Particle swarm optimization based fuzzy sliding mode controller for the Twin Rotor MIMO system

Allouani, Fouad; Boukhetala, Djamel; Boudjema, Farès

doi:10.1109/melcon.2012.6196611

Cited by 19 publications

(5 citation statements)

References 6 publications

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“…Fire water system control regulators pumps running under normal conditions, constant pressure, the pressure can be considered to remain unchanged, is a dead process: pressure rise during the time constant is first-order inertia link, shown in its transfer function is as follows [4][5][6][7] :…”

Section: System Modelingmentioning

confidence: 99%

Fire Water Supply Control System of Petrochemical Enterprises

Ren

Song

2013

AMR

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The petroleum chemical industry in the production process, fire water supply control system can not meet the normal transmission of signal nonlinear, reliability is low. Compared with the conventional fuzzy PID control system of PID water supply system, can be effective for the nonlinear input signal, and faster than the reaction rate of PID control, the more reliable. Application of fuzzy control to the fire water supply system of conventional, enhanced fire system speediness, stability and reliability.

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Section: System Modelingmentioning

confidence: 99%

Fire Water Supply Control System of Petrochemical Enterprises

Ren

Song

2013

AMR

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“…It is non-linear system and its non-linearity design of model is very complex. These are the MIMO system which is defined as the Multi-input Multi-output with their significant cross coupling (Allouani et al, 2012). The TRMS system like a helicopter model (TRMS 33-949S).…”

Section: Introductionmentioning

confidence: 99%

Control of Twin Rotor MIMO System using PID and LQR Controller

Chaudhary

Kumar

2019

Journal of Aircraft and Spacecraft Technology

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Control design of a TRMS system such as helicopter is very complicated task. Since the TRMS are very high non-linear system, so its non-linearity too high and the control design is very difficult problem for TRMS system. It has two rotors, the first one is the main rotor and the second one is tail rotor. The TRMS System are based on the beam and support by a counter balance. Horizontal and vertical plane are included in the TRMS System for PID and LQR controller both are discuss in this paper. PID and LQR controller both are discuss in this paper. By Simulink result for vertical and horizontal plane, LQR controller better the PID control. All the simulation work is done by the MATLAB/Simulink of the results, environment and the working of simulation results have been done at the end of this paper.

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“…Based on these studies, in this paper, the authors propose an L 1 adaptive controller for a two-input-two-output vertical rotor orientation control system and design an experimental rig based on dSPACE to test the performance of the rotor orientation control system. The work in this paper may also be applied to twin-rotor mechanical system control [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

L1 Adaptive Control for a Vertical Rotor Orientation System

Liu

Yang

et al. 2016

Applied Sciences

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Bottom-fixed vertical rotating devices are widely used in industrial and civilian fields. The free upside of the rotor will cause vibration and lead to noise and damage during operation. Meanwhile, parameter uncertainties, nonlinearities and external disturbances will further deteriorate the performance of the rotor. Therefore, in this paper, we present a rotor orientation control system based on an active magnetic bearing with L 1 adaptive control to restrain the influence of the nonlinearity and uncertainty and reduce the vibration amplitude of the vertical rotor. The boundedness and stability of the adaptive system are analyzed via a theoretical derivation. The impact of the adaptive gain is discussed through simulation. An experimental rig based on dSPACE is designed to test the validity of the rotor orientation system. The experimental results show that the relative vibration amplitude of the rotor using the L 1 adaptive controller will be reduced to ∼50% of that in the initial state, which is a 10% greater reduction than can be achieved with the nonadaptive controller. The control approach in this paper is of some significance to solve the orientation control problem in a low-speed vertical rotor with uncertainties and nonlinearities.

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Particle swarm optimization based fuzzy sliding mode controller for the Twin Rotor MIMO system

Cited by 19 publications

References 6 publications

Fire Water Supply Control System of Petrochemical Enterprises

Fire Water Supply Control System of Petrochemical Enterprises

Control of Twin Rotor MIMO System using PID and LQR Controller

L1 Adaptive Control for a Vertical Rotor Orientation System

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