Evolutionary Tuning of PID Controllers for a Spatial Cable-Driven Parallel Robot

The tuning technique of a proportional–integral–derivative (PID) controller for a spatial cable-driven parallel robot by using evolutionary algorithms has not been investigated yet so far. Thus, this study proposes a tuning technique of gains of PID controllers by using three following evolutionary algorithms (EAs): Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). The objective function of optimization is the integral of the square error (ISE) and the minimum energy consumption. The performances of these algorithms are studied and are compared to each other based on responses of the end-effector and convergence characteristics of best values, mean values, and standard deviations. The results reveal that all GA, DE, and PSO give good performances. However, PSO and DE are better compared to GA. The GA needs more generations to achieve optimal gains while PSO and DE need less time to find out the optimal gains.