• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.15-22
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• 2004

Some recent advances in stability and optimality for the nonlinear receding horizon control (NRHC) or the nonlinear model predictive control (NMPC) are assessed. The NRHCs with terminal conditions are surveyed in terms of a terminal state equality constraint, a terminal cost, and a terminal constraint set. Other NRHCs without terminal conditions are surveyed in terms of a control Lyapunov function (CLF) and cost monotonicity. Additional approaches such as output feedback, fuzzy, and neural network are introduced. This paper excludes the results for linear receding horizon controls and concentrates only on the analytical results of NRHCs, not including applications of NRHCs. Stability and optimality are focused on rather than robustness.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.731-736
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• 1993

In this paper, a predictive fuzzy control algorithm to supervise the elevator system with plural elevator cars is proposed and its performance is evaluated. Elevator group controller must decide the service elevator for a registered hall call to satisfy the multiple control objects. The proposed group control algorithm ensures the efficient operations of the group cars and provides the improved level of service, coping with multiple control objects and uncertainty of system state. The feasibility of the proposed control algorithm is evaluated by simulation on computer.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.857-862
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• 1992

An elevator group supervisory control logic is investigated to supervise multiple elevators, ensuring their efficient operations. In this paper, a predictive fuzzy control logic of group elevator system is developed for coping with multiple control objects and uncertainty of system state. Simulation of this control logic shows considerable improvements of system performance by the reduction of average waiting time and long wait rate.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.4-172
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• 2001

In this paper, the Generalized Predictive Control(GPC) method based on Fuzzy Neural Networks(FNNs) is presented for the control of chaotic nonlinear systems without precise mathematical models. In our method, FNNs is used as the predictor whose parameters are tuned by the error between the actual output of nonlinear chaotic system and that of FNNs model. The parameters of GPC controller are adjusted via the gradient descent method where the difference between the actual output and the reference signal is used as a control error. Finally, computer simulation on the representative continuous-time chaotic system(Duffing system) is presented to demonstrate the effectiveness of our chaos control method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1374-1377
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• 1993

In this paper, a predictive fuzzy control algorithm to supervise the elevator system with plural elevator cars is developed and its performance is evaluated. Elevator group controller must decide which of the cars is suitable for responding the registered hall call and allocate it to the selected car controller. In most cases, the purpose of group control is to minimize waiting time of passengers and occurrence of long wait as much as possible. The proposed algorithm ensures the efficient operations of the group cars and provides the improved level of service, coping with multiple control objects and uncertainty of system state. The feasibility of the proposed control algorithm is evaluated by graphic simulator on computer.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.765-770
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• 2005

This paper presents a new intelligent digital redesign (IDR) method via the compensated bilinear transformation to design the digital controller such that the digital fuzzy system is equivalent to the analog fuzzy system in the sense of the state-matching. This paper especially consider a multirate control scheme with a predictive feature, where the digital control input is held constant N times between the sampling points. More precisely, the multirate control scheme is proposed that utilizes a numerical integration scheme to approximately predict the current state from the state measured at the sampling points, the delayed measurements. For this system, the IDR conditions incorporated with stabilizability in the format of the linear matrix inequalities (LMIs) are derived. The superiority of the proposed technique is convincingly visualized through a numerical example.

• Ocean Systems Engineering
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• v.14 no.1
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• pp.73-84
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• 2024

In order to achieve the best performance, the automatic control with advanced technology is made of sheathed steel to withstand a wide range of wave loads. This model shows how to control the vibration of the fiber panel as a solution using the new results from the Lyapunov stability question, a modification of the bat that making it easy to calculate and easy to use. It is used to reduce the storage space required in this system. The results show that the planned worker can compensate effectively for the unplanned delay. The results show that the proposed controller can compensate for delays and errors. Fuzzy control (predictive control) demonstrated the external vibration can be reduced.

• Journal of Power Electronics
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• v.12 no.5
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• pp.800-812
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• 2012

This paper analyses the mathematical model and control strategies of a Hybrid Active Power Filter with Injection Circuit (IHAPF). The control strategy based on the load harmonic current detection is selected. A novel control method for a IHAPF, which is based on the analyzed control mathematical model, is proposed. It consists of two closed-control loops. The upper closed-control loop consists of a single fuzzy logic controller and the IHAPF model, while the lower closed-control loop is composed of an Adaptive Network based Fuzzy Inference System (ANFIS) controller, a Neural Generalized Predictive (NGP) regulator and the IHAPF model. The purpose of the lower closed-control loop is to improve the performance of the upper closed-control loop. When compared to other control methods, the simulation and experimental results show that the proposed control method has the advantages of a shorter response time, good online control and very effective harmonics reduction.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.583-586
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• 2003

There are electric four-wheeled vehicles to assist elder people. Because of the vehicles'dynamic characteristic such as impossible to move abeam, it is difficult for these people who has little experience and has little knowledge to drive. Also to judge the future state of dynamic obstacles and to decide how to elude them safely are more difficult. We installed the predictive fuzzy controller(evaluates the future states which several kinds of operation candidates were done and chooses the best one) that modeled humans'algorithms in the system. Human predicts the future states of dynamic obstacles and chooses an operation(wait, steer, go back, etc) to elude safely. To elude dynamic obstacles flexibly, we added expert's knowledge for safe driving to this controller. In this paper, we propose the intelligent soft driving system by the controller that can elude dynamic obstacles safely, and we confirm the effectiveness by a simulation.