• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.79-84
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C31) for robotic manipulators to achieve trajectory tracking by the joint angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.4
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• pp.26-37
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve trajectory tracking by the joint angles Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller. feedback controller. and PID type time-varying auxiliary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require a an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.913-916
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• 1988

Indirect adaptive pole assignment PID controllers for unknown systems with time varying delay, is proposed. Unknown system parameters are estimated by recursive least square method, and time varying delay is estimated using indirect predictors. Since the order of parameter vectors didn't increase, the computational burden is not largely increased in spite of using indirect adaptive control method with time varying delay estimation. Computer simulation is performed to illustrate the efficiency of the proposed method.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.72-74
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• 2005

In this paper, we propose an adaptive PID controller using a cell-mediated immune response to improve a PID control performance. The proposed controller is based on the specific immune response of the biological immune system that is cell-mediated immunity. The immune system of organisms in the real body regulates the antibody and the T-cells to protect an attack from the foreign materials like virus, germ cells, and other antigens. It has similar characteristics that are the adaptation and robustness to overcome disturbances and to control the plant of engineering application. We first build a model of the T-cell regulated immune response mechanism and then designed an I-PID controller focusing on the T-cell regulated immune response of the biological immune system. We apply the proposed methodology to building structures to mitigate vibrations due to strong winds for evaluation of control performances. Through computer simulations, system responses are illustrated and additionally compared to traditional control approaches.

• Smart Structures and Systems
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• v.6 no.1
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• pp.1-16
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• 2010

This paper presents a novel smart MEMS accelerometer which employs a hybrid control algorithm and an estimator. This scheme is realized by adding a sliding-mode controller to a conventional PID closed loop system to achieve higher stability and higher dynamic range and to prevent pull-in phenomena by preventing finger displacement from passing a maximum preset value as well as adding an adaptive nonlinear observer to a conventional PID closed loop system. This estimator is used for online estimation of the parameter variations for MEMS accelerometers and gives the capability of self testing to the system. The analysis of convergence and resolution show that while the proposed control scheme satisfies these criteria it also keeps resolution performance better than what is normally obtained in conventional PID controllers. The performance of the proposed hybrid controller investigated here is validated by computer simulation.

• Journal of Advanced Marine Engineering and Technology
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• v.3 no.1
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• pp.19-31
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• 1979

Recently the adaptive control system, which keeps the control system always optimal by adjusting the control parameters automatically according to the variations of the plant parameters, have become very important in the field of control engineering. The adaptive control systems are usally composed of the plant identification, the decision of the optimal control parameters, and the adjustment of the control parameters. This paper deals with a method of the adaptive control system when PI or PID controller is used in the feed back control system. Its controlled object (the plant) is assumed to be described by the transfer function of $\frac{ke^{-LS}}{1+TS}$ where k, T and L are steady state gain, time constant and pure dead time respectively, and their values are variable in accordance with the change of environmental circumstance. It has been known that a pseudo-random binary signal is quite effective for the measurement of an impulse response of a plant. In adaptive control systems, however, the impulse response itself is not appropriate to determine the control parameters. In this paper, the authors propose a method to estimate directly the parameters of the plant k, T and L by means of the correlation technique using 3 level M-sequence signal as a test signal. The authors also propose a method to determine the optimal parameters of the PI or PID controller in the sense of minimizing the square integral of the control error in the feed back control system, and the values of the optimal parameters are computed numerically for various values of T and L, and the results are examined and compared with those of the conventional methods. Finally the above-mentioned two methods are combined and an algorithm to struct an adaptive control system is suggested. The experiments for the indicial responses by means of both the model of the temperature control system using SCR actuater and the analog simulations have shown good results as expected, and the effectiveness of the proposed method is verified. The M-sequence generator and the time delay circuit, which are manufactured for the experiments, are operated in quite a good condition.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2824-2826
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• 2002

Immune system is an evolutionary biological system to protect innumerable foreign materials such as virus, germ cell, and etcetera. Immune algorithm is the modeling of this systems response that has adaptation and reliability when disturbance occur. In this paper, immune algorithm is proposed to control four wheels steering AGV(Automated Guided Vehicle) in container yard. The adaptive immune system is applied to the PID controller. For design the PID controller using immune algorithm, we tune PID parameters by off-line manner, in order to avoid the damage from abrupt control force. Repeatedly, the PID parameters are adjusted to be accurate by on-line fine tuner of immune algorithm. And then the computer simulation result from the viewpoint of yaw rate and lateral displacement are analyzed and compared with result of conventional PID controller.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.3
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• pp.41-47
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• 2003

In this study an elevator plant model is made with an electro-hydraulic servo valve and a single rod cylinder. A PID controller, a velocity feedback PID controller and a MRAC controller ate designed. Experimental apparatus including an elevator plant model and these controllers are constructed. In case of experiment, external load which is made with a hydraulic cylinder and a pressure control valve burdens varying load to the elevator plant model being driven. With experiment, the control performances of three proposed control methods are compared.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.9
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• pp.646-654
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• 1988

In this paper, a speed controller using a microcomputer is implemented and applied to a DC Servo Motor. Adaptive control is applied to a system for which a priori knowledge to its mathematical model is insufficient, on the basis of input and output data an apropriate controller is constructed through which the system input is synthesized. The pole-placement PID self tuning control algorithms as a control algorithm is used to compare the performance of the controller with that of the classical PID controller through computer simulations and experiments.