lower level controller, optimization based-control allocation strategy is used to map the upper level control inputs to the actual actuator commands, taking into account the actuator constraints. Numerical simulation studies are conducted in order to evaluate the proposed driving control algorithm. It is found from simulation study that the proposed driving controller improves vehicle maneuverability, lateral stability as well as prevents vehicle rollover.

Design of a Path Tracking Scheme and Collision Avoidance Controller for Autonomous Vehicles

Abstract This paper presents an obstacle avoidance scheme for autonomous vehicles as an active safety procedure in unknown environments. The obstacle avoidance problem is treated using a nonlinear model predictive framework in which simplified dynamics are used to predict the state of the actual vehicle over the look-ahead horizon. Due to the slight dissimilarity between the simplified model used for trajectory generation and the actual vehicle trajectory, a separate tracking controller is designed to track the generated trajectory. The longitudinal dynamics of the vehicle is controlled using the inverse dynamics of the vehicle power-train model and the lateral controller is designed based on the linear quadratic regulator. In the nonlinear model predictive framework, the threat of local obstacles is augmented into the performance index using a parallax-based method. The simulation results show that the presented model-predictive-control-based trajectory generation and tracking controller, together, give satisfactory performance in terms of obstacle avoidance when applied to the full nonlinear vehicle model.

An Investigation into Integrated Human Driver Model for Closed-loop Simulation of Intelligent Safety System

Abstract This paper presents an Integrated Human Driver Model for Closed-loop simulation of the Intelligent Safety System. A Lateral Human Driver Model is developed to represent steering behavior of human driver using finite preview optimal control method. A Longitudinal Human Driver Model represents human drivers throttle and brake control behavior relative to preceding vehicle motion. The Longitudinal Human Driver Model computes a desired acceleration and generates throttle/brake inputs to maintain vehicle-to-vehicle clearance at a desired level or to control vehicle speed. An integrated driver model has been developed using the Longitudinal and lateral driver models to represent the behavior of a human driver in alternative driving situation, i.e., vehicle following, lane following and emergency braking, etc. Simulation studies are conducted using Carsim model which is validated using vehicle test data. It is shown that Human drivers behaviors can be well represented by the Integrated Human driver model presented in this paper. Finally, demonstration of the Intelligent Safety Systems close-loop simulation with Integrated Human Driver Model would be conducted.