The Cuckoo Search Algorithm Applied to Fuzzy Logic Control Parameter Optimization

In the design of control systems, the tuning of controller parameters has a fundamental role in the performance of both transient and steady-state regimes. From this perspective, the tuning of controller parameters has been carried out using perturbation and observation methods, computational tools based on optimization algorithms for low-complexity systems, and more recently, using metaheuristic algorithms for highly complex systems with improved tuning procedures that guarantee the operation and stability of the systems. Thus, avant-garde optimization algorithms that mimic the evolution of self-organizing biological systems, also called metaheuristic nature-inspired algorithms, have gained high relevance due to their great potential for solving optimization problems. Hence, the Cuckoo Search (CS) algorithm, a very promising and nearly recent developed nature-inspired algorithm, has been used in the design and optimization of Fuzzy Logic Control (FLC) systems due to its great potentiality. In particular, this chapter studies the application of the CS algorithm for tuning controller parameters in two different case studies. The first one is associated with the FLC parameter tuning of a nonlinear magnetic levitation system, and the second case study is related to the FLC optimization of the energy management system of a residential microgrid. Simulation results are provided to emphasize and analyze the features of the optimized controllers for the two cases and compared against other more conventional techniques. Obtained outcomes show that the adjustment of FLC parameters, performed through the CS algorithm, is efficient and improves the performance of the two FLC, which makes the CS algorithm becomes a powerful alternative for performing the controller parameter tuning in modern control systems.