Guidance of an Off-Road Tractor-Trailer System Using Model Predictive Control

Abstract

This thesis presents an effort to improve the path following reliability of a tractor-trailer system by using a non-linear Model Predictive Control (MPC) approach. The proposed method allows an autonomous mobile robot to make informed control decisions based on anticipating changes in the path conditions, rather than reacting to them, which could potentially reduce path following error on turns. Using a non-linear tractor-trailer model, the controller takes the tractor’s measured position and heading, as well as information about the path geometry in front of it, and it determines the optimal steer angle. Then, in the case of an Ackerman-steered vehicle, a secondary algorithm takes the desired steer angle and calculates the amount of voltage to apply to the steering wheel motor to achieve the steer angle. In comparison, a differential- steered, or skid-steered vehicle takes the set point (given as a turn rate in radians per second) and computes the voltages to the traction motors internally. In the MATLAB simulation study, the controller algorithm is capable of guiding a 2- 1/2 meter long trailer around a 5-meter radius turn, when towed by a four wheel drive off-road utility vehicle, with a maximum error of 8.5 centimeters. These results are highly idealized, however. Adding sensor noise and process noise in simulation increases the error, and inherent sensor bias and latency during the live run increases the error substantially. From the experimental results, it is concluded that non-linear MPC has the potential to improve the reliability of the path following of a robot and trailer system. In order to fully reap the benefits of non-linear MPC however, the model has to be accurate, and the computer has to be fast enough to compute predictions from the model in real-time.