• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2522-2534
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• 2020

It is well known that the model of nuclear reactors features natural nonlinearity, and variable parameters during power tracking operation. In this paper, a disturbance observer-based adaptive sliding mode control (DOB-ASMC) strategy is proposed for power tracking of the pressurized-water reactor (PWR) in the presence of lumped disturbances. The nuclear reactor model is firstly established based on point-reactor kinetics equations with six delayed neutron groups. Then, a new sliding mode disturbance observer is designed to estimate the lumped disturbance, and its stability is discussed. On the basis of the developed DOB, an adaptive sliding mode control scheme is proposed, which is a combination of backstepping technique and integral sliding mode control approach. In addition, an adaptive law is introduced to enhance the robustness of a PWR with disturbances. The asymptotic stability of the overall control system is verified by Lyapunov stability theory. Simulation results are provided to demonstrate that the proposed DOB-ASMC strategy has better power tracking performance than conventional sliding mode controller and PID control method as well as conventional backstepping controller.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.444-452
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• 2003

An integration system with multi-measurement sets can be realized via combined application of a centralized and federated Kalman filter. It is difficult for the centralized Kalman filter to remove a failed sensor in comparison with the federated Kalman filter. All varieties of Kalman filters monitor innovation sequence (residual) for detection and isolation of a failed sensor. The innovation sequence, which is selected as an indicator of real time estimation error plays an important role in adaptive mechanism design. In this study, the centralized Kalman filter with adaptive measurement fusion is introduced by means of innovation sequence. The objectives of adaptive measurement fusion are automatic isolation and recovery of some sensor failures as well as inherent monitoring capability. The proposed adaptive filter is applied to the GPS/SDINS integration system with an additional sensor. Simulation studies attest that the proposed adaptive scheme is effective for isolation and recovery of immediate sensor failures.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.838-846
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• 2006

The purpose of this study to develop the air-conditioning system that adopts adaptive model as an indoor climate control logic for energy saving. The adaptive model using the ability of human thermal adaptation could be expected to alleviate the indoor set-point temperature compared with the past heat-balance model. Especially, in case of hybrid air-conditioning system coupled with natural ventilation and heating/cooling system, the adaptive model can be describe the thermal comfort of inhabitant who stay at hybrid system controlled buildings with accuracy. In this paper, the concept of adaptive model will be described and the results of a continuous measurement on the actual thermal experiences and behaviors of thermal adaptation for office worker will be reported.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.178-182
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• 1995

In this paper, the fuzzy approximator and nonlinear inversion control scheme are considered. An adaptive nonlinear control is proposed based on the speed gradient algorithms proposed by Fradkov. This proposed control scheme is that three types of adaptive law is utilized to approximate the unknown function f by fuzzy logic system in designing the nonlinear inversion controller for the nonlinear system. In order to reduce the approximation errors, the differences of nonlinear function and fuzzy approximator, another three types of adaptive law is also introduced and the stability of proposed control scheme are proven with SG algorithm.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.903-905
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• 2003

In this paper, adaptive high-order neural network controller(AHONNC) is adopted to control of an induction servomotor. A algorithm is developed by combining compensation control and high-order neural networks. Moreover, an adaptive bound estimation algorithm was proposed to estimate the bound of approximation error. The weight of the high-order neural network can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the closed-loop system can be guaranteed. Simulation results for induction servomotor drive system are shown to confirm the validity of the proposed controller.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.428-437
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• 2006

Many physical systems are hybrid in the sense that they have continuous behaviors and discrete phenomena. In control system with multiple models, switching strategy and stability of the closed-loop system under switching are very important issues. In this paper, a novel adaptive control scheme based on multiple parameter models is proposed to cope with a change in Parameters. Switching strategy guarantees the non-increase in the global control Lyapunov function if the estimation of Lyapunov function value converges. Least-square estimation is used to find the estimated value of the Lyapunov function. Switching and adaptation law guarantees the stability of closed-loop system in the sense of Lyapunov. Simulation results on anti-lock brake system are shown to verify the effectiveness of the proposed controller in view of a large change in system parameters.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2187-2189
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• 2003

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of chaotic systems. The conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on a direct adaptive control method is proposed to control chaotic systems whose mathematical models are not available. The gradient-descent method is used for training a wavelet neural network controller. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic system.

• Journal of Drive and Control
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• v.14 no.2
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.217-224
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• 2005

Generally, the underwater flight vehicle (UFV) depth control system operates with the following problems: it is a multi-input multi-output (MIMO) system because the UFV contains both pitch and depth angle variables as well as multiple control planes, it requires robustness because of the possibility that it may encounter uncertainties such as parameter variations and disturbances, it requires a continuous control input because the system that has reduced power consumption and acoustic noise is more practical, and further, it has the speed dependency of controller parameters because the control forces of control planes depend on the operating speed. To solve these problems, an adaptive fuzzy sliding mode controller (AFSMC), which is based on the decomposition method using expert knowledge in the UFV depth control and utilizes a fuzzy basis function expansion (FBFE) and a proportional integral augmented sliding signal, is proposed. To verify the performance of the AFSMC, UFV depth control is performed. Simulation results show that the AFSMC solves all problems experienced in the UFV depth control system online.

• International Journal of Control, Automation, and Systems
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• v.3 no.spc2
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• pp.278-287
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• 2005

In this paper a new excitation control scheme that improves the transient stability of multi machine power systems is proposed. To this end the backstepping technique is used to transform the system to a suitable partially linear form. On this system, a combination of both feedback linearization and adaptive control techniques are used to confront the nonlinearities. As shown in the paper, the resulting nonlinear control law ensures the uniform boundedness of all the state and estimated variables. Furthermore, it is proven that all the error variables are uniformly ultimately bounded (DUB) i.e. they converge to arbitrarily selected small regions around zero in finite-time. Simulation tests on a two generator infinite bus power system demonstrate the effectiveness of the proposed control.