Optimal Gantry Crane PID Controller Based on LQR With Prescribed Degree of Stability by Means of GA, PSO, and SA

Abstract

Trade between islands and countries is increasing in the current era of globalization which also increases the traffic of goods at ports. Rubber Tyred Gantry Crane (RTGC) is an important component in the seaports distribution chain, which act as a loading and unloading machine at the container yard. However, heavy trade traffic will likely cause fatigue and negligence if the RTGC is operated manually. Therefore, it is necessary to automate RTGC by applying optimal control. The paper introduces an alternative approach to designing an optimal PID controller built from the LQR method combined with a prescribed degree of stability for achieving the required transient and steady-state responses of RTGC in the port. Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Simulated Annealing (SA) are applied to select the suitable stability degree value and weighting matrices in the LQR cost function. Simulation results indicate that GA can provide the optimal PID controller to follow the reference trajectory and minimize the swing angle better than PSO and SA.