Intelligent trajectory tracking in autonomous plantation robots using PSO-tuned nonlinear fuzzy PID, fuzzy PID, and...

This study presents a comparative analysis of three trajectory optimization controllers, particle swarm optimization (PSO)-tuned Nonlinear Fuzzy tuned proportional-integral-derivative (PID), Fuzzy PID, and Conventional PID, for a planting autonomous robot using a differential drive mobile robot (DDMR). The focus is on optimizing the trajectory tracking performance, minimizing positional and orientation errors, and ensuring stability and accuracy in seed planting applications. The PSO algorithm is used to tune the parameters of the Nonlinear Fuzzy PID controller, thereby enhancing its ability to adapt to the system's dynamic and nonlinear nature. The performance of the three controllers is evaluated using various reference trajectories, including rectangular pulses, squares, straight lines, and circular paths, simulated in MATLAB/Simulink. Key performance metrics, such as tracking error, Absolute integral error (IAE), and integral square error (ISE), are analyzed to assess the effectiveness of each controller. Results demonstrate that PSO-FPID achieves significantly lower tracking errors and superior orientation control. For straight-line trajectories, tracking errors were 0.525, 1.529, and 2.992 for PSO-FPID, FPID, and PID, respectively, confirming the advantage of the proposed approach. Overall, the PSO-tuned FPID controller delivers more precise and reliable trajectory tracking, leading to effective seed placement and improved agricultural efficiency.