Multi-objective evolutionary–fuzzy augmented flight control for an F16 aircraft

In this article, the multi-objective design of a fuzzy logic augmented flight controller for a high performance fighter jet (the Lockheed-Martin F16) is described. A fuzzy logic controller is designed and its membership functions tuned by genetic algorithms in order to design a roll, pitch, and yaw flight controller with enhanced manoeuverability which still retains safety critical operation when combined with a standard inner-loop stabilizing controller. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom motion base real-time flight simulator, and flight tested by a qualified pilot instructor

PID vs LQR controller for tilt rotor airplane

The main thematic of this paper is controlling the main manoeuvers of a tilt rotor UAV airplane in several modes such as vertical takeoff and landing, longitudinal translation and the most important phase which deal with the transition from the helicopter mode to the airplane mode and visversa based on a new actuators combination technique for specially the yaw motion with not referring to rotor speed control strategy which is used in controlling the attitude of a huge number of vehicles nowadays. This new actuator combination is inspired from that the transient response of a trirotor using tilting motion dynamics provides a faster response than using rotor speed dynamics. In the literature, a lot of control technics are used for stabilizing and guarantee the necessary manoeuvers for executing such task, a multiple Attitude and Altitude PID controllers were chosen for a simple linear model of our tilt rotor airplane in order to fulfill the desired trajectory, for reasons of complexity of our model the multiple PID controller doesnt take into consideration all the coupling that exists between the degrees of freedom in our model, so an LQR controller is adopted for more feasible solution of complex manoeuvering, the both controllers need linearization of the model for an easy implementation

Improvement of Pitch Motion Control of an Aircraft Systems

The movement of the aircraft pitch is very important to ensure the passengers and crews are in intrinsically safe and the aircraft achieves its maximum stability.The objective of this study is to provide a solution to the control system that features particularly on the pitch angle motion of aircraft systemin order to have a comfort boarding. Three controllers were developed in these projects which wereproportional integral derivative (PID), fuzzy logic controller (FLC), and linear quadratic regulator (LQR) controllers. These controllers will help improving the pitch angle and achievingthe target reference. By improving the pitch motion angle, the flight will be stabilized and in steady cruise (no jerking effect), hence provides all the passengers withthe comfort zone. Simulation results have been done and analyzed using Matlab software. The simulation results demonstrated LQR and FLC were better than PID in the pitch motion system due to the small error performance. In addition, withstrong external disturbances, a single controller is unable to control the system, thus, the combination of PID and LQR managed to stabilize the aircraft

Antiretroviral therapy of HIV infection using a novel optimal type-2 fuzzy control strategy

Abstract The human immunodeficiency virus (HIV), as one of the most hazardous viruses, causes destructive effects on the human bodies' immune system. Hence, an immense body of research has focused on developing antiretroviral therapies for HIV infection. In the current study, we propose a new control technique for a fractional-order HIV infection model. Firstly, a fractional model of the HIV model is investigated, and the importance of the fractional-order derivative in the modeling of the system is shown. Afterward, a type-2 fuzzy logic controller is proposed for antiretroviral therapy of HIV infection. The developed control scheme consists of two individual controllers and an aggregator. The optimal aggregator modifies the output of each individual controller. Simulations for two different strategies are conducted. In the first strategy, only reverse transcriptase inhibitor (RTI) is used, and the superiority of the proposed controller over a conventional fuzzy controller is demonstrated. Lastly, in the second strategy, both RTI and protease inhibitors (PI) are used simultaneously. In this case, an optimal type-2 fuzzy aggregator is also proposed to modify the output of the individual controllers based on optimal rules. Simulations results demonstrate the appropriate performance of the designed control scheme for the uncertain system

Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes

It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller

NASA LaRC Workshop on Guidance, Navigation, Controls, and Dynamics for Atmospheric Flight, 1993

This publication is a collection of materials presented at a NASA workshop on guidance, navigation, controls, and dynamics (GNC&D) for atmospheric flight. The workshop was held at the NASA Langley Research Center on March 18-19, 1993. The workshop presentations describe the status of current research in the GNC&D area at Langley over a broad spectrum of research branches. The workshop was organized in eight sessions: overviews, general, controls, military aircraft, dynamics, guidance, systems, and a panel discussion. A highlight of the workshop was the panel discussion which addressed the following issue: 'Direction of guidance, navigation, and controls research to ensure U.S. competitiveness and leadership in aerospace technologies.

Robust Multivariable controller Design with the simultaneous H2 /H∞/µ for Single Person Aircraft

In a physical system several targets are normally being considered in which each of nominal and robust performance has their own strengths and weaknesses. In nominal performance case, system operation without uncertainty has decisive effect on the operation of system, whereas in robust performance one, operation with uncertainty will be considered. The target of present paper is a balance between nominal and robust performance of state feedback A new approach of present paper is the combination of two controllers of μ and H2/H∞.  The controller for robust stability status, nominal performance, robust performance and noise rejection are designed simultaneous. Controller will be achieved from solving constraint optimization problem. Where a simultaneous H2 /H∞/µ robust multivariable controller has been designed over an X-29 Single Person.  This model has three inputs and three outputs, where the state space equations of the system response to an unstable one. Simulation results show the effectiveness and benefits of the method.DOI:http://dx.doi.org/10.11591/ijece.v3i2.235

9th EASN International Conference on Innovation in Aviation & Space

This Special Issue book contains selected papers from works presented at the 9th EASN (European Aeronautics Science Network) International Conference on Innovation in Aviation & Space, which was held in Athens, Greece from the 3rd until the 6th of September, 2019. About 450 participants contributed to a high-level scientific gathering, providing some of the latest research results on the topic, as well as some of the latest relevant technological advancements. Eight interesting articles, which cover a wide range of topics including characterization, analysis and design, as well as numerical simulation, are contained in this Special Issue

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

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 feed-forward 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.Comment: This paper has been accepted for publication in Information Science Journal 201