Simulated Annealing Algorithm Based Tuning of LQR Controller for Overhead Crane

Overhead cranes are utilized for transporting loads in various workplaces while avoiding spatial conflicts with workers. Many researchers have investigated the control of cranes to increase productivity and safety. LQR is an often-studied controller used for this purpose. In practice, the weighting matrices of the LQR controller are tuned manually utilizing a trial and error method. In this paper, the use of the Simulated Annealing Algorithm (SAA) to tune the weighting matrices of the LQR is investigated. A new objective function is proposed which incorporates important criteria which are: minimization of overshoot, rise time, and settling time. The performance of the algorithm for crane control is evaluated in both simulations and lab experiments.