Abstract

The control of car-like robots is crucial for various applications, including autonomous vehicles and industrial automation. Achieving precise and robust movement in these robots, especially under varying load conditions, necessitates advanced control strategies. Traditional proportional-integral-derivative (PID) tuning methods, such as Ziegler-Nichols and Cohen-Coon, often fall short in addressing the dynamic challenges posed by changing loads. Although adaptive and intelligent methods have been explored, they can be computationally intensive and complex to implement. There is a clear need for a more efficient and adaptive PID tuning approach that maintains simplicity while offering robustness against varying loads. This research aims to develop a gain-scheduled PID tuning method specifically designed for car-like robots, enabling them to adapt to varying load conditions during point-to-point movements. The study focuses on the kinematic model of car-like robots, operating in simulated environments with dynamically changing loads. The gain-scheduled PID controller is designed using a combination of analytical and adaptive techniques. The analytical technique aims to meet step response performance criteria, including no overshoot, a rise time of less than 1 s, and a settling time of under 1.5 s. In contrast, the adaptive technique focuses on updating the gains during point-to-point movements to accommodate load variations, ensuring optimal performance throughout the robot's operation. The results are validated across multiple load-carrying scenarios. The performance of the proposed method is benchmarked against basic PID tuning method. In three trials of random load-carrying across 10-point destinations, the basic tuning method resulted in an average completion time of 27.86 s, while the gain-scheduled tuning method achieved an average of 11.27 s. This demonstrates that the gain-scheduled approach offers superior adaptability and robustness, along with reduced computational complexity. The study successfully achieves the objective of developing a robust and efficient PID tuning method for car-like robots.

Keywords

Gain-scheduled, Car-like robot, Proportional integral derivative tuning, Pid tuning, Control system.

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