Aircraft roll control system using LQR and fuzzy logic controller

Usta, Mehmet Ali; Akyazı, Ömür; Akpınar, Adem

doi:10.1109/inista.2011.5946069

Cited by 31 publications

(18 citation statements)

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“…;-(xz -Xl) (4) For example, the fust point for the membership function NB is 'OOh' and the second point is '28h'. The first slope for membership function NB is 'FFh' since the first slope is a straight line and the second slope is calculated using the Equation.…”

Section: Integration Of Sub-blocks For Modeling Of Controllermentioning

confidence: 99%

“…The MA V control system has to therefore be fast, flexible and robust in order to achieve proper platform stabilization and guidance. Several control strategies have been proposed in the literature, such as PID [2] [3], fuzzy logic [4] [5], sliding mode control [6] [7], neural networks [S] [9], linear quadratic regulator [4] [5], model predictive control [10] [11] etc. with each strategy having its own merits and demerits.…”

Section: Introductionmentioning

confidence: 99%

“…Roll control is a lateral problem and this work is developed to control the roll angle of an aircraft for roll control in order to stabilize the system when an aircraft performs the rolling motion. Transfer function from aileron deflection angle to roll angle is given the following equation[4].28.925 2 +29.815-140 8. …”

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confidence: 99%

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ASIC implementation of fuzzy-PID controller for aircraft roll control

John¹,

Rasheed

Reddy

2013

2013 International Conference on Circuits, Controls and Communications (CCUBE)

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Generally, an aircraft contains three rotational motions: pitch, yaw and roll. The roll motion is controlled by ailerons on either side of the aircraft is considered as the plant.In this work a fuzzy-PID controller has been designed and implemented in ASIC in order to control roll motion. First step is to identify a suitable controller which is followed by modeling of the controller and lastly by the hardware implementation.Software simulations prove that the performance of roll control system has improved significantly using a fuzzy-PID controller compared to a conventional PID controller. The working frequency of the design in FPGA is 29.83MHz while in ASIC it is 56.05MHz.

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Section: Integration Of Sub-blocks For Modeling Of Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ASIC implementation of fuzzy-PID controller for aircraft roll control

John¹,

Rasheed

Reddy

2013

2013 International Conference on Circuits, Controls and Communications (CCUBE)

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“…In recent decades, enormous techniques including linear and non-linear approaches are investigated to propose control schemes for F-16 [5][6][7]. It has been stated that LQR controller to be the best for pitch control of aircraft system compared with linear feedback control in [8].…”

Section: Introductionmentioning

confidence: 99%

System Modeling and Controller Design for Lateral and Longitudinal Motion of F-16

Ahmed¹,

Li²,

Maqsood³

et al. 2019

ACIS

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Classical control laws are still widely used in aviation industry because of their good structural understanding, simplicity, and better tracking control performance. However in recent decades the application of such controllers are getting substantial interest of researchers. This paper addresses controller design method for longitudinal and lateral motion autopilots of F-16. Aircraft complete mathematical model was obtained using Newton-Euler formulism. The non-linear model was linearized around equilibrium points at certain trim conditions to obtain state space model of the system. Comparative analysis of two linear controllers, Proportional-Integral-Derivative (PID) and Linear-Quadratic-Regulator (LQR) is investigated and control algorithm is proposed. Both the control schemes use feedback control laws and a careful selection of tuning parameters for controllers is carried out to track the desired input reference. Effectiveness of both controllers is illustrated with the help Matlab/Simulink figures and results.

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“…In the last two decades several investigations have been reported on LQR, namely, self-adjusting LQR [4], switched LQR [5], hybrid LQR [6] and fuzzy LQR [7]. In addition, LQR techniques have been successfully implemented for a large number of complex systems such as the double inverted pendulum [8], fuel cell systems [9], vibration control system [10], electric vehicles [11], and aircraft [12].…”

Section: Introductionmentioning

confidence: 99%

Algebraic Riccati equation based Q and R matrices selection algorithm for optimal LQR applied to tracking control of 3rd order magnetic levitation system

Kumare¹,

Jerome²

2016

Archives of Electrical Engineering

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This paper presents an analytical approach for solving the weighting matrices selection problem of a linear quadratic regulator (LQR) for the trajectory tracking application of a magnetic levitation system. One of the challenging problems in the design of LQR for tracking applications is the choice of Q and R matrices. Conventionally, the weights of a LQR controller are chosen based on a trial and error approach to determine the optimum state feedback controller gains. However, it is often time consuming and tedious to tune the controller gains via a trial and error method. To address this problem, by utilizing the relation between the algebraic Riccati equation (ARE) and the Lagrangian optimization principle, an analytical methodology for selecting the elements of Q and R matrices has been formulated. The novelty of the methodology is the emphasis on the synthesis of time domain design specifications for the formulation of the cost function of LQR, which directly translates the system requirement into a cost function so that the optimal performance can be obtained via a systematic approach. The efficacy of the proposed methodology is tested on the benchmark Quanser magnetic levitation system and a detailed simulation and experimental results are presented. Experimental results prove that the proposed methodology not only provides a systematic way of selecting the weighting matrices but also significantly improves the tracking performance of the system.

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Aircraft roll control system using LQR and fuzzy logic controller

Cited by 31 publications

References 1 publication

ASIC implementation of fuzzy-PID controller for aircraft roll control

ASIC implementation of fuzzy-PID controller for aircraft roll control

System Modeling and Controller Design for Lateral and Longitudinal Motion of F-16

Algebraic Riccati equation based Q and R matrices selection algorithm for optimal LQR applied to tracking control of 3rd order magnetic levitation system

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