A comparison of bio-inspired optimization methodologies applied to the tuning of industrial controllers

Céspedes, M.; Contreras, Monica; Cordero, José Antonio; Montoya, Gustavo; Valverde, Karen Brenes; Rojas, José David

doi:10.1109/concapan.2016.7942340

Cited by 4 publications

(1 citation statement)

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“…Ant colony optimization (ACO), invasive weed optimization (IWO), genetic algorithms (GA), neural networks (NN), particle swarm optimization (PSO) and biogeography-based optimization (BBO) are examples of bio-inspired optimization methodologies that have been used for tuning the parameters of drone controllers [6], [7]. In [8], ACO was used to tune fuzzy PID controller parameters.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms for Multi-Objective Optimization of Drone Controller Parameters

Shamshirgaran

Javidi

Simon

2021

Preprint

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Drones are effective for reducing human activity and interactions by performing tasks such as exploring and inspecting new environments, monitoring resources and delivering packages. Drones need a controller to maintain stability and to reach their goal. The most well-known drone controllers are proportional-integral-derivative (PID) and proportionalderivative (PD) controllers. However, the controller parameters need to be tuned and optimized. In this paper, we introduce the use of two evolutionary algorithms, biogeography-based optimization (BBO) and particle swarm optimization (PSO), for multi-objective optimization (MOO) to tune the parameters of the PD controller of a drone. The combination of MOO, BBO, and PSO results in various methods for optimization: vector evaluated BBO and PSO, denoted as VEBBO and VEPSO; and non-dominated sorting BBO and PSO, denoted as NSBBO and NSPSO. The multi-objective cost function is based on tracking errors for the four states of the system. Two criteria for evaluating the Pareto fronts of the optimization methods, normalized hypervolume and relative coverage, are used to compare performance. Results show that NSBBO generally performs better than the other methods.

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Section: Introductionmentioning

confidence: 99%