Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task

Abstract

Development of unmanned aerial vehicles (UAVs) has become the most important research areas in the field of autonomous aeronautical control. This paper proposes a robust and intelligent controller based on adaptive-network-based fuzzy inference system (ANFIS) and improved ant colony optimization (IACO) to govern the behavior of a three degree of freedom quadrotor UAV. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of aircraft are highly nonlinear. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the desired trajectory of the quadrotor in 2D Vertical plane and the IACO algorithm aims is to facilitate convergence to the ANFIS's optimal parameters in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed IACO tuned ANFIS controller, a comparison between the proposed ANFIS-IACO controller and other controller's performance such us ANFIS only and proportional-integral-derivative controllers is illustrated using the same system. As expected, the hybrid ANFIS-IACO controller gives very satisfactory results than the others methods already developed in the same study.

Fractional order modelling and robust multi-model intelligent controllers’ synthesis for ACP1000 nuclear power plant

“…Where the membership functions of Al and Bl are described by bell-shaped functions [17], given as follows.…”

“…A research has been explored in which ANFIS controller has been used for single phase full bridge inverter in [16]. An investigation has been made in [17] for the optimization of ANFIS controller using ant colony algorithm. In [18], different algorithms such as grid portioning, subtractive clustering, fuzzy C-mean clustering, and context based fuzzy C-mean clustering methods have been investigated for the optimization of ANFIS controllers.…”

UAV Swarm Intelligence: Recent Advances and Future Trends

“…The control layer controls the UAV to fly according to the planned path, which is the basis of UAV cluster research. By establishing control system frameworks [77]- [80] and designing corresponding controllers [81]- [83]], it is possible to solve the reconstruction of different types of drones in cluster formations [84]- [89], cluster search And tracking [93]- [95]and cluster anti-collision and other aspects [96]- [98]. An overall summary of the classification of the control layer is given in TABLE 4.…”

“…Selma B et al proposed a robust intelligent controller based on an adaptive network fuzzy inference system (ANFIS) and improved ant colony optimization (IACO) to control the behavior of three-degree-of-freedom four-rotor aircraft. Using the ANFIS controller to reproduce the desired trajectory of the quadrotor in the two-dimensional vertical plane, this method reduces the learning error and improves the quality of the controller [81]. For the full-attitude maneuvering control of the suspension load of multiple UAVs under uncertain conditions, a controller that can handle the uncertain parameters of the crane system is used, and the limit of evaluation using the planned trajectory is given [82].…”

Optimal Design and Performance Comparison of a Combined ANFIS-PID with Back Stepping Technique, Using Various Meta-Heuristic Algorithms to Solve Wheeled Mobile Robot Trajectory Tracking Problem

“…The ANFIS could solve this problem by combining fuzzy logic controller advantage to deal with the uncertainties with the ability of neural networks to learn from plants/processes, this concept has attracted an exceptional attention in various engineering fields [36][37][38][39][40]. In this context, several hybrid designs have been made to enhance the robustness of the ANFIS controlled system, by benefiting from advantage and simplicity of a PID controller, we distingue the following mergers: hybrid ANFIS-PID [41][42][43], (ANFIS+I) controller optimizing using multidimensional PSO for Quadrotor Position Control [44], (ANFIS-PD+I) controller for robot manipulator [45].…”