• 전자공학회논문지B
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• 제29B권11호
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• pp.31-35
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• 1992

This paper presents a decentralized adaptive controller design for a robot manipulator to track the given desired trajectory in the joint space. The controller is of distributed structure and does not require the complex manipulator dynamic model, thereby it is computationally very efficient. Each joint is independently controlled by a PID feedback part and a velocity-acceleration feedforward part. Simulation results for a two-link direct drive manipulator conform that the proposed decentralized scheme is feasible.

• 한국정밀공학회지
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• 제13권10호
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• pp.94-104
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C31) for robotic manpulators to achieve accurate 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 contorl 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. Morever, this scheme does not require an accurate dynamic modeling nor values of manpipulator parameters and payload. Performance of the adaptive controller is illustated by simulation and experimental results for a SCARA robot.

• 유공압시스템학회논문집
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• 제7권3호
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• pp.1-6
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• 2010

This paper deals with the issue of simple adaptive position control for a pump-controlled cylinder system. A fixed displacement pump is utilized instead of servo valve and its speed is controlled by AC motor. The whole control system is composed of a pair of interconnected subsystems, that is, a feedback control system and a feedforward control system. From experiments it is shown that position control using simple adaptive control can accomplish significant reduction in position tracking error comparing to a conventional PID control.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.161-164
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• 2004

The yo-yo system has been introduced as an interesting plant to demonstrate the effectiveness of intelligent controllers. Having nonlinear and asymmetric characteristics, the yo-yo plant requires a controller quite different from conventional controllers such as PID. In this paper is presented an adaptive method of controlling the yo-yo system. Fuzzy logic controller based on human expertise is referred at first. Then, an adaptive fuzzy controller which has adaptation features against the variation of plant parameters is proposed. Finally, experimental results are presented.

• Journal of Electrical Engineering and Technology
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• 제8권2호
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• pp.252-261
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• 2013

In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have the characteristics of the conventional PID, but adjust the parameters of PID controller online using identified Jacobian information from RBFNN. Hence, it has strong adaptability to the variation of the system operating condition. The effectiveness of the proposed controller is tested on a two-machine five-bus power system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency oscillations under different operating conditions and is superior to the lead-lag damping controller tuned by EA.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.647-651
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• 1987

Yeast biomass in a biological continuous stirred tank reactor was controlled with an APPLE II microcomputer using adaptive control theory of bilinear systems. The controller used is as simple as a PID controller, but required less information. Cell concentration was well controlled by adjusting the inlet flow rate following the algorithm.

• Journal of Advanced Marine Engineering and Technology
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• 제21권4호
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• pp.414-420
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• 1997

An intelligent speed control system for marine diesel engines is presented. The approach adopt¬ed is to use a conventional PID controller for normal operation and a feedforward controller for adaptive control. The feedforward controller is a neural network. The neural network is the inverse dynamics model of the plant, which is being trained on line. The parametric model of the diesel engine is represented in a linear second-order system, with a first-order combustion part and a revolution part each at a normal operating point. The time delay in the control of the com¬bustion part is approximated to the first-order system. The tuned PID parameters are set based on the model for normal operating point. To obtain the inverse dynamics of the diesel engine system, two neural networks are used, one for inverse, the other for forward dynamics. The former is posi¬tioned across the plant to learn its inverse dynamics during operation, and the latter is placed in series with the controlled plant. Simulation results are presented to illustrate the applicability of the proposed scheme to intelligent adaptive control of diesel engines.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.36-41
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• 1993

The adiabatic slirn-type autoclave reactor for free radical polymerization of LDPE is represented by a two-compartment four-cell model, which is proven effective to predict the reactor behavior as well as the polymer properties. Since the temperature distribution along the reactor axis plays the central role for the properties of the polymer product, it is important in practice to regulate the temperature in each compartment. The present study aims for the application of the adaptive control algorithm not only in the period of start-up but also during the steady state operation. It is shown that the temperature control is significantly improved over the conventional PID-control and this also brings about a reduction of variations in the polymer properties. This study demonstrates the potential application of the adaptive controller for the control of the polymerization reactor operated under the adiabatic condition.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.375-378
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• 1991

This paper presents a design method of synchronous generator excitation controller using self-tuning PID algorithm. Controller parameter is determined by using adaptive control theory in order to maintain optimal operation of generator under the various operating conditions. To determine the optimal parameter of controller. minimum variance algorithm using the recursive leastsquare(RLS) indentification method is adopted and the difference between the speed deviation with weighted factor and voltage deviation is used as the input signal of adaptive controller, which provides good damping and conversion characteristics. The results tested on a single machine infinite bus system verify that the proposed controller has better dynamic performances than conventional controller.