**1. Introduction**

This study proposed a new adaptive steering control strategy for trajectory tracking controller of a heavy vehicle. The controller aims to automatically steer the vehicle along the desired trajectory and adapt to various speeds and trajectories

during varied manoeuvrings. A path tracking controller is a controller module that is developed to provide electronic actuation to the vehicle system while navigating the vehicle automatically. An effective controller needs to be developed to ensure a functional steering module while autonomously navigating through various paths. Commonly, there are several types of trajectory tracking controllers as reviewed previously [1, 2]. One of the most common types of controllers is kinematic controllers such as Pure Pursuit and Follow-the-Carrot due to the simplicity and stability it can provide. This type of controllers relies on the kinematic properties of the vehicles such as speed and acceleration, as well as the travelled distance for the controller feedback. Compared to other dynamic controllers that require the kinetic properties of the vehicle such as torques, moments, and forces, geometric and kinematic properties are relatively easier to measure.

In geometric/kinematic controllers, one of the most established controllers is the Stanley controller as published in [3, 4]. The controller was developed by the Stanford University racing team on their autonomous vehicle (Stanley) in winning the DARPA Challenge in 2005 [5]. Compared to other geometric/kinematic controllers, the Stanley controller does not include a lookahead distance in its formulation. This enables the controller to be robust enough without depending on what lies ahead. However, the Stanley controller poses different problems, which is common in most of the geometric/kinematic controllers. It was found that the performance of this controller on any given trajectory depends on how well the parameter-tuning process was. A finely tuned Stanley controller will be effective only on the driving conditions that it was tuned for. However, it needs to be retuned to work with another road course and speed range. Previous studies have been discussing this issue and stated the same conclusion for most of geometric/ kinematic controllers [1, 3, 6].

Therefore, an improvement is proposed to include an adaptive algorithm that will adjust the controller's parameters based on the driving conditions. Adaptive controllers for trajectory tracking controller have been proposed in numerous studies recently [7–10] to improve the adaptability and stability of the controller under varying conditions. These controllers are designed to cater robustness in a specific area, such as for slippery roads [7], unknown slip conditions [9], and unknown skidding conditions [10]. While the adaptiveness of these controllers in the designated area was proven, respectively, it may not be as effective when dealing with multiple types of disturbances other than the ones it was designed for. For example, an adaptive controller designed to cater various skidding conditions may not be able to cater unknown yaw disturbance. Also, most of the studies for an autonomous vehicle are using the linear vehicle model to develop the control structure where most of the nonlinearity in vehicle motions is neglected such as frictions and aerodynamic effects. In addition, some adaptive algorithms consist of algorithms that require high computational capability due to its associated complexity. Therefore, the adaptive controller proposed in this study is aimed to solve these issues by (1) considering a nonlinear vehicle model containing most of the nonlinearity of a vehicle motion in the controller development phase; (2) using adaptive inputs as vehicle speed and heading error, which both are directly dependent on the sharpness of turns and vehicle slips; and (3) using a simple geometric/kinematic controller as the basic controller to be modified.

Overall, the main contribution of this work is on the development of a knowledge-based algorithm using the adaptive mechanism of path tracking controller to accommodate the varying trajectories and vehicle's speed setting.

*Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path… DOI: http://dx.doi.org/10.5772/intechopen.92667*

Two inputs are considered in triggering the adaptive algorithm, which are the heading error, *ϕ*, and vehicle speed, *v*. The range of speed region catered in this study is based on the limitations and the expected operating region of the autonomous heavy vehicle, which is up to 72 km/h. Variations in road course trajectory are observed in terms of the instantaneous difference between vehicle heading and trajectory direction, *ϕ* with a range between 0 and 75 deg. in both directions. This may as well cater sharp turns. The basic controller is modified to increase its sensitivity to disturbance. Then, by optimising the controller parameters for different disturbance input combinations, a knowledge database is developed. With this, a set of optimum parameters can be chosen depending on the instantaneous speed and trajectory experienced by the vehicle. An algorithm has been developed to carry out the selection process. The controller's performance is then evaluated on six different trajectories and four random speed values to evaluate its effectiveness against the basic Stanley controller and the modified controller without an adaptive algorithm. Results show a promising prospect for the proposed controller.

This chapter starts with Introduction section that covers a brief background of the study, followed by the modelling of a nonlinear seven-degree-of-freedom (7DoF) vehicle model used to simulate the vehicle's behaviour. Next, the proposed adaptive controller is explained in Section 3, beginning with the basic controller's and the adaptive algorithm development. The simulation and experiment procedures in evaluating the controllers including the development of road courses used are presented in Section 4, with the findings discussed in Section 5. Conclusions for this work are presented in the final section.
