Optimisation of fuzzy logic quadrotor attitude controller – particle swarm, cuckoo search and BAT algorithms

Abstract

Bio-inspired optimization algorithms have recently attracted much attention in the control community. Most of these algorithms mimic particular behaviors of some animal species in such a way that allows solving optimization problems. The present paper aims at applying three metaheuristic methods for optimizing Fuzzy Logic Controllers used for quadrotor attitude stabilization. The investigated methods are Particle Swarm Optimization (PSO), BAT algorithm and Cuckoo Search (CS). These methods are applied to find the best output distribution of singleton membership functions of the Fuzzy Controllers. The quadrotor control requires measured responses, therefore, three objective functions are considered: Integral Squared Error, Integral Time-weighted Absolute Error and Integral Time-Squared Error. These metrics allow performance comparison of to compare the controllers in terms of tracking errors and speed of convergence. The simulation results indicate that BAT algorithm demonstrated higher performance than both PSO and CS. Furthermore, BAT algorithm is capable of offering 50% less computation time than CS and 10% less time than PSO. In terms of fitness, BAT algorithm achieved an average of 5% better fitness than PSO and 15% better than Cuckoo Search. According to these results, the BAT-based Fuzzy Controller exhibits superior performance compared with other algorithms to stabilize the quadrotor.

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