Bio-Inspired Multiojective Tuning of PID-Controlled Antilock Braking Systems

The proportional-integral-derivative (PID) controller is widely adopted to control numerous process in industrial applications owing to its simplicity, clear functionality, and effectiveness. Due to a wide range of applications, distinct methods to determine PID gains are available in the specialized literature, and multiobjective optimization methods became an attractive approach to solve this problem, mainly when more complex systems are considered. Additionally, it is important to compare novel metaheuristics with traditional methods to follow the evolution of the state of the art. For that purpose, it was proposed a new multiobjective formulation of a PID-controlled antilock braking system (ABS) optimization through nondominated sorting genetic algorithm II (NSGA-II), multiobjective dragonfly algorithm (MODA), multiobjective salp swarm algorithm (MSSA) and a new version of MSSA with opposition based learning initialization and evolution. The experimental results were compared through spacing, euclidean distance and hypervolume metrics and showed that the change made in the MSSA (1) have improved the dominance and (2) the spread of Pareto front (PF) solutions.