Vincent Winstead

Estimation of road grade and vehicle mass via model predictive control

The paper develops an active on-line estimation scheme for road grade and vehicle mass of an automotive vehicle. The scheme combines an extended Kalman filter (EKF) to generate on-line parameter estimates and a model predictive controller (MPC) to control vehicle speed trajectory so that to enhance parameter identifiability. For the latter purpose a receding horizon optimization of a cost function which penalizes a predicted variance of the parameter estimates as well as the deviation of the vehicle speed from the desired value. Uncertainties in engine torque delivery can be accounted for with this approach and probabilistic constraints on vehicle speed to maintain vehicle operation within traffic limits can be enforced. A simulation example for a typical mid-size vehicle is reported

A receding horizon optimal control approach to active state and parameter estimation in automotive systems

A receding horizon optimal control approach is proposed to control a system in such a way as to best estimate on-line its states and parameters, without significantly degrading tracking performance or violating pointwise-in-time constraints. Several automotive examples are considered to illustrate the potential of the approach. They include: vehicle mass and road grade estimation, engine wall-wetting parameter estimation, engine mapping, and estimation of the position and velocity of a moving vehicle from angle-only (passive) measurements by another vehicle.

Observer control in a tracking problem via model predictive control

The paper describes a model predictive control (MPC) approach for optimally (in a receding horizon sense) controlling the trajectory of a mobile tracker to minimize errors in estimating the state of another, moving object. With the use of the MPC approach, pointwise-in-time state and control constraints can be enforced for the mobile tracker, and its control system can react dynamically to changing operating conditions (such as new obstacles appearing or disappearing). After a discussion of this problem in a general setting, the paper focuses on a case study of the mobile tracker and moving object on a plane. The mobile tracker can measure the distance to the moving object and this measurement is affected by noise. The effect of the noise may increase if the line of sight from the mobile tracker to the moving object deviates from the orientation of the moving tracker or it passes through interference zones and, in addition, the mobile tracker has to stay clear of the obstacles.

Autonomous Lane Changing Using Model Predictive Control

This paper takes a look at one of the problems associated with the concept of autonomous control of vehicles in the current traffic environment, namely the changing of lanes. Given the increase in traffic density on highways and interstate roads over the past few decades, safe navigation of individual vehicles has required increased driver attention and diligence to an increased number of visual information cues. The concept of autonomous vehicles operating without driver intervention in the present traffic system appears daunting. One aspect of traffic maneuvering involves changing lanes to a position between two other vehicles. Although this aspect appears straightforward, it is the lack of accurate knowledge of other vehicle maneuvering which makes the task difficult. Using Model Predictive Control (MPC) techniques, the task is addressed in an optimization problem framework.