Optimization of controls through metaheuristic algorithms applied to aerial vehicles
Abstract
Population-based techniques that are inspired by nature have resulted in effective methods to solve complex optimization problems. These methods are capable of finding optimal parameters for controllers. Here, we designed a compensated Proportional Derivative controller (PD), which enables an unmanned aerial vehicle (UAV) to be positioned in a predetermined path. Optimal parameters for our controller were determined based on minimizing a fitness function computing the error between desired and real dynamics in three-dimensional space. We compared six different metaheuristics methods: Particle Swarm Optimization (PSO), DE variant with linear population size reduction (L-SHADE), Grey Wolf Optimation (GWO), Elephant Herding Optimization (EHO), Whale Optimization Algorithm (WOA) and Continuous-state Cellular Automata Algorithm (CCAA), obtaining the best trajectory when the PSO algorithm was implemented.
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References
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