Fuzzy Logic Controllers for Aircraft Flight Control

Luo, Jiufei; Lan, Edward

doi:10.1007/978-0-585-28000-4_4

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…Design of the controller (8) In this paper, fuzzy PI controller and FSMC are involved in GPC to minimize the criteria. For the both controllers, seven triangular membership functions are used for input linguistic Figure 2 while the fuzzy rule table is designed in Table I. I) Fuzzy PI controller: The continuous form of a conven tional fuzzy PI controller [18] is given by:…”

Section: Gener Alized Predictive Control Methodsmentioning

confidence: 99%

Generalized predictive control based on fuzzy sliding-mode control strategy and fuzzy PI controller for synchronization of uncertain chaotic systems

Driss

Mansouri

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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In this paper, we consider the synchronization of uncertain chaotic systems using a generalized predictive control (GPC) algorithm based on fuzzy sliding-mode control (FSMC) strategy and fuzzy PI controller respectively. Optimization per formance and synchronization error are the criteria used to distinguish between the two approaches. The control chattering problem affects negatively on the accuracy of FSMC which needs improvements to avoid this problem. The improvements make FSMC more complex to use. Nonetheless, fuzzy PI controller performs better without any improvement, which makes it easier to use. The performances of the two methods are tested considering the synchronization of two uncertain Duffing systems.To illustrate the effectiveness of the described approaches, some numerical results are given.

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Section: Gener Alized Predictive Control Methodsmentioning

confidence: 99%

Generalized predictive control based on fuzzy sliding-mode control strategy and fuzzy PI controller for synchronization of uncertain chaotic systems

Driss

Mansouri

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Solutions known as fuzzy inference systems (FIS) were then designed to map a set of inputs to outputs, using fuzzy logic and fuzzy sets to define vague conditions. These characteristics allowed fuzzy logic and fuzzy set theory to find a lot of uses in various areas, such as medicine [5]- [7], computer security [8]- [14], networking [15], [16], aeronautics [17], stock trading [18], and many others [4], [19]- [22].…”

Section: Introductionmentioning

confidence: 99%

BDFIS: Binary Decision Access Control Model Based On Fuzzy Inference Systems

Regateiro

Pereira

Aguiar

2019

International Conferences on Software Engineering and Knowledge Engineering

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Access control is a ubiquitous feature in almost all computer systems, and as data becomes more and more of an important asset for organizations, so do the associated access control policies. However, with the increase in the amount of data being produced, e.g. in IoT and social networks, the interest in simpler access control is increasing as well since more subjects (public, researchers, etc.) are now requesting access to it. Defining the exact conditions to allow each subject to access the data can be difficult, especially when vaguely defined conditions such as "expertise of a researcher" come into play. Fuzzy Inference Systems (FIS) allow to process these vague conditions and enables access control mechanisms to be more easily applied. The contribution of this paper lies in showing how a FIS can be used to output binary access control decisions (grant/deny) and what are the differences in the inference process that stems from restricting the output to these two output values.

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“…Some of the most important applications of the fuzzy logic theories in the aerospace field include: the performance of both stability augmentation and automatic flight control functions for the longitudinal and lateral-directional motions for an X-29 aircraft (16) , the development of a pitch attitude hold system for a typical fighter jet under a variety of performance conditions that include approach, subsonic cruise and supersonic cruise (17) , the design of a flight control system and navigation tasks for autonomous unmanned aerial vehicles (18) , the development of an intelligent anti-lock braking system (ABS) control for an aircraft (19,20) , the development of an augmented flight control for an F-16 aircraft (21) , and the representation, manipulation and utilisation of aerodynamic characteristics in order to model an aircraft with and without winglets (22) . Another application was implemented in a new method for aerodynamic forces conversion from a frequency to a Laplace domain, which was validated on the F/A-18 aircraft for aeroservoelasticity studies (23) .…”

Section: Introductionmentioning

confidence: 99%

A hybrid fuzzy logic proportional-integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy Part 1: Morphing system mechanisms and controller architecture design

Grigorie

Botez

Попов

et al. 2012

Aeronaut. j. (1968)

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Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. Questions? Contact the NRC Publications Archive team atPublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. If you wish to email the authors directly, please see the first page of the publication for their contact information. NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous. NRC Publications Record / Notice d'Archives des publications de CNRC:http://nparc.cisti-icist.nrc-cnrc.gc.ca/eng/view/object/?id=09754f89-50a9-46b8-b57c-53c657e4118c http://nparc.cisti-icist.nrc-cnrc.gc.ca/fra/voir/objet/?id=09754f89-50a9-46b8-b57c-53c657e4118c AbstractThe present paper describes the design of a hybrid actuation control concept, a fuzzy logic Proportional-Integral-Derivative plus a conventional On-Off controller, for a new morphing mechanism using smart materials as actuators, which were made from Shape Memory Alloys (SMA). The research work described here was developed for the open loop phase of a morphing wing system, whose primary goal was to reduce the wing drag by delaying the transition (from laminar to fully turbulent flows) position toward the wing trailing edge. The designed controller drives the actuation system equipped with SMA actuators to modify the flexible upper wing skin surface. The designed controller was also included, as an internal loop, in the closed loop architecture of the morphing wing system, based on the pressure information received from the flexible skin mounted pressure sensors and on the estimation of the transition location.The controller's purposes were established following a comprehensive presentation of the morphing wing system architecture and requirements. The strong nonlinearities of the SMA actuators' characteristics and the system requirements led to the choice of a hybrid controller architecture as a combination of a bi-positional on-off controller and a Fuzzy Logic Controller (FLC). In the chosen architecture, the controller would behave as a switch between the SMA cooling and heating phases, situations where the output current is 0 A or is controlled by the FLC.In the design phase, a Proportional-Integral-Derivative scheme was chosen for the FLC. The input-output mapping of the fuzzy model was designed, taking account of the system's error and its change in error, and a final architecture for the hybrid controller was obtained. The sh...

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Fuzzy Logic Controllers for Aircraft Flight Control

Cited by 11 publications

References 6 publications

Generalized predictive control based on fuzzy sliding-mode control strategy and fuzzy PI controller for synchronization of uncertain chaotic systems

Generalized predictive control based on fuzzy sliding-mode control strategy and fuzzy PI controller for synchronization of uncertain chaotic systems

BDFIS: Binary Decision Access Control Model Based On Fuzzy Inference Systems

A hybrid fuzzy logic proportional-integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy Part 1: Morphing system mechanisms and controller architecture design

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