Self-Organizing Recurrent Wavelet Fuzzy Neural Network-Based Control System Design for MIMO Uncertain Nonlinear Systems Using TOPSIS Method

Lin, Chih-Min; Le, Tien-Loc

doi:10.1007/s40815-018-0550-z

Cited by 21 publications

(6 citation statements)

References 54 publications

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“…Hence, the weight regulation algorithm can be modified in that x i (k) in equation ( 15) can be replaced with e(k) + De(k). Finally, we obtained the running algorithm of the controller, which is expressed as equation (15). This algorithm simplifies the calculation process and also improves the velocity of the convergence…”

Section: Combined Feedforward and Single-neuron Adaptive Pid Controlmentioning

confidence: 99%

“…NN techniques have developed rapidly; 15–18 research works involving the use of NNs for the modeling/correction of hysteresis have been published. 19–22 Deng and Tan 19 created an expanded input field of hysteresis for one-to-one mapping conversion by using a hysteretic operator and then used a NARMAX model to describe the hysteresis in PZT actuators.…”

Section: Introductionmentioning

confidence: 99%

“…10,11 The Bouc-Wen model has the feature of computational simplicity as only one nonlinear differential equation is demanded; however, the symmetric structure of this model limits its modeling accuracy. [12][13][14] NN techniques have developed rapidly; [15][16][17][18] research works involving the use of NNs for the modeling/correction of hysteresis have been published. [19][20][21][22] Deng and Tan 19 created an expanded input field of hysteresis for one-to-one mapping conversion by using a hysteretic operator and then used a NARMAX model to describe the hysteresis in PZT actuators.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

Liang

Hong

et al. 2019

Measurement and Control

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A new polynomial fitting model based on a neural network is presented to characterize the hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural networks can only solve one-to-one mapping, a hysteresis mathematical model is proposed to expand the input of the neural network by converting the multi-valued into one-to-one mapping. Experiments were performed under designed excitation with different driven voltage amplitudes to obtain the parameters of the model using the polynomial fitting method. The simulation results were in good accordance with the measured data and demonstrate the precision with which the model can predict the hysteresis. Based on the proposed model, a single-neuron adaptive proportional–integral–derivative controller combined with a feedforward loop is designed to correct the errors induced by the hysteresis in the piezoelectric actuator. The results demonstrate superior tracking performance, which validates the practicability and effectiveness of the presented approach.

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Section: Combined Feedforward and Single-neuron Adaptive Pid Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

Liang

Hong

et al. 2019

Measurement and Control

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“…However, this is a time-consuming method, and the network's performance needs to be improved [29]. Recently, a self-organizing algorithm has been proposed to automatically construct a network's structure [30][31][32]. In this work, the self-organizing algorithm is applied to obtain a suitable network structure for the designed network.…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of Function-Link Type-2 Fuzzy Asymmetric CMAC Based on Self-Organizing Algorithm and Modified Jaya Algorithm

Quynh

Long

et al. 2020

IEEE Access

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In this work, a novel self-organizing interval type-2 fuzzy cerebellar model articulation controller design for chaotic systems synchronization is presented. A function-link network and an asymmetric-membership function are employed to improve the learning ability and flexibility of the proposed network. The gradient-descent method is used to derive the adaptive laws for online updating the network parameters. Suitable learning rates for adaptive laws can be achieved by employing the modified Jaya algorithm. Furthermore, the proposed self-organizing algorithm is used to generate new fuzzy rules or delete less-active fuzzy rules. The control system's exponential stability is analyzed by applying the Lyapunov theory. Finally, numerical simulations on the synchronization of four-dimensional chaotic systems (considering external disturbances and system uncertainties) are conducted to validate the efficiency and performance of the proposed method. INDEX TERMS self-organizing algorithm, type-2 fuzzy CMAC, Jaya algorithm, function-link network, asymmetric membership function.

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“…The trial-and-error approach is a common way to constrain the network structure dimension, but it usually takes a lot of time to train and learn and does not guarantee that the parameters and network structures can be precisely defined. For this reason, many studies have developed solutions that combine various effective techniques, additional networks and algorithms such as wavelet function [7,22], function link network [24] and self-organising algorithms [22,23].…”

mentioning

confidence: 99%

Wavelet Interval Type-2 Takagi-Kang-Sugeno Hybrid Controller for Time-Series Prediction and Chaotic Synchronization

et al. 2022

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This paper presents a new hybrid neural network for time series prediction and chaotic synchronization. The proposed controller consists of a wavelet interval type-2 Takagi Sugeno Kang fuzzy brain emotional learning controller (WIT2TFBELC), a wavelet interval type-2 Takagi Sugeno Kang fuzzy cerebellar model articulation controller (WIT2TFCMAC), and a robust compensator (RB). The main controller combines the WIT2TFBELC and the WIT2TFCMAC. A TSK fuzzy system is used to create a hybrid structure with the WIT2TFBELC and the WIT2TFCMAC. The TSK fuzzy system can effectively adjust the weighting for the main controller to achieve the time series prediction and chaotic prediction with better tracking response. Morever a robust compensator is used to achieve robust ability of the system. A Lyapunov function was used to establish the adaptive laws and effectively adjust the system parameters online. Finally, two examples of the application of the proposed algorithm are presented to point out the performance of proposed method.INDEX TERMS TSK fuzzy system, Interval type-2 wavelet function, brain emotional learning control, cerebellar model articulation controller, 5D chaotic system, Henon map time series.

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Self-Organizing Recurrent Wavelet Fuzzy Neural Network-Based Control System Design for MIMO Uncertain Nonlinear Systems Using TOPSIS Method

Cited by 21 publications

References 54 publications

Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

Optimum Design of Function-Link Type-2 Fuzzy Asymmetric CMAC Based on Self-Organizing Algorithm and Modified Jaya Algorithm

Wavelet Interval Type-2 Takagi-Kang-Sugeno Hybrid Controller for Time-Series Prediction and Chaotic Synchronization

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