Eunhyek Joa

This paper describes an integrated chassis control (ICC) algorithm of differential braking, front/rear traction torque, and active roll moment control. The integrated control algorithm is designed to maximize driving velocity and enhance vehicle lateral stability in cornering. The target longitudinal acceleration is determined based on the driver's intention and vehicle current status to ensure vehicle lateral stability in high-speed maneuvering. An optimization-based control allocation strategy

This paper presents a tyre slip-based integrated chassis control of front/rear traction distribution and four-wheel braking for enhanced performance from moderate driving to limit handling. The proposed algorithm adopted hierarchical structure: supervisor – desired motion tracking controller – optimisation-based control allocation. In the supervisor, by considering transient cornering characteristics, desired vehicle motion is calculated. In the desired motion tracking controller, in order to tr

This paper presents a lateral driver model for vehicle–driver closed-loop simulation at the limits of handling. An appropriate driver model can be used to evaluate the performance of vehicle chassis control systems via computer simulations before vehicle tests which incurs expenses especially at the limits of handling. The driver model consists of two parts. The first part is an upper-level controller employing force-based approach to reduce the number of unknown vehicle parameters. The feedforw

This paper presents a novel energy-efficient motion planning algorithm for Connected Autonomous Vehicles (CAVs) on urban roads. The approach utilizes two components: a decision-making algorithm and an optimization-based trajectory planner. The decision-making algorithm leverages Signal Phase and Timing (SPaT) information from connected traffic lights to select a lane with the aim of reducing energy consumption. The algorithm is based on a heuristic rule which is learned from human driving data.

We present an output feedback stochastic model predictive controller (SMPC) for constrained linear time-invariant systems. The system is perturbed by additive Gaussian disturbances on state and additive Gaussian measurement noise on output. A Kalman filter is used for state estimation and an SMPC is designed to satisfy chance constraints on states and hard constraints on actuator inputs. The proposed SMPC constructs bounded sets for the state evolution and uses a tube-based constraint tightening

<div class="section abstract"><div class="htmlview paragraph">This paper presents the integrated chassis control(ICC) of four-wheel drive(4WD), electronic stability control(ESC), electronic control suspension(ECS), and active roll stabilizer(ARS) for limit handling. The ICC consists of three layers: 1) a supervisor determines target vehicle states; 2) upper level controller calculates generalized forces; 3) lower level controller, which is contributed in this paper, optimally allocat

The challenge lies in developing fully autonomous vehicles is to drive safely in inclement weather. Driving in inclement weather is often a risky task because reacting proactively and stabilizing the vehicle on low friction road is a challenging task unlike driving on high friction road. To tackle such issue, this paper presents a predictive motion framework to operate safely on low friction road without prior knowledge of tire-road friction coefficient. The proposed control algorithm consists o

This paper addresses the eco-driving problem for connected vehicles on urban roads, considering localization uncertainty. Eco-driving is defined as longitudinal speed planning and control on roads with the presence of a sequence of traffic lights. We solve the problem by using a data-driven model predictive control (MPC) strategy. This approach involves learning a cost-to-go function and constraints from state-input data. The cost-to-go function represents the remaining energy-to-spend from the

<div class="section abstract"><div class="htmlview paragraph">This paper presents an integrated chassis control method for vehicle stability under various road friction conditions without information on tire-road friction. For vehicle stability, vehicle with an integrated chassis control needs to cope with the various road friction conditions. One of the chassis control method under various road conditions is to determine and/or limit control inputs based on tire-road friction coeffi

This paper describes an integrated control for limit handling by integrating fourwheel-drive (4WD), which decides front/rear traction, and electronic stability control (ESC), which serves differential braking of each tire. The main concept of the proposed integrated control algorithm (ICA) is optimally utilizing the friction circle with tire slip information and pre-defined sub-optimal solution to increase overall vehicle speed in cornering. The proposed algorithm consists of the following three

We propose a Model Predictive Control (MPC) with a single-step prediction horizon to approximate the solution of infinite horizon optimal control problems with the expected sum of convex stage costs for constrained linear uncertain systems. The proposed method aims to enhance a given sub-optimal controller, leveraging data to achieve a nearly optimal solution for the infinite horizon problem. The method is built on two techniques. First, we estimate the expected values of the convex costs using