PID Controller Design for Overhead Crane Based on Improved GEO

This paper proposes a Golden Eagle Optimizer(GEO) based on Quasi-Oppositional Learning and Quadratic interpolation (IGEO) to solve the problem of tedious parameter optimizing in overhead crane's PID controllers. The IGEO algorithm proposed in this paper introduced an adaptive function to balance global and local searches based on the GEO algorithm, and integrated Quasi-oppositional learning and Quadratic interpolation in the global and local search phases of the proposed algorithm, respectively. The proposed algorithm and three other swarm intelligence optimization algorithms are simulated in six benchmark test functions. Experimental results show that the proposed algorithm has a stronger convergence speed and search ability. And the proposed algorithm is used to optimize the parameters of an overhead crane PID controller. The Experimental simulation results demonstrate that compared with GEO and PSO algorithms, the PID controller based on the proposed algorithm can provide the best comprehensive effect for the overhead crane's trolley positioning control and the lifting payload swing angle control.