Predictive control of an autonomous ground vehicle using an iterative linearization approach

Abstract

This paper presents the design of a controller for an autonomous ground vehicle. The goal is to track the lane centerline while avoiding collisions with obstacles. A nonlinear model predictive control (MPC) framework is used where the control inputs are the front steering angle and the braking torques at the four wheels. The focus of this work is on the development of a tailored algorithm for solving the nonlinear MPC problem. Hardware-in-the-loop simulations with the proposed algorithm show a reduction in the computational time as compared to general purpose nonlinear solvers. Experimental tests on a passenger vehicle at high speeds on low friction road surfaces show the effectiveness of the proposed algorithm.

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

“…The challenge of these approaches is in deciding the adequate assumptions and abstractions for the domain-specific knowledge. In predictive control [18][19][20], for example, the controller decides the value of the control signal based on the predicted, rather than the current, values of parameters pertinent to the state of the car and its environment. The prediction could be seen as approximating the values of these parameters from their current values, their rate of change, and the prediction time that corresponds to the latencies in the control loop.…”

Design and Implementation of Autonomous Surface Vehicle

“…Model Predictive Control algorithm has many computational complexities which can't be used in Real time. Experiment results failed when it was conducted in real conditions in the outside world [5]. …”

Hierarchical reasoning game theory based approach for evaluation and testing of autonomous vehicle control systems

“…The weighting terms w i may change depending on the aggressiveness of the driver, but intuitively, collision avoidance should be the most important factor. Thus, the following relationship between the weights should be kept: w 1 w 2 , w 3 , w 4 .…”

“…Predictive driver models can be utilized in the higher level outer loop controller generating the reference trajectories for the lower level inner loop controller, thereby ensuring similar behavior to that of a human-driven vehicle and improving the comfort level of the passengers [1]. In addition, these models can provide predictions of the future trajectories of the vehicles in the vicinity of the host autonomous vehicle and be used as inputs for the inner loop controllers such as model predictive controllers (MPC) [2]- [4].…”