Adaptive control of rigid body satellite

Arif, Thawar

doi:10.1007/s11633-008-0296-4

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…The stability analyses are given in [20,22]. For the non-linear system, the proposed adaptive controller in [23] and the stability analysis in [24,25] may be considered as an alternative. If we assume that the input-output relations of the system and the model reference system are identical, then the following relations can be written…”

Section: The First Methods To Obtain the Control Signalmentioning

confidence: 99%

Two new control signal approaches for obtaining the MRAS-CDM and a real-time application

Öcal

Bir

Tibken

2011

Int. J. Autom. Comput.

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The coefficient diagram method (CDM) is one of the most effective control design methods. It creates control systems that are very stable and robust with responses without the overshoot and small settling time. Furthermore, all control parameters of the control systems are changed by varying some adjustment parameters in CDM depending on the demands. The model reference adaptive systems (MRAS) are the systems that follow and change the control parameters according to a given model reference system. There are several methods to combine the CDM with MRAS. One of these is to use the MRAS parameters as a gain of the CDM parameters. Another is to directly use the CDM parameters as the MRAS parameters. In the industrial applications, the system parameters can be changed frequently, but if the controller, by self-tuning, recalculates and develops its own parameters continuously, the system becomes more robust. Also, if the poles of the controlled systems approach the jw axis, the response of the closed-loop MRAS becomes more and more insufficient. In order to obtain better results, CDM is combined with a self-tuning model reference adaptive system. Systems controlled by a model reference adaptive controller give responses with small or without overshoot, have small settling times, and are more robust. Thus, in this paper, a hybrid combination of MRAS and CDM is developed and two different control structures of the control signal are investigated. The two methods are compared with MRAS and applied to real-time process control systems.

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Section: The First Methods To Obtain the Control Signalmentioning

confidence: 99%

Two new control signal approaches for obtaining the MRAS-CDM and a real-time application

Öcal

Bir

Tibken

2011

Int. J. Autom. Comput.

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“…The chosen technique is the MCS which was applied successfully for large-scale systems in several works. 8–10 Motivated by the works of Arif, 10 we follow the same steps to apply this strategy to the control problem of TRMS, thanks to its nature as model-independent control strategy with simple control laws. Furthermore, the use of hyperstability theory for the synthesis of control laws allows to guarantee the asymptotic stability of control system.…”

Section: Application Of Decentralized Adaptive Controller To the Trmsmentioning

confidence: 99%

“…1 MCS is developed originally in Stoten and Benchoubane 2,3 to achieve excellent closed-loop control despite the presence of plant parameter variations, external disturbances, dynamic couplings and plant nonlinearities. [4][5][6][7][8][9][10] In fact, compared to almost all adaptive techniques, the advantage of MCS is not requiring any system model for control law derivation. In addition, it requires few design parameters and then minimal computation time.…”

Section: Introductionmentioning

confidence: 99%

Model reference adaptive control for twin rotor multiple-input and multiple-output system via minimal controller synthesis

Chelihi

Chemachema

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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A model reference adaptive controller for a twin rotor multiple-input multiple-output system is considered in this article. The objective is to make the twin rotor multiple-input multiple-output system move quickly and accurately to the desired attitudes specified by a reference model. Because of the coupling influence between the two axes of the twin rotor multiple-input multiple-output system and its nonlinear complexity, the controller design is performed on the vertical plane and horizontal plane separately. Thus, the nonlinear multiple-input and multiple-output model of the twin rotor multiple-input multiple-output system is decoupled into two subsystems, and the cross-couplings are considered as disturbances to each other. The obtained two models are transformed via Lie derivatives to a canonical form required for adaptive controller design. Then, a hyperstability-based adaptive control technique is applied for each subsystem. The proposed controller design is evaluated in simulations of cross-coupled condition. The obtained results show that the controlled system is robust against disturbances with high tracking performance.

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“…The DFLS described by (3) was shown to possess universal approximating capabilities to a large class of nonlinear dynamic systems [17] . Our objective now is to develop an appropriate control law for input u in (1) and an adaptation law for the parameter matrixȲ of the DFLS (5), such that the closed loop system is stable in the sense that the tracking error e = y d − y is uniformly bounded, and the identification errors and identifier parameters are also uniformly bounded.…”

Section: Fig 1 the Basic Configuration Of A Fuzzy Logic Systemmentioning

confidence: 99%

State Observer Based Dynamic Fuzzy Logic System for a Class of SISO Nonlinear Systems

Hamdy

2013

Int. J. Autom. Comput.

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This paper presents an observer based dynamic fuzzy logic system (DFLS) scheme for a class of unknown single-input single-output (SISO) nonlinear dynamic systems with external disturbances. The proposed approach does not need the availability of the state variables. Within this scheme, the DFLS is employed to identify the unknown nonlinear dynamic system. The control law and parameter adaptation laws of the DFLS are derived based on Lyapunov synthesis approach. The control law is robustfied in H∞ sense to attenuate external disturbance, model uncertainties, and fuzzy approximation errors. It is shown that under appropriate assumptions, it guarantees the boundedness of all the signals in the closed-loop system and the asymptotic convergence to zero of tracking errors. The proposed method is applied to an inverted pendulum system to verify the effectiveness of the proposed algorithms.

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Adaptive control of rigid body satellite

Cited by 7 publications

References 28 publications

Two new control signal approaches for obtaining the MRAS-CDM and a real-time application

Two new control signal approaches for obtaining the MRAS-CDM and a real-time application

Model reference adaptive control for twin rotor multiple-input and multiple-output system via minimal controller synthesis

State Observer Based Dynamic Fuzzy Logic System for a Class of SISO Nonlinear Systems

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