Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task
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Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task Boumediene Selma1 · Samira Chouraqui1 · Hassane Abouaïssa2 Received: 30 March 2019 / Accepted: 12 February 2020 © Springer Nature Switzerland AG 2020
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. Keywords Unmanned aerial vehicle (UAV) · Intelligent control · Adaptive neuro-fuzzy inference system (ANFIS) · Improved ant colony optimization (IACO)
1 Introduction The unmanned aerial vehicles (UAVs) technology has continuously been evolving with exceptional growth over the last years [1], leading to the emergence of a large number of services offered and potential applications. Drones are not meant to only serve with military purposes [2], but have also become widely used in civilian and industrial domain such us logistics and transportation [3–5], photography and filmmaking [6], safety and security [7], mapping [8], agriculture [9, 10], monitoring [11–14], surveillance [15–17], architecture [18–20] and others applications. They were originally used for missions tedious or too dangerous for humans and all this due to their ease of deployment,
low maintenance cost [21], high mobility and hovering capability [22]. In general, UAVs are preferred for their ability to stabilize at a particular position and altitude [23], to fly at various speeds [24–26], to hover in a stationary position over a target [22, 27], and to perform all these maneuvers in close proximity to obstacles [28, 29]. The selection of the optimal controller parameters is a very important issue for every command and control problems in order to reduce learning errors and improve the quality of the controller [30, 31]. Engineers face daily with increasingly complex problems which arise in very different sectors, such as image processing, d
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