• International Journal of Safety
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• v.5 no.2
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• pp.6-11
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• 2006

The time delay in servo control on LonWorks/IP Virtual Device Network (VDN) is highly stochastic in nature. LonWorks/IP VDN induced time delay deteriorates the performance and stability of the real-time distributed control system and hinders an effective preventive and predictive maintenance. Especially in real-time distributed servo applications on the factory floor, timely response is essential for predictive and preventive maintenance. In order to guarantee the stability and performance of the system for effective preventive and predictive maintenance, LonWorks/IP VDN induced time delay needs to be predicted and compensated for. In this paper position control simulation of DC servo motor using Zero Phase Error Tracking Controller (ZPETC) as a feedforward controller, and Internal Model Controllers (IMC) based on Smith predictor with disturbance observer as a feedback controller is performed. The validity of the proposed control scheme is demonstrated by comparing the IMC based on Smith predictor with disturbance observer.

• Journal of the Korean Society of Safety
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• v.21 no.4 s.76
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• pp.25-32
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• 2006

LonWorks over IP(LonWorks-IP) virtual device network(VDN) is an integrated form of LonWorks device network and IP data network. In especially real-time distributed servo applications on the factory floor, timely response is essential for predictive and preventive maintenance. The time delay in servo control on LonWorks-IP based VDN has highly stochastic nature. LonWorks-IP based VDN induced transmission delay deteriorates the performance and stability of the real-time distributed control system and can't give an effective preventive and predictive maintenance. In order to guarantee the stability and performance of the system, and give an effective preventive and predictive maintenance, LonWorks-IP based VDN induced time-varying uncertain time delay needs to be predicted and compensated. In this paper new Pill control scheme based on Smith predictor, disturbance observer and band pass filter is proposed and tested through computer simulation about position control of DC servo motor. It is shown that how can the proposed control scheme be designed to minimize the effects of uncertain varying time delay and model uncertainties. The validity of the proposed control scheme is compared and demonstrated with the comparison of internal model controllers(IMC) based on Smith predictor with and without disturbance observer.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.186-193
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• 2008

The Internet's low cost and ubiquity present an attractive option for real-time distributed control of processes on the factory floor. When integrated with the Internet, the LonWorks open control network can give ubiquitous accessibility with the distributed control nature of information on the factory floor. One of the most important points in real-time distributed control of processes is timely response. There are many processes on the factory floor that require timely response. However, the uncertain time delay inherent in the network makes it difficult to guarantee timely response in many cases. Especially, the transmission characteristics of the LonWorks/IP network show a highly stochastic nature. Therefore, the time delay problem has to be resolved to achieve high performance and quality of the real-time distributed control of the process in the LonWorks/IP Virtual Device Network (VDN). It should be properly predicted and compensated. In this paper, a new distributed control scheme that can compensate for the effects of the time delay in the network is proposed. It is based on the PID controller augmented with the Smith predictor and disturbance observer. Designing methods for output feedback filter and disturbance observer are also proposed. Tracking position control experiment of a geared DC Servo motor is performed using the proposed control method. The performance of the proposed controller is compared with that of the Internal Model Controller (IMC) with the Smith predictor. The result shows that the performance is improved and guaranteed by augmenting a PID controller with both the Smith predictor and disturbance observer under the stochastic time delay in the LonWorks/IP VDN.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.609-615
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• 2007

We propose an optimized adaptive PID control scheme. This paper is focused on the development of model reduction as well as a new adoptive control structure (viz. a recursive least square estimation (RLSE) method-based structure) that is constructed with smith-predictor structure and a real time estimator. The estimator adjust parameters of a reduced model in real time. It leads to robust and superb control performance for the noise or variation of parameters of process. Experimental study reveals that the proposed control structure exhibits more superb output performance in comparison to some previous methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.449-455
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• 2011

In this paper, a time-delay problem of a tele-operated control system is investigated and compensated by neural network. The smith predictor requires an exact system model to deal with a time-delay in the system. To compensate for modeling errors in the configuration of the Smith predictor, a neural network approach is presented. Based on forming the Smith predictor structure, the radial basis function(RBF) neural network estimator is used. Simulation and experimental studies are conducted to show the functionality of the proposed method.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.675-678
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• 1989

This paper proposes two types of the preview algorithms to predict the joint velocities and joint positions, and deals with a control approach using the preview algorithms for the precise trajectory control. Specifically, a predictor an the form of discrete time state equations is proposed based on the robot dynamics model linearized by the computed torque method. And another state predictor is proposed by the beat line fitting in the least square sense, where present joint velocities and positions and several past positions are employed. Then computer simulations are performed for the SCARA robot with two d.o.f. to show the validities of the proposed algothrithms.

• Journal of Power System Engineering
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• v.21 no.4
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• pp.77-83
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• 2017

The aim of this paper is to solve the chattering and delayed response problems caused by gear backlash. In the gear mechanism based systems, for example, in robot systems, the actuators provide the reduction gear with motors to transfer effectively electric power to mechanical power. Therefore, the gear backlash exists and is an unavoidable fact which makes many undesirable problems. In this paper, the authors try to make a solution for this issue and, introduce several control methods which are PID only, PID with Smith predictor and super-twisting algorithm based SMC(sliding mode control). Each control method is applied to the real plant in which strong backlash is included. By comparison results, it is clear that SMC gives the best control performance with little backlash effects. Also, the usefulness and effectiveness of proposed control method is verified by experiment.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.434-443
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• 1999

In this paper, neural network predictive PID (NNPPID) control system is proposed to reduce the vibration of building structure. NNPPID control system is made up predictor, controller, and self-tuner to yield the parameters of controller. The neural networks predictor forecasts the future output based on present input and output of building structure. The controller is PID type whose parameters are yielded by neural networks self-tuning algorithm. Computer simulations show displacements of single and multi-story structure applied to NNPPID system about disturbance loads-wind forces and earthquakes.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.260-263
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• 2000

A technique that uses linear prediction to achieve noise reduction in a voice signal which has been mixed with an ambient noise (Signal to Noise (S-N) ratio = about 0dB) is proposed. This noise reduction method which is based on the linear prediction estimates the voice spectrum while ignoring the spectrum of the noise. The performance of the noise reduction method is first examined using the transversal linear predictor filter. However, with this method there is deterioration in the tone quality of the predicted voice due to the low level of the S-N ratio. An additional processing circuit is then proposed so as to adjust the noise reduction circuit with an aim of improving the problem of tone deterioration. Next, we consider a practical application where the effects of round on errors arising from fixed-point computation has to be minimized. This minimization is achieved by using the lattice predictor filter which in comparison to the transversal type, is Down to be less sensitive to the round-off error associated with finite word length operations. Finally, we consider a practical application where noise reduction is necessary. In this noise reduction method, both the voice spectrum and the actual noise spectrum are estimated. Noise reduction is achieved by using the linear predictor filter which includes the control of the predictor filter coefficient’s update.