A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

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.

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Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment of the ANFIS parameters to reduce tracking error and improve the quality of the controller. The results showed perfect behavior for the control law to control a UAV trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with ANFIS and PID control methods.

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