Neuro-Fuzzy-Based Adaptive Control for Autonomous Drone Flight

Information Sciences, 2019

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feedforward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.

15th IFAC Symposium on Information Control Problems in Manufacturing INCOM, 2015

Quadrotors are nonlinear systems that can be controlled by human experts. Since the mathematical models of Quadrotors are quite complex, the expert knowledge may be one way to come to a solution for controlling Quadrotors. However, human experts make occasionally mistakes, and thus some linguistic rules used in the controller may be false or redundant. Hence, fuzzy logic type 2 optimized by ANFIS (Adaptive Neuro-Fuzzy Inference Systems), which can deal with uncertainties, could be applied to control Quadrotors. ANFIS can optimize the number of linguistic rules and the domain of membership functions could be adjusted automatically. In addition, ANFIS should also be capable of identifying bad rules so the digital system (micro-controller) can decrease the computational resources required for implementing the fuzzy logic controller type 2. The controller designed can accomplish excellent experimental results when it is reduced by an ANFIS system. To confirm robustness in the fuzzy logic controller a noise signal was added in the position control loop for the Quadrotor. Besides, a comparison between fuzzy logic controller type 2 tuned by an expert and Fuzzy Logic type 2 optimized by an ANFIS is illustrated. Experimental results confirmed the good response reached when fuzzy logic type 2 optimized by ANFIS is deployed.

2014

Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rot...

Non-linear Multi-Variable Control of Quad-rotor Helicopters using Fuzzy-Neural Networks, 2020

Most of recent Engineering research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. Many different approaches to the control and stabilization of quad-rotor helicopters have been proposed, none of which may either be completely right or wrong, because the solutions to these problems are not straight forward. Some are obviously more suited than others, depending on the situation. In disaster situations, the importance of organizing and managing available resources and the responsibilities for dealing with all humanitarian aspects of emergencies, in particular preparedness, response and recovery in order to mitigate the impact of disasters cannot be overemphasized. Primarily enabled by advancements in Artificial Intelligence, Automatic Control, Communications and Sensor Technologies, flying robots like the miniature quad-rotor helicopter can easily help in assessing disaster situations, navigate and locate victims trapped in buildings or under piles of rubble, and provide first-responders with information about a site or a situation, very quickly and accurately. A robust adaptive fuzzy-neural controller is developed to monitor and control the non-linear, multi-variable flying states of the quad-rotor helicopter; keeping it aloft in the planned path and the desired speed through a series of complex weather conditions. Results show that very stable flight and trajectory following can be achieved with the designed controller.

IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02

This paper proposes an adaptive neuro-fuzzy inference controller using a feed forward neural network based on nonlinear regression. The general regression neural network is used to construct the base of an adaptive neuro-fuzzy system. This neural network uses a different learning capability when compared with the classical clustering algorithm. The parameters of the general regression neural network are obtained using the gradient descent and least squares algorithms. The simplification of the neuro-fuzzy architecture is done throw the elimination of the rules, maintaining the performance of the controller. In the simulation, the adaptive neuro-fuzzy controller is used to control the helicopter motion in the hover flight mode position. The longitudinal and lateral cyclic, the collective and pedals are used to enable the helicopter to maintain its position fixed in space. Results show the effectiveness of the proposed method.

2019

Quadcopters or Drones have multiple applications for different purpose such as investigation, assessment, exploration, rescue and decreasing the human strength in an adverse situation. Unmanned air vehicles (UAV) is designed with four propellers to resolve the issue of constancy however it will make the drone further more complex for the dynamic modelling and for the control system. In this research, smart controller is intended to regulate the attitude of drone. Simulation ideal model and fuzzy logic control approach is formulated to implement for four elementary motions i.e. roll, pitch, yaw, and height of our drone for the application of earthquake. The key objective of this research paper is to develop the anticipated output as compare to the desired input.