Model Reference Adaptive Control of Piecewise Linear Systems with Applications to Aircraft Flight Control

Sang, Qian

doi:10.18130/v3q79n

Cited by 5 publications

(13 citation statements)

References 41 publications

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“…The above stated challenge was solved in reference [1] by the use of switching index or indicator function. Such implementation has its own challenges.…”

Section: Technical Approachmentioning

confidence: 99%

“…The design procedure proposed in reference [1] was followed to develop the adaptive controller gains used in this work.…”

Section: System Developmentmentioning

confidence: 99%

“…Detailed steps for the development of these adaptive controllers and proof of its stability are well documented in [1] and will not be repeated here. The equations for updating the gains of the adaptive controllers are given here.…”

Section: System Developmentmentioning

confidence: 99%

“…The propulsion system dynamics and the airframe dynamics both have dual effects on the aircraft and on the interaction among them. Current methodologies used to control these dynamics are not sufficient enough so that the traditional approach of designing the propulsion control system and flight control system for longitudinal control is not effective [1]. We believe that Artificial Neural Networks (ANNs), which have been successfully used as computational tools, have greater prediction ability compared with the classical signal analysis techniques and can be effective in controlling the class of uncertain dynamic systems [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…The outcome of this work can be used for designing practical reconfigurable control systems for controlling uncertain dynamics of commercial aircrafts. In this contest, several control methodologies and architectures have been reported in the literature [1]. Common adaptive control and indirect adaptive control architecture use controllers derived from a plant model, while the model is continuously updated using system identification techniques.…”

Section: Introductionmentioning

confidence: 99%

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Reconfigurable control strategy for aircraft model operating under uncertainty

et al. 2015

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In this paper an intelligent control methodology is developed and used for the design and implementation of reconfigurable controllers capable of switching between different operating conditions of a longitudinal aircraft model. The methodology is based on the adaptive-neuro fuzzy inference system (ANFIS) and is used to design reconfigurable controllers for commercial aircrafts operating under uncertain flight conditions. This methodology is an attempt to solve the problems of using indicator function presented in [I]. Some of these problems include; discontinuity when switching between operating conditions, requirement of an indicator function and, impracticality for real-time implementation. Several linearized longitudinal aircraft models are extracted from a Generic Transport Model (GTM) and are used to test by simulation the performance of the developed control system. In the work, two adaptive controllers are designed and used to test the performance of the reconfigurable control system. Two additional optimal controllers are developed using the LQR method and are used to establish two reference models of the aircraft operating at two different flight conditions. Then an ANFIS based reconfigurable controllers are trained to work in coordination with the established reference models. Finally, the ANFIS based reconfigurable control system is tested under several operating conditions near the operating conditions of the reference models. The different tests of the ANFIS based reconfigurable control system produced desirable results under different operating condition with minimum performance error. The design of the adaptive controllers as well as the development and test results of the reconfigurable control system are all presented in this paper.978-1-4673-7300-5/15/$31.00

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“…The above stated challenge was solved in reference [1] by the use of switching index or indicator function. Such implementation has its own challenges.…”

Section: Technical Approachmentioning

confidence: 99%

“…The design procedure proposed in reference [1] was followed to develop the adaptive controller gains used in this work.…”

Section: System Developmentmentioning

confidence: 99%

Section: System Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reconfigurable control strategy for aircraft model operating under uncertainty

et al. 2015

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Multivariable adaptive distributed leader-follower flight control for multiple UAVs formation

Zhen

2017

Aeronaut. j.

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The Unmanned Aerial Vehicles (UAVs) become more and more popular due to various potential application fields. This paper studies the distributed leader-follower formation flight control problem of multiple UAVs with uncertain parameters for both the leader and followers. This problem has not been addressed in the literature. Most of the existing literature considers the leader-follower formation control strategy with parametric uncertainty for the followers. However, they do not take the leader parametric uncertainty into account. Meanwhile, the distributed control strategy depends on less information interactions and is more likely to avoid information conflict. The dynamic model of the UAVs is established based on the aerodynamic parameters. The establishment of the topology structure between a collection of UAVs is based on the algebraic graph theory. To handle the parametric uncertainty of the UAVs dynamics, a multivariable model reference adaptive control (MRAC) method is addressed to design the control law, which enables follower UAVs to track the leader UAV. The stability of the formation flight control system is proved by the Lyapunov theory. Simulation results show that the proposed distributed adaptive leader-following formation flight control system has stronger robustness and adaptivity than the fixed control system, as well as the existing adaptive control system.

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Intelligent fuzzy controller for lateral control of aircraft models

et al. 2015

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In this paper an intelligent control design methodology is presented for the design of controller for commercial aircrafts operating under abnormal flight conditions. The intelligent methodology is fuzzy logic based reference model control. Therefore, an intelligent fuzzy controller is designed and used as control system to meet transient requirements determined by a desired reference model. A generic transport model (GTM) of commercial aircrafts was used to extract a linearized latitudinal dynamic model and it was utilized for testing the effectiveness of the developed intelligent fuzzy controller. A baseline reference controller is used for performance comparison of the intelligent fuzzy controller. First, a fuzzy based controller was developed to track and control a specified state(s) of the aircraft and then its performance is compared with a baseline nominal reference controller. The testing was conducted under two scenarios: 1) Nominal flight conditions to control the yaw movement of the aircraft and 2) A stall failure on the rudder flight surface. The fuzzy controller was designed and interfaced with the aircraft's aileron and rudder to observe its effectiveness in controlling the lateral movement of the aircraft. The controller was designed based off linearized model of the aircraft to simplify the design process and track the stability of the developed controller. The results obtained from testing of the intelligent fuzzy controller produced desirable performance. The performance of the intelligent fuzzy controller was superior to that produced by the baseline controller. However, the time needed to completely identify and maintain the abnormal state was less than the desired time. Overall, the performance of the intelligent fuzzy controller was superior to that produced by the baseline controller. Details of the design process and the test results of the developed intelligent fuzzy control are report in this paper.

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Model Reference Adaptive Control of Piecewise Linear Systems with Applications to Aircraft Flight Control

Cited by 5 publications

References 41 publications

Reconfigurable control strategy for aircraft model operating under uncertainty

Reconfigurable control strategy for aircraft model operating under uncertainty

Multivariable adaptive distributed leader-follower flight control for multiple UAVs formation

Intelligent fuzzy controller for lateral control of aircraft models

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