This chapter discusses the development of an adaptive path tracking controller equipped with a knowledge-based supervisory algorithm for an autonomous heavy vehicle. The controller was developed based on a geometric/kinematic controller, the Stanley controller. One of the mostly known issues with any geometric/kinematic controller is that a properly tuned controller may not be valid in a different operating region than the one it was being tuned/optimised on. Therefore, this study proposes an adaptive algorithm to automatically choose an optimal controller parameter depending on the manoeuvring and vehicle conditions. An optimal knowledge database is developed for an adaptive algorithm to automatically obtain the parameter values based on the vehicle speed, v, and heading error, ϕ. Several simulations are carried out with different trajectories and speeds to evaluate the effectiveness of the controller against its predecessors, namely, Stanley and the non-adaptive modified Stanley (Mod St) controllers. The simulated steering actions are then compared against human driver’s experimental data along the predefined paths. It was shown that the proposed adaptive algorithm managed to guide the heavy vehicle successfully and adapt to various trajectories with different vehicle speeds while recording lateral error improvement of up to 82% compared to the original Stanley controller.
Part of the book: Automation and Control
To date, research efforts have demonstrated the stimulated need for the Internet of Things (IoT) based monitoring device in their laboratory. The benefits of remote laboratories in overcoming time constraints and the disadvantages of usability of conventional laboratories are well known. In addition to the current control engineering laboratories, a remote lab that incorporates an industry-relevant method has been established to assist in the understanding of data acquisition with cost-effective platform integration. However, one of the greatest challenges is the creation of a low-cost and user-friendly remote laboratory experiment that is ideal for interacting with the actual laboratory via a mobile device. The main objective of this work is therefore to build a remote laboratory system based on the IoT using the LabVIEW-Arduino interface with the example of proportional-integral-derivative (PID) tuning scheme for the LD-Didactic temperature plant. The practical work would include the implementation of the low-cost Arduino module connecting the actual plant to mobile devices. In addition, interfaces have been built using the Blynk application to allow communication between the end user and the laboratory equipment. In line with the Industrial Revolution 4.0 (IR 4.0), the proposed study structure called for the digitization of the current laboratory experiment method.
Part of the book: LabVIEW