Fuzzy Flight Control Research Articles

HYBRID MULTI-AGENT METHOD FOR OPTIMIZATION OF FUZZY COMPUTER SYSTEMS

Recently, intelligent computer systems based on fuzzy logic and soft computing are used quite effectively to solve a wide range of complex applied problems in various fields of human activity. One of the promising areas of modern research in the field of artificial intelligence is the creation and testing of bioinspired multi-agent and evolutionary methods for the synthesis and optimization of fuzzy automatic control systems (ACS) and decision support systems (DSS). This paper is devoted to the development and research of a multi-agent method of parametric optimization of fuzzy systems (FS) based on hybrid improved grey wolf algorithms. The proposed method allows optimizing the parameters of fuzzy computer systems more efficiently, compared to the basic and improved grey wolf methods. This method uses group hunting and dimensional learning-based hunting strategies, as well as local search strategies based on algorithms of gradient descent and extended Kalman filter, which significantly reduces computational costs and increases the rate of convergence to optimal solutions when optimizing parameters of complex fuzzy systems. To study the effectiveness of the developed hybrid multi-agent method in this work, the synthesis and parametric optimization of the fuzzy flight control system for the unmanned aerial vehicle (UAV) is carried out. In particular, the optimization of the vectors of normalizing coefficients, adjustable parameters of linguistic terms (LT), and weight coefficients of the rule's consequences of the rule base (RB) for the altitude controller of the fuzzy ACS for the UAV is performed. The obtained results of the comparative analysis confirm the significant advantages of the developed hybrid multi-agent method of parametric optimization, as well as the feasibility of its application for the synthesis of different types of ACSs and DSSs based on fuzzy logic.

Event-Triggered Fuzzy Flight Control of a Two-Degree-of-Freedom Helicopter System

In this article, the problem of flight control for a two-degree-of-freedom helicopter system is studied. Since the helicopter is a multiinput, multioutput nonlinear control system, a Takagi–Sugeno (T–S) fuzzy model is applied to approximate the system. All submodels of the new T–S fuzzy model contain constant terms due to the nonlinear characteristics of the helicopter system. In this article, sampled-data control is considered and the sampled data are transmitted to the system over a communication network. A large amount of sampled data transmitted over the network can significantly increase the computational and communication burdens for the network with a limited bandwidth. To overcome this difficulty, an event-triggered mechanism is introduced. In order to validly control the T–S fuzzy system, a fuzzy proportional integral-derivative (PID) controller is designed based on the Lyapunov method and practical stability criteria, which are obtained by using improved integral inequalities and the linear matrix inequality technique. Finally, a numerical example is given to show the effectiveness of the obtained results.

Quadrotor attitude and position fuzzy control using linearised inverted moment matrix

Controlling non-linear dynamic model of quadrotor is a challenging task since the quadrotor is considered as an under-actuated system with highly coupled dynamics due to both the six Degrees Of Freedom (DOF) motion and four control input variables. The objective of this research is to investigate the implementation of fuzzy logic concepts using linearised inverted moment matrix in order to design Multi-Inputs-Multi-Outputs (MIMO) fuzzy flight control system for the quadratic non-linear dynamic model. Non-linear mathematical model of the quadrotor is derived and MIMO attitude and position fuzzy controllers are designed. The invert of moment matrix is linearised and differential actions of PD-fuzzy controllers are considered as well. The quadrotor dynamic behaviour is simulated and evaluated using M-file programs, the results have shown that using PD-fuzzy controller type is capable to achieve the design requirements and significantly improves the performance of the quadrotor stability characteristics.

Computer Vision-based Navigation and Predefined Track Following Control of a Small Robotic Airship

For small robotic airships, it is required that the airship should be capable of following a predefined track. In this paper, computer vision-based navigation and optimal fuzzy control strategies for the robotic airship are proposed. Firstly, visual navigation based on natural landmarks of the environment is introduced. For example, when the airship is flying over a city, buildings can be used as visual beacons whose geometrical properties are known from the digital map or a geographical information system (GIS). Then a geometrical methodology is adopted to extract information about the orientation and position of the airship. In order to keep the airship on a predefined track, a fuzzy flight control system is designed, which uses those data as its input. And genetic algorithms (GAs), a general-purpose global optimization method, are utilized to optimize the membership functions of the fuzzy controller. Finally, the navigation and control strategies are validated.

A fuzzy flight controller combining linguistic and model-based fuzzy control

In this paper, we address the design of a fuzzy flight controller that achieves stable and robust “aggressive” maneuverability for an unmanned helicopter. The fuzzy flight controller proposed consists of a combination of a fuzzy gain scheduler and linguistic (Mamdani-type) controller. The fuzzy gain scheduler is used for stable and robust altitude, roll, pitch, and yaw control. The linguistic controller is used to compute the inputs to the fuzzy gain scheduler, i.e., desired values for roll, pitch, and yaw at given desired altitude and horizontal velocities. The flight controller is obtained and tested in simulation using a realistic nonlinear MIMO model of a real unmanned helicopter platform, the APID-MK3.

Enhancing Flight Safety: Recovery from Windshear During Take-Off

Right safety during normal as well as during hazardous weather conditions is a key issue in modern flight control systems. From all weather phenomena, low-altitude wind shear remains a particular concern to flight safety. Aircraft might not be able to cope with the energy loss due to a windshear encounter. A fuzzy flight controller that employs total energy concepts has been extended to improve safety during windshear. Through incorporating a special windshear recovery procedure, the fuzzy controller provides aggressive thrust management and flight envelope protection. The application of this generic windshear recovery concept in a take-off scenario is demonstrated by simulation examples.