Toward intelligent flight control

Stengel, Robert F.

doi:10.1109/21.257764

Cited by 85 publications

(25 citation statements)

References 87 publications

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“…The artificial intelligent methods such as fuzzy systems, neural networks and expert systems have the potential to "learn" the plant model from input-output data or "learn" fault knowledge from past experience, and they can be used as function approximators to construct the analytical model for residual generation, or as supervisory schemes to make the fault analysis decisions [18]. The nonlinear modeling ability of neural networks has been utilized for nonlinear fault diagnosis problems [19]- [21].…”

mentioning

confidence: 99%

Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Diao

Passino

2001

IEEE Trans. Contr. Syst. Technol.

149

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Abstract-Stimulated by the growing demand for improving the reliability and performance of systems, fault-tolerant control has been receiving significant attention since its goal is to detect the occurrence of faults and achieve satisfactory system performance in the presence of faults. To develop an intelligent fault-tolerant control system, we begin by constructing a design model of the system using a hierarchical learning structure in the form of Takagi-Sugeno fuzzy systems. Afterwards, the fault-tolerant control scheme is designed based on stable adaptive fuzzy/neural control, where its on-line learning capabilities are used to capture the unknown dynamics caused by faults. Finally, the effectiveness of the proposed methods has been studied by extensive analysis of system zero dynamics and asymptotic tracking abilities for both indirect and direct adaptive control cases, and by "component level model" simulation of the General Electric XTE46 turbine engine.

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mentioning

confidence: 99%

Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Diao

Passino

2001

IEEE Trans. Contr. Syst. Technol.

149

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“…They can approximate the nonlinear functions in order to construct the analytical model for generating residuals. Moreover, they can be used as classifiers to perform fault detection and analysis [1]. Neural networks are known to approximate any nonlinear function and they are widely used in nonlinear (and robust) fault diagnosis problems [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Feature-based fault detection of industrial gas turbines using neural networks

Rasaienia¹,

Moshiri²,

Moezzi³

2013

Turk J Elec Eng & Comp Sci

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Gas turbine (GT) fault detection plays a vital role in the minimization of power plant operation costs associated with power plant overhaul time intervals. In other words, it is helpful in generating pre-alarms and paves the way for corrective actions in due time before incurring major equipment failures. Hence, finding an efficient fault detection technique that is applicable in the online operation of power plants involved with minor computations is an urgent need in the power generation industry. Such a method is studied in this paper for the V94.2 class of GTs. As the most leading stage for developing a feature-based fault detection system and moving from a fixed time-scheduled maintenance to a condition-based one, principal component analysis is used for dimension reduction in the sensor data space and dimensionless key features are employed instead. One healthy condition and 6 faulty conditions are used to provide a realistic data set that is used for feature extraction, training, and testing artificial neural networks. In the proposed method, multilayer perceptron (MLP) and learning vector quantization (LVQ) networks are used for the fault classification. The good performance of the LVQ networks is presented by properly selecting the network architecture and respective initial weight vectors. When comparing the results of the MLP and LVQ networks for the fault classification, the LVQ network shows better classification results.

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“…"reflexive skills", typically achieved through a low level feedback control system) and a high-level knowledge of the set of vehicle's achievable behaviors (Stengel 1993). Stengel (1993) proposes a method for controlling maneuvered formation of autonomous non-holonomic vehicles with the purpose of obtaining a desired target region. This approach was based on tracking of pairs of virtual leaders whose control inputs are obtained in a single optimization process based on model predictive control (MPC) technique.…”

Section: Introductionmentioning

confidence: 99%

A Novel Integrated Guidance and Control System Design in Formation Flight

Sadeghi

Abaspour

Sadati

2015

J.Aerosp. Technol. Manag.

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Bird's formation flight is one of the best types of cooperation in nature. The bird's flight was the motivation of humans for flying. After one century of flight development, bird's formation flight was the motivation of humans for aircraft's formation flight. The closeness of aircrafts in formation flight and the effect of disturbances such as vortex make the formation flight control a challenging issue for control designers. This paper introduces a novel integration between guidance commands and system controller inputs. In recent papers the control system inputs were derived from approximate equations, and this approximation caused maneuver limitation. To tackle this problem, a new method is introduced, which employs proportional-integral-derivative (PID) controller in the integration block. This integrated guidance and control system employs the pure pursuit guidance to determine the unmanned aerial vehicle's acceleration command. A two-loop dynamic inversion technique is used for designing attitude and velocity controller, while the acceleration feedback control is used between the guidance system and attitude controller, which leads to increase in maneuverability of unmanned aerial vehicle's formation flight. The simulation results show that the proposed method can control the UAV's formation with sufficient accuracy in severe maneuvers.

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Toward intelligent flight control

Cited by 85 publications

References 87 publications

Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Feature-based fault detection of industrial gas turbines using neural networks

A Novel Integrated Guidance and Control System Design in Formation Flight

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