A novel error critic for variable structure control with an ADALINE

Efe, Mehmet Önder

doi:10.1191/0142331202tm062oa

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…The method that allows creating and maintaining a predefined sliding motion in the phase space by adjusting the parameters of the Adaline-type controller is considered in [5].…”

Section: Previous Researchmentioning

confidence: 99%

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An Algorithm of Setting the Weights of a Neural Network Controller

Kucherov¹,

Dikhtyarenko²,

Kozub³

2015

JOACE

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The paper considers a novel method of setting a neural networks controller that takes part in the control of a dynamic plant with unknown parameters. The uncertainties are usually overcome by using sliding mode for controller with a switching input signal. Consequently, as a result the obtained system not sufficiently reliable by reason of high frequency switching control signal and long processing time. To remove this deficiency, the paper considers the neural network controller that is set by means of its learning based on the results of the latest testing. The characteristic feature of the algorithm is its ability to fix the faulty control situations introduced into the learning algorithm, thus giving it the properties of self-learning. The suggested algorithm provides the control quasi-optimality on time and accuracy in controlling a dynamic object. Finally, numerical examples are presented to demonstrate system efficiency for developed method 

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“…The method that allows creating and maintaining a predefined sliding motion in the phase space by adjusting the parameters of the Adaline-type controller is considered in [5].…”

Section: Previous Researchmentioning

confidence: 99%

“…Rule (5) is valid whenever the conditions which are optimal to the object mode are not met, that is, the conditions of NN * and  * are not executed. The system execution may be terminated if the operation conditions are the NN * and  * .…”

Section: Problem Formulationmentioning

confidence: 99%

An Algorithm of Setting the Weights of a Neural Network Controller

Kucherov¹,

Dikhtyarenko²,

Kozub³

2015

JOACE

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show abstract

“…The original tuning approach is proposed in Sira-Ramirez and Colina-Morles (1995), where the desired output is available. The integer order version of the problem addressed here is studied in Efe (2002), where the crux of the approach is to extract a quantified error on the applied control signal utilizing the available measurements. In Efe (2002) and Topalov et al (2007), the map (·) is a monotonically increasing function of its argument and a common choice for it is a unit function, i.e.…”

Section: Sliding Mode Control Through a Fractional Order Adaptation Smentioning

confidence: 99%

“…Error on the control signal is naturally not a computable quantity, however, such a quantity can be extracted based on the behavioral properties as considered here and inEfe (2002) andTopalov et al (2007) …”

mentioning

confidence: 99%

ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

Efe

2008

Soft Comput

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This paper presents an approach to improve the performance of intelligent sliding model control achieved by the use of a fundamental constituent of soft computing, named Adaptive Linear Element (ADALINE). The proposed scheme is based on the fractional calculus. A previously considered tuning scheme is revised according to the rules of fractional order differintegration. After a comparison with the integer order counterpart, it is seen that the control system with the proposed adaptation scheme provides (1) better tracking performance, (2) suppression of undesired drifts in parameter evolution and (3) a very high degree of robustness and insensitivity to disturbances. The claims are justified through some simulations utilizing the dynamic model of a two degrees of freedom (DOF) direct drive robot arm and overall, the contribution of the paper is to introduce the fractional order calculus into a robust and nonlinear control problem with some outperforming features that are absent when the integer order differintegration operators are adopted.

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“…For instance, McGookin et al (2000) use a genetic algorithm-based method to optimize the parameters of an SMC. Also, Efe (2002) presents an SMC-based parameter tuning strategy for a single layer artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode controlled bioreactor using a time-varying sliding surface

Tokat

2009

Transactions of the Institute of Measurement and Control

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Bioreactor processes based on substrate concentration have both highly non-linear behaviour and parameter uncertainties mostly bounded with physical and biological constraints, which make them a subject area of sliding mode control. This work deals with sliding mode control of a fermentation process in a continuous stirred bioreactor. A new sliding surface is defined using angular information as a function of time that provides adjustable continuous movement of the sliding surface. Computer simulations are presented to show the effectiveness and efficiency of the proposed method and to make a quantitative comparison with the conventional sliding mode controller having a constant sliding surface. The excess amount of substrate concentration in the mixture causes undesired by-product information. Therefore, the substrate concentration should not stay above its desired value for a long time. It is shown in the simulations that the performance of the proposed method, especially in regard to the settling time of the substrate concentration and reaching time, is improved. It is also shown that different system state trajectories could be obtained with the proposed method by adjusting the defined parameters of the angular information.

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A novel error critic for variable structure control with an ADALINE

Cited by 5 publications

References 12 publications

An Algorithm of Setting the Weights of a Neural Network Controller

An Algorithm of Setting the Weights of a Neural Network Controller

ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

Sliding mode controlled bioreactor using a time-varying sliding surface

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