GWO-Based Performance Improvement of PD-Type Iterative Learning Fuzzy Control of Tower Crane Systems

The current paper suggests the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems for cart, arm angular and payload positions of tower crane systems. Three cost-effective SISO fuzzy controllers are implemented as first order discrete-time intelligent Proportional-Integral controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. These fuzzy controllers are included in three SISO current iteration Iterative Learning Control system structures with PD-type learning rules. The parameters of the PD-type learning rules are optimally tuned in the iteration domain using a metaheuristic Grey Wolf Optimizer (GWO) algorithm, which solves optimization problems with objective functions defined as the sums of squared control errors multiplied by time. The performance improvement is proved in terms of applying ten iterations of GWO.