Fuzzy Sliding mode Control for missile autopilot design

Sadr, Negar Elmi; Momeni, Hamid Reza

doi:10.1109/ieeegcc.2011.5752563

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…Investigations in [10][11][12][13][14][15] illustrate that the sliding mode control method finds widel applications on nonlinear system control and has a good performance. Besides, the closedloop system has insensitivity to disturbance and uncertainties, which means that the system has strong robustness against uncertainties including parameters variations and disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…In [11], by using boundary layer method to restrain the chatting, NE Sadr et al design a sliding mode controller for missile autopilot and the system has strong robustness to uncertainties and disturbances. Besides, Murat Furat et al design a secondorder integral sliding mode controller for a SISO system in [12], making the system overcome the parameter fluctuation and uncertainties caused by the external load.…”

Section: Introductionmentioning

confidence: 99%

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Quadratic Integral Sliding Mode Control for Nonlinear Harmonic Gear Drive Systems with Mismatched Uncertainties

Ding

Xiao

2018

Mathematical Problems in Engineering

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For a class of nonlinear harmonic gear drive systems with mismatched uncertainties, a novel robust control method is presented on the basis of quadratic integral sliding mode surface, and the closed-loop system has satisfying performance and strong robustness against mismatched uncertainties and nonlinear disturbances. Considering time-varying nonlinear torques and parameters variations which are caused by nonlinear frictions and backlash, a nonlinear harmonic gear drive system mathematic model is established and the effect of nonlinear parts is compensated during control system design. It is proven that the quadratic integral sliding mode surface can be reached in finite time and the closed-loop system is asymptotic stable robustly. The simulation studies are carried out in comparison with traditional linear sliding mode control and integral sliding mode control, verifying the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quadratic Integral Sliding Mode Control for Nonlinear Harmonic Gear Drive Systems with Mismatched Uncertainties

Ding

Xiao

2018

Mathematical Problems in Engineering

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“…Many intelligence-related concepts have been applied into various scientific and engineering researches. There are also obvious achievements in the flight control domain [4][5][6][7][8][9][10][11]. In 1986, a general back-propagation algorithm for an artificial neural network (ANN) was introduced by Rumelhart [12].…”

mentioning

confidence: 99%

“…Different wind disturbances are also implemented in the flight simulations. In past decades, many researchers have applied intelligent concepts to the problem of intelligent landing control [7][8][9][10][11], but those intelligent concepts are not adaptive to various wind disturbance conditions. In previous studies, wind disturbances were considered, but the limits are low [8,[30][31][32][33].…”

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confidence: 99%

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Disturbance encountered landing system design based on sliding mode control with evolutionary computation and cerebellar model articulation controller

Juang

2015

Applied Mathematical Modelling

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a b s t r a c tCurrently, most aircraft have an automatic landing system (ALS) installed. In normal flight conditions, an aircraft automatic landing system can significantly reduce the pilot's workload. Conventional automatic landing systems are designed by the use of gain scheduling or traditional adaptive control techniques; once the flight conditions or wind disturbance intensity is beyond the limits of the system, the pilot must turn off the automatic landing system and manually take over the aircraft landing procedures. The purpose of this study is to integrate the cerebellar model articulation controller (CMAC) and the sliding mode control (SMC) into the aircraft landing system. Genetic algorithm (GA), particle swarm optimization (PSO) and chaotic particle swarm optimization (CPSO) are used to adjust the parameters of the sliding mode control. The proposed intelligent control system can not only effectively improve the landing system to counter wind disturbance, but also help the pilots guide the aircraft to a safe landing in difficult environments. In addition, Lyapunov theory is applied to derive adaptive learning rules for the control system. Furthermore, the TI C6713 rapid property is utilized to develop an embedded control system for a digital signal processing (DSP) controller. The realization of on-line real-time control can thereby be achieved.

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Aircraft flight control using method of robustness aimed at uncertainty

Wang

Zheng²,

Wang

2022

J. Phys.: Conf. Ser.

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Method of μ synthesis based on self-contained structured singular value theory provides a resolution for control system with uncertain character. It makes the control system to have enough robustness and optimal control performance. When the aircraft is flying in the dense aerosphere, the autopilot of the aircraft is facing such a control system with a lot of uncertain elements. To obtain the optimal robustness in such case, the method of μ synthesis is used to design the autopilot in this paper. Also the result of simulation is tested and analysed. And the final analysis indicates the adopted controller has an effective result to the uncertainty.

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Fuzzy Sliding mode Control for missile autopilot design

Cited by 7 publications

References 5 publications

Quadratic Integral Sliding Mode Control for Nonlinear Harmonic Gear Drive Systems with Mismatched Uncertainties

Quadratic Integral Sliding Mode Control for Nonlinear Harmonic Gear Drive Systems with Mismatched Uncertainties

Disturbance encountered landing system design based on sliding mode control with evolutionary computation and cerebellar model articulation controller

Aircraft flight control using method of robustness aimed at uncertainty

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