• Journal of Power Electronics
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• v.16 no.6
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• pp.2294-2305
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• 2016

The introduction of a high-voltage direct-current (HVDC) system based on a modular multilevel converter (MMC) for wind farm integration has stimulated studies on methods to control this type of converter. This research article focuses on the control of the AC voltage and circulating current for a wind-farm-side MMC (WFS-MMC). After theoretical analysis, emotional learning (EL) controllers are proposed for the controls. The EL controllers are derived from the learning mechanisms of the amygdala and orbitofrontal cortex which make the WFS-MMC insensitive to variance in system parameters, power change, and fault in the grid. The d-axis and q-axis currents are respectively considered for the d-axis and q-axis voltage controls to improve the performance of AC voltage control. The practicability of the proposed control is verified under various conditions with a point-to-point MMC-HVDC system. Simulation results show that the proposed method is superior to the traditional proportional-integral controller.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1705-1714
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• 2018

This paper develops a novel controller called iterative learning sliding mode (ILSM) to control linear and nonlinear fractional-order systems. This control applies a combination structures of continuous and discontinuous controller, conducts the system output to the desired output and achieve better control performance. This controller is designed in the way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order systems. The proposed controller unlike the conventional iterative learning control for fractional systems does not need to apply direct control input to output of the system. It is shown that the controller perform well in partial and complete observable conditions. Simulation results demonstrate very good performance of the iterative learning sliding mode controller for achieving the desired control objective by increasing the number of iterations in the control loop.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.443-446
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• 2003

Conventional disturbance rejection methods have to derive the inverse model of a system. However, the inverse model of n nonholonomic system is not unique, because an inverse it changes depending on initial conditions and desired values. A kind of internal model control (IMC) using feedback error learning is discussed for the motion control of nonholonomic mobile robots in this paper, The present method is different from a conventional IMC whose control system consists of an inverse model, a direct model and a filter. The present disturbance rejection method need not use a direct model, where the remaining two elements are composed of the same inverse model based on neural networks.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.186-189
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• 1990

The automization of manufacturing lines may be accomplished by replacing the human operator with computer system. This paper describes an idea to fully automize the razor qrinding process. Now, in this system, to control the process, human operator must estimate the qrinded states and control the grinding machine continuously. We propose two methods to automize this process by using CMAC memory. One is about learning expert-rules without direct communication with operator. And the other is complete self-learning method based on CMAC's learning algorithm. These ideas may be applied for another manufacturing processes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.370-374
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• 1999

The proposed algorithm used the Hybrid teaming rule in the input and hidden layer, and Back-Propagation teaming rule in the hidden and output layer. From the results of simulation of tracking control with one link manipulator as a plant, we verify the usefulness of the proposed control method to compare with common direct adaptive neural network control method; proposed hybrid teaming rule showed faster loaming time faster settling time than the direct adaptive neural network using Back-propagation algorithm. Usefulness of the proposed control method is that it is faster the learning time and settling time than common direct adaptive neural network control method.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.3
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• pp.284-292
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• 1997

This paper is concerned with position control of direct drive robots. The proposed algorithm consists of the feedback controller and neural networks. Mter the completion of learning, the output of the feedback controller is nearly equal to zero, and the neural networks play an important role in the control system. Therefore, the optimum retuning of control parameters is unnecessary. In other words, the proposed algorithm does not need any knowledge of the con¬trolled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the position control of a parallelogram link-type direct drive robot.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.4
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• pp.404-413
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• 1997

Direct-drive robots are suitable to position and force control with high accuracy, but it is difficult to design a controller which gives satisfactory perfonnance because of the system's nonlinearity and link-interactions. This paper is concerned with the force control of direct-drive robots. The pro¬posed algorithm consists of feedback controllers and a neural network. Mter the completion of learning, the outputs of feedback controllers are nearly equal to zero, and the neural network con¬troller plays an important role in the control system. Therefore, the optimum adjustment of parameters of feedback controllers is unnecessary. In other words, the proposed algorithm does not need any knowledge of the controlled system in advance. The effectiveness of the proposed algo¬rithm is demonstrated by the experiment on the force control of a parallelogram link-type direct¬drive robot.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1173-1177
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• 1990

Cerebellar Model Linear Associator Net(CMLAN), a kind of neuro-net based adaptive control function generator, was applied to the problem of direct inverse calibration of three and six d.o.f. POMA 560 robot. Since CMLAN autonomously maps and generalizes a desired system function via learning on the sampled input/output pair nodes, CMLAN allows no knowledge in system modeling and other error sources. The CMLAN based direct inverse calibration avoids the complex procedure of identifying various system parameters such as geometric(kinematic) or nongeometric(dynamic) ones and generates the corresponding desired compensated joint commands directly to each joint for given target commands in the world coordinate. The generated net outputs automatically handles the effect of unknown system parameters and dynamic error sources. On-line sequential learning on the prespecified sampled nodes requires only the measurement of the corresponding tool tip locations for three d.o.f. manipulator but location and orientation for six d.o.f. manipulator. The proposed calibration procedure can be applied to any robot.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.640-645
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• 2004

This paper proposes a direct adaptive control method using self-recurrent wavelet neural network (SRWNN) for stable path tracking of mobile robots. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). Unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. For this ability of the SRWNN, the SRWNN is used as a controller with simpler structure than the WNN in our on-line control process. The gradient-descent method with adaptive learning rates (ALR) is applied to train the parameters of the SRWNN. The ALR are derived from discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.520-522
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• 1996

The Direct Drive Arm(DDA) is a SCARA typed direct drive manipulator with two degrees-of-freedom(DOF) using the direct drive motor of the NSK company. A controller system for the SCARA robot of DDA is designed using a DSP (TMS32Oc3O), which has the highest performance among the third DSP chips in the TI company. The design objective of the system is to implement dynamic control algorithms and neural control algorithms for real time learning which require a lot of calculations and large memory and have been tested only by simulations so far. The controller uses a DSP, a high speed D/A, 32-bit Counter and a large DRAM to implement advanced robot control algorithms.