• 제어로봇시스템학회논문지
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• 제22권1호
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• pp.40-45
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• 2016

This paper presents the neuro-fuzzy control method for balancing a two-wheel mobile robot. A two-wheel mobile robot is built for the experimental studies. On-line learning algorithm based on the back-propagation(BP) method is derived for the Takagi-Sugeno(T-S) neuro-fuzzy controller. The modified error is proposed to learn the B-P algorithm for the balancing control of a two-wheel mobile robot. The T-S controller is implemented on a DSP chip. Experimental studies of the balancing control performance are conducted. Balancing control performances with disturbance are also conducted and results are evaluated.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2286-2288
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• 2001

In this paper, a drive system of induction motor with high performance is realized on the viewpoint of the design and experiment, using the DSP (TMS320F240). The speed controller for induction motor drive system is designed on the basis of a neuro-fuzzy network. The neuro-fuzzy controller acts as a feed-forward controller that provides the right control input for the plant and accomplishes error back-propagation algorithm through the network. The proposed network is used to achieve the high speedy calculation of the space vector PWM (Pulse Width Modulation) and to build the neuro-fuzzy control algorithm, for the real-time control. The proposed neuro-fuzzy algorithm on the basis of DSP shows that experimental results have good performance for the precise speed control of an induction motor drive system. It is confirmed that the proposed controller could provide more improved control performance than conventional v/f vector controllers through the experiment.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권2호
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• pp.48-54
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• 2002

In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules are created by an expert. To adapt the more precise model is implemented by error back-propagation learning algorithm to adjust the link-weight of fuzzy membership function in the neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of the proposed algorithm designed above, an operating characteristic of a DC servo motor with variable load is investigated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.710-717
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• 1993

In this paper, we focus upon the design and applications of adaptive fuzzy-neuro controllers. An intelligent control system is proposed by exploiting the merits of two paradigms, a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to update the fuzzy control rules on-line with the output error. And, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.493-496
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• 2000

In this paper, an adaptive neuro-fuzzy controller using the conditional fuzzy c-means(CFCM) methods is proposed. Usually, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Finally, we applied the proposed method to the water path temperature control system and obtained a better performance than previous works.

• 전력전자학회논문지
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• 제16권5호
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• pp.484-493
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• 2011

본 논문은 뉴로-퍼지 제어기를 이용한 최적의 퍼지 소속함수에 동조하는 다층 신경회로망을 사용한 성능이 개선된 MPPT 알고리즘을 제안한다. 퍼지 제어기의 성능은 퍼지규칙과 퍼지 소속함수의 폭의 영향을 받는다. 뉴로-퍼지 제어기는 신경망 학습을 통해 퍼지 소속함수의 최적 폭을 이용하기 때문에 기존 퍼지 제어기보다 우수한 응답특성을 갖는다. 실험과 시뮬레이션 결과를 통해 제안된 알고리즘의 우수한 제어특성을 확인한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.367-367
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian membership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• Journal of Advanced Marine Engineering and Technology
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• 제24권2호
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• pp.72-81
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• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.

• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1439-1450
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• 2013

This paper investigates a robust neuro-fuzzy control (NFC) method which can accurately follow the speed reference of an interior permanent magnet synchronous motor (IPMSM) in the existence of nonlinearities and system uncertainties. A neuro-fuzzy control term is proposed to estimate these nonlinear and uncertain factors, therefore, this difficulty is completely solved. To make the global stability analysis simple and systematic, the time derivative of the quadratic Lyapunov function is selected as the cost function to be minimized. Moreover, the design procedure of the online self-tuning algorithm is comparatively simplified to reduce a computational burden of the NFC. Next, a rotor angular acceleration is obtained through the disturbance observer. The proposed observer-based NFC strategy can achieve better control performance (i.e., less steady-state error, less sensitivity) than the feedback linearization control method even when there exist some uncertainties in the electrical and mechanical parameters. Finally, the validity of the proposed neuro-fuzzy speed controller is confirmed through simulation and experimental studies on a prototype IPMSM drive system with a TMS320F28335 DSP.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.157-157
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• 2000

In the fuzzy control for the multi-variable system, it is difficult to obtain the fuzzy rule. Therefore, the parallel structure of the independent single input-single output fuzzy controller using a pairing between the input and output variable is applied to the multi-variable system. The concept of relative gain matrix is used to obtain the input-output pairs. However, among the input/output variables which are not paired the interactive effects should be taken into account. these mutual coupling of variables affect the control performance. Therefore, for the control system with a strong coupling property, the control performance is sometimes lowered. In this paper, the effect of mutual coupling of variables is considered by tile introduction of a simple compensator. This compensator adjusts the degree of coupling between variables using a neural network. In this proposed neuro-fuzzy controller, the Neural network which is realized by back-propagation algorithm, adjusts the mutual coupling weight between variables.