• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.11
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• pp.945-954
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• 1990

In this paper, we presents a robust adaptive control system design method in the work coordinate of the robot arm for conveyor tracking. In the design, if the weighting function $L_K$ is smaller than the design parameter then the transient characteristics of system becomes stable, if $L_K$ is larger than then the system becomes unstable. Proposed design method presented here is based on model referenece adaptive control and Popov stability theorem. By the utiliza/tion of an auxilary input, it is improved the transent characteristics of the system in comparison with the conventional model reference adptive control, since the rate of V and V(t) is large. The usefulness of a proposed design method has been confirmed by computer simulations.

• Journal of Aerospace System Engineering
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• v.17 no.5
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• pp.34-41
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• 2023

This paper applies the icing effect and wing rock uncertainty to small unmanned aerial vehicles (UAVs), which have recently attracted attention. Attitude control simulations were performed using various control methods. First, the selected platform, the Skywalker X8 UAV with blended wing-body (BWB) configuration, was linearized for both its baseline form, and a form with applied icing effects. Subsequently, using MATLAB SimulinkⓇ, simulations were conducted for roll and pitch attitude control of the baseline configuration and the configuration with icing effects, employing disturbance observer-based PID control, model reference adaptive control, and model predictive control. Furthermore, the study introduced wing rock uncertainty simultaneously with icing effects on the configured model-a combination not previously explored in existing research-and conducted simulations. The performance of each control Method was compared and analyzed.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.117-123
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• 2000

A new control method for precision robust position control of a PMSM (Permanent Magnet Synchronous Motor) using asymptotically stable adaptive load torque observer is presented in the paper. Precision position control is obtained for the PMSM system approximately linearized using the field-orientation method. Recently, many of these drive systems use the PMSM to avoid backlashes. However, the disadvantages of the motor are high cost and complex control because of nonlinear characteristics. Also, the load torque disturbance directly affects the motor shaft. The application of the load torque observer is published in [1] using fixed gain. However, the motor flux linkage is not exactly known for a load torque observer. There is the problem of uncertainty to obtain very high precision position control. Therefore, a model reference adaptive observer is considered to overcome the problem of unknown parameter and torque disturbance in this paper. The system stability analysis is carried out using Lyapunov stability theorem. As a result, asymptotically stable observer gain can be obtained without affecting the overall system response. The load disturbance detected by the asymptotically stable adaptive observer is compensated by feedforwarding the equivalent current which gives fast response. The experimental results are presented in the paper using DSP TMS320c31.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.65-71
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• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1169-1172
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• 1995

In this paper, the external force control and hybrid force control algorithms are proposed to apply Deburring process. the purpose of adjust which can be implemented to on unknown environments, adaptive control law(MRAC) is adopted. IF a model system is given, the plant system can be controlled on the way which we will introduce to. We showed the validation and the possibility of Deburring process with multi-dimensional force control through experiments. the experimental result show the validity of Deburring in the robot manipulator.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.3
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• pp.53-61
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• 2006

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy nile as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.4
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• pp.78-83
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• 1984

A method is proposed for the adaptive control of linear time varying discrete systems. The stability problem of the model reference adaptive control systems is considered by means of the properties of hypergtability, The hyperstability approach also allows for solutions to the adaptation mechanism. Employing the principles of the continuous time case with output feedback renders it to the discrete case which simplified the system design. The system response is obtained by applying the ramp and step input as a reference signal to the system respectively. With checking the adaptation time for ramp and step input the validity of proposed algorithm was confirmed by the computer simulation.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.77-78
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• 2008

This paper presents to control speed of induction motors with uncertainties. We use an adaptive backstepping controller with fuzzy neural networks(FNNs) and model reference adaptive system(MRAS) at Indirect vector control method. The adaptive backstepping controller using FNNs can control speed of induction motors even we have a minimum of information. And this controller can be used to approximate most of uncertainties which are derived from unknown motor parameters, load torque such as disturbances. MRAS estimates to rotor resistance and also can find optimal flux to minimize power losses of Induction motor. Indirect vector PI current controller is used to keep rotor flux constant without measuring or estimating the rotor flux. Simulation and experiment results are verified the effectiveness of this proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2279-2284
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• 2005

Generally, biped walking is difficult to control because it is a nonlinear system with various uncertainties. In this paper, we design a robust control system based on sliding-mode control (SMC) of 5-link biped robot using the wavelet neural network(WNN), in order to improve the efficiency of position tracking performance of biped locomotion. In our control system, the WNN is utilized to estimate uncertain and nonlinear system parameters, where the weights of WNN are trained by adaptive laws that are induced from the Lyapunov stability theorem. Finally, the effectiveness of the proposed control system is verified by computer simulations.