Adaptive Fuzzy Control of Robot Manipulators with Asymptotic Tracking Performance

Fateh, Sara; Fateh, Mohammad Mehdi

doi:10.1007/s40313-019-00496-5

Cited by 28 publications

(19 citation statements)

References 18 publications

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“…Therefore, ANFIS is used as the classifier in the developed method. ANFIS has excellent performance in different engineering tasks such as signal processing, classification, fault detection, control of nonlinear systems, image processing, clustering and time series prediction (Fateh and Fateh 2020;Bouyahya et al 2020;Farahani and Rahmani 2019).…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

Katooli

Koochaki

2020

J Control Autom Electr Syst

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Three-phase transformers (TPT) play a significant and crucial function in the power networks in order to connect the subsystems and deliver the electrical energy to final customers. The TPT are one of the most high-priced equipment in modern power networks, and therefore their working condition should be constantly monitored to prevent their breakdown, power outages and huge financial damage. Accordingly, this paper presents a hybrid method for detection and classification of incipient faults in TPT using dissolved gas analysis techniques (DGAT) information and rule-based machine learning method. In the developed method, the most informative and important items of DGAT data out of 14 items selected by association rules mining technique (ARMT) are employed as the input of adaptive neuro-fuzzy inference system (ANFIS). The ARMT is implemented to select the items, which have maximum information and can train the ANFIS more accurately. Furthermore, in order to enhance the accuracy of ANFIS and improve its robustness in different implementations, black widow optimization algorithm is applied for ANFIS training. In order to evaluate the performance of developed method on real issues, two industrial data collections obtained from Iran-Transfo Company chemical laboratory and Damavand power substations are used. The obtained results through MATLAB simulations proved that the developed method has high fault detection and classification accuracy, robust function in different implementations, short run time and simple structure.

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Section: Introductionmentioning

confidence: 99%

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

Katooli

Koochaki

2020

J Control Autom Electr Syst

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“…It is also challenging to establish a systematic NN control theory to solve a series of problems, such as the mechanism of NN control, stability analysis, systematic design, etc. Backstepping control usually leads to the problem of "complexity explosion" when it is applied in the processing of unknown functions, so the methods of adaptive NN control [36], adaptive fuzzy control [45] and adaptive NN backstepping control [35] are put forward to address such a problem. These techniques enable systems to be greatly adaptive and robust obeying the required performance criteria for the control.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Control of Chaotic Fractional-Order Permanent Magnet Synchronous Motors Using Backstepping Technique

2020

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The backstepping technique is greatly effective for the integer-order triangular non-linear systems. Nevertheless, it is dramatically challenging to implement backstepping technique in the manipulation of fractional-order permanent magnet synchronous motors (FOPMSMs), since the fractional derivatives of the composite functions are deeply complex. In this paper, adaptive neural network (NN) backstepping-based control scheme for FOPMSMs on the basis of fractional Lyapunov stability criterion is established. First, we propose a novel adaptive synchronous controller for FOPMSMs by coupling with NNs and backstepping technique. Then, we present a detailed stability analysis in terms of FOPMSMs via the proposed controller. Finally, a simulation example is given to reveal that the proposed controller can effectively eliminate or restrain the chaos of FOPMSMs, and keep the tracking signals synchronous with the reference signals.

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“…Most of the researches use the powerful optimization tools of the linear matrix (LMI) to ensure and guarantee appropriate conditions for the closed-loop system stability (Zhang and Yang 2018;Wang et al 2019;Zhang et al 2019). Extensive researches have been developed for observerbased decentralized control on the connected systems (Goel and Swarup 2017;Fateh and Fateh 2020;Ranjbar et al 2020;and Yu 2020). Goel and Swarup (2017) state that a robust super-twisting sliding mode controller for a multiinput-multi-output system has been provided.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed controller gains are selected, using an adaptive estimation mechanism to tackle the gain overestimation problem. Fateh and Fateh (2020) state that a decentralized adaptive fuzzy control design (AFC) based on voltage control strategy is presented with an approximate fuzzy error com-pensator. Asymptotic tracking performance is adjusted by compensating for the approximate fuzzy errors adaptively.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Observer-Based Decentralized Sliding Mode Control for Connected Systems in the Presence of Unknown Interconnection and Bounded Disturbance

Ranjbar

Noiey

Rezaie

2020

J Control Autom Electr Syst

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In this manuscript, the control problem for a class of connected systems with unknown interconnections in the presence of distributed disturbance is studied. For this purpose, an adaptive observer-based decentralized sliding mode controller is proposed to provide the stability of the closed-loop system. In this approach, the connection matrix between subsystems is also assumed unknown, and therefore the dependency to the knowledge of connections is reduced. Due to the lack of availability of most states, a combination of the Luenberger observer and the sliding mode controller is proposed. The observer is to estimate the unmeasurable states, observing the disturbance effects, and thereby facilitating determination of the control law in an adaptive scheme. Furthermore, the effects of unknown interconnections between subsystems are estimated using adaptive laws, to find equivalent of interconnections among subsystems. To control and stabilize the overall system, a decentralized sliding mode controller is designed to stabilize the original system using the estimated disturbance and states. To analyze the stability, based on the proposed observer, direct method of Lyapunov is proposed together with the LMI technique. The stability of system and convergence of the estimation error of each subsystem to zero is guaranteed. The observer-based method is finally applied into two numerical examples to verify the significance of the proposed approach.

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Adaptive Fuzzy Control of Robot Manipulators with Asymptotic Tracking Performance

Cited by 28 publications

References 18 publications

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

Adaptive Neural Network Control of Chaotic Fractional-Order Permanent Magnet Synchronous Motors Using Backstepping Technique

Adaptive Observer-Based Decentralized Sliding Mode Control for Connected Systems in the Presence of Unknown Interconnection and Bounded Disturbance

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