Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm. (August 2021)

• Record Type: Journal Article
• Authors: Shirzadeh, Masoud; Amirkhani, Abdollah; Tork, Nastaran; Taghavifar, Hamid
• Is Part Of: ISA transactions. Volume 114(2021)
• Page Start: 171
• Page End: 190
• Publication Date: 2021-08
• Subjects: Type-2 fuzzy controller -- Fuzzy neural network -- Quadrotor -- Trajectory tracking -- Cuckoo algorithm; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2020.12.047
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques ( i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem. Highlights: Trajectory tracking of a quadrotor is proposed using a type-2 fuzzy neural controller. Strong robustness is provided against the structured and unstructured uncertainties. Exponential sliding mode reaching law is introduced for a smooth asymptotic stability. Controller design parameters are obtained optimally through Cuckoo optimization algorithm.