• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1427-1430
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• 2005

In this paper, the range finder system using a PSD sensor suitable for low-cost localization sensor of a mobile robot. Because the distance-voltage output of a PSD sensor has a non-linear property, the linearizing function is proposed through the experimental characteristics of the sensor. And the characteristics are tested and the distance-voltage data are measured in various colors and materials of object. For a known environment, a mobile robot scans the surroundings using a PSD sensor that can rotate $360^{\circ}$. Finally, the performance and accuracy of the developed system are verified according to the comparison the distance by proposed function with real distance

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.668-672
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• 2005

In this paper, a distance measuring system using a PSD sensor in proposed, which in suitable for low-cost localization sensor of a mobile robot. Because the distance-voltage output of PSD sensor has a non-linear property, the linearizing function is proposed through the experimental characteristics of the sensor. And the characteristics are tested and the distance-voltage data are measured in various colors and materials of object. The parameters of the proposed function are extracted by using the measured data. Finally, the performance and the accuracy of the developed system are verified according to the comparison of the distance by the proposed function with the real distance.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.982-990
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• 1994

In this paper, we consider feedback-kinearizing control of brushless dc motors which have been increasingly used in high-performance servo applications, We completely characterize the whole class of the feedback controllers that enable the brushless dc motors to behave like linear systems but without torque ripple. The whole class of the feedback-linearizing controllers is characterized in the explicit form which contains a function to be chosen freely. The previously known controllers correspond to either the particular ones in our whole class of the feedback-Linearzing controllers or their truncated Fourier expansions. This free function can be used to achieve other control objectives as well as linear dynamic characteristics. Furthermore, our feedback-linearizing controllers can be easily determined from the measurement data of back EMF.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.526-531
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• 1997

To control nonlinear chemical processes, a new neural linearizing control scheme is proposed. This is a hybrid of a radial basis function(RBF) network and a linear controller, thus the control action applied to the process is the sum of both control actions. Firstly, to train the RBF newtork a linear reference model is determined by analyzing the past operating data of the process. Then, the training of the RBF newtork is iteratively performed to minimize the difference between outputs of the process and the linear reference model. As a result, the apparent dynamics of the process added by the RBF newtork becomes similar to that of the linear reference model. After training, the original nonlinear control problem changes to a linear one, and the closed-loop control performance is improved by using the optimum tuning parameters of the linear controller for the linear dynamics. The proposed control scheme performs control and training simultaneously, and shows a good control performance for nonlinear chemical processes.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.597-600
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• 1997

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of polynomial compensation. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssections of Volterra kernels of the nonlinear system up to 3rd order. We construct a polynomial compensation function from comparison between lst order Volterra kernel and high order kernels. The polynomial compensation function is, in this case, of third order whose coefficients are variable depending on the amplitude of the input signal. Once we can get compensation function of nonlinear system, we can construct a linearization scheme of the nonlinear system. That is. the effect of second and third order Volterra kernels are subtracted from the output, thus we obtain a sort of linearized output. The authors applied this method to a saturation-type nonlinear system by simulation, and the results show good agreement with the theoretical considerations.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1845-1847
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• 1991

In general, it is not easy to find the linearizing coordinate transformation map for a class of systems which are state equivalent to linear systems, because it is required to solve a set of partial differential equations. It is possible to construct an arbitrary nonlinear function with a backpropagation(BP) net. Utilizing this property of BP neural net, we construct a desired linearizing coordinate transformation map. That is, we implement a unknown coordinate transformation map through the training of neural weights. We have shown an example which supports this idea.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.36-36
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• 2000

The paper presents a speed control algorithm for a full pitch-controlled wind turbine system. Torque of a blade generated by wind energy is non-linear function of a wind speed, angular velocity, and pitch angle of the blade. The design of a cor_troller, in general, is performed by linearizing the torque in the vicinity of a operating point assuming the angular velocity of the blade is constant. For speed control, however, the angular velocity is no longer a constant, so that linearization of the torque in terms of a wind speed and pitch angle is impossible. In this study, a reference pitch model is derived in terms of a wind speed, angular velocity, and pitch angle, which makes it possible to design a controller without linearizing the non-linear torque model of the blade. The validity of the algorithm is demonstrated with the results produced through sets of experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.109-116
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• 2001

The paper presents a speed control algorithm for a full pitch-controlled wind turbine system. Torque of a blade generated by wind energy is a nonlinear function of wind speed, angular velocity, and pitch angle of the blade. The design of the controller, in general, is performed by linearizing the torque in the vicinity of the operating point assuming the angular velocity of the blade is constant. For speed control, however the angular velocity is on longer a constant, so that linearization of the torque in terms of wind speed and pitch angle is impossible. In this study, a reference pitch model is derived in terms of a wind speed, angular velocity, and pitch angle, which makes it possible to design a controller without linearizing the nonlinear torque model of the blade. This paper also suggests a method of designing a hydraulic control system for changing the pitch angle of the blade.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.727-731
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• 2000

This paper presents a new practical autopilot design approach to acceleration control for tail-controlled STT(Skid-to-Turn) missiles. The approach is novel in that the proposed parametric affine missile model adopts acceleration as th controlled output and considers the couplings between the forces as well as the moments and control fin deflections. The aerodynamic coefficients in the proposed model are expressed in a closed form with fittable parameters over the whole operating range. The parameters are fitted from aerodynamic coefficient look-up tables by the function approximation technique which is based on the combination of local parametric models through curve fitting using the corresponding influence functions. In this paper in order to employ the results of parametric affine modeling in the autopilot controller design we derived a parametric affine missile model and designed a feedback linearizing controller for the obtained model. Stability analysis for the overall closed loop sys-tem is provided considering the uncertainties arising from approximation errors. the validity of the proposed modeling and control approach is demonstrated through simulations for an STT missile.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.731-736
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• 1994

Experiment on a lab-scale pH process is carried out to evaluate the control performance of the neural linearizing control scheme(NLCS) using a radial basis function(RBF) network which was previously proposed by Kim and Park. NLCS was developed to overcome the difficulties of the conventional neural controllers which occur when they are applied to chemical processes. Since NLCS is applicable for the processes which are already controlled by a linear controller and of which the past operating data are enough, we first control the pH process with PI controller. Using the operating data with PI controller, the linear reference model is determined by optimization. Then, a IMC controller replaces the PI controller as a feedback controller. NLCS consists of the IMC controller and a RBF network. After the learning of the neural network is fully achieved, the dynamics of the process combined with the neural network becomes linear and close to that of the linear reference model and the control performance of the linear control improves. During the training, NLCS maintains the stability and the control performance of the closed loop system. Experimental results show that the NLCS performs better than PI controller and IMC for both the servo and the regulator problems.