Framework of combined adaptive and non-adaptive attitude control system for a helicopter experimental system

Deng, Mingcong

doi:10.1007/s11633-006-0229-z

Cited by 5 publications

(4 citation statements)

References 6 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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“…Equation (11) In order for Af (x(t), t) + Ag(x(t), t)'u(t) to fulfill matching conditions for sliding mode control, it is necessary to calculate h (x (t), u (t), t) defined by g(x(t),t)h(x(t), u(t), t) = Af (x(t), t)+Ag(x(t), t)u(t) (20) then t()=f(x(t), t)+-(x(t), t)h(x(t), u(t), t)+-(x(t), t)u(t) (21) from equation (19). h(x(t), u(t), t) is called the uncertainty of a definite part, and consists of known model configuration and unknown parameters [2]. In this paper, control law is designed by using …”

Section: Experimental System Modelling and Parameters Estimationmentioning

confidence: 99%

“…Concerning with this topic, many works have been developed (for example, [1],etc.). Recently, we gave a combined adaptive and non-adaptive attitude control method [2] based on adaptive sliding mode control method [4], [5] and some conventional control methods [3] for our helicopter experimental system. In [2], it is assumed that the structure of the uncertainty was known but the parameters were unknown.…”

Section: Introductionmentioning

confidence: 99%