Quantum Particle Swarm Optimisation Proportional–Derivative Control for Trajectory Tracking of a Car-like Mobile Robot

The goal of this research is to formulate and compare two algorithms, classical particle swarm optimisation (PSO) and quantum PSO (QPSO), for optimising the motion of a car-like mobile robot. Both algorithms are evaluated on the basis of their reduction and stabilisation of the root mean square error (RMSE) between the robot’s desired and actual trajectories. An implementation of the robot’s dynamic motion is provided. The robot’s mass and inertia are considered. The robot’s settings and the viscosity of the surroundings present a few obstacles to following the specified path. For each algorithm, the proportional (Kp) and derivative (Kd) parameters of the controller are optimised, and the convergence speeds and stabilities of the controllers are compared. The results show that both algorithms perform comparably. However, the QPSO method converges faster and is more stable at optimal Kp and Kd values. The ramifications of this research extend beyond trajectory tracking. Enhanced optimisation approaches can lead to higher performance in a variety of robotic systems, including autonomous cars, drones, and automation systems, by employing advanced quantum algorithms, such as QPSO.