• Nuclear Engineering and Technology
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• 제54권3호
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• pp.940-947
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• 2022

Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization (HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry's law of physics. To evaluate the performance of a new algorithm, it must be used in various problems. On the other hand, the optimization of the proportional-integral-derivative (PID) gains for load-following of a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, the power control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model with six groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms, an efficient objective function is required. Therefore, the integral of the time-weighted square error (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrained by a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. The results show that this method provides superior results compared to an empirically tuned PID controller with the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.

• International Journal of Control, Automation, and Systems
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• 제5권6호
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• pp.621-629
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• 2007

In the adaptive fuzzy control field for affine nonlinear systems, there are two basic configurations: direct and indirect. It is well known that the direct configuration needs more restrictions on the control gain than the indirect configuration. In general, these restrictions are difficult to check in practice where mathematical models of plant are not available. In this paper, using a simple extension of the universal approximation theorem, we show that the only required constraint on the control gain is that its sign is known. The Lyapunov synthesis approach is used to guarantee the stability and convergence of the closed loop system. Finally, examples of an inverted pendulum and a magnet levitation system demonstrate the theoretical results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.2085-2086
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• 2006

This paper addresses the problem of stability analysis and control synthesis of a digital fuzzy control systems. The authors shows that the stability properties (in the Lyapunov sense) of a digital fuzzy control system can be deduced from the stability properties of the its approximate discretization in the sufficiently small sampling time.

• 전자공학회논문지SC
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• 제37권6호
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• pp.7-14
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• 2000

본 논문에서는 부 시스템간의 상호 연결 시 시간지연을 갖는 이산시간영역의 섭동을 갖는 대규모 시스템의 강인 안정화를 위한 분산 제어기를 설계한다. 안정화를 도모하기 위하여 상태 궤환 제어기를 이용하였으며, 이러한 제어기의 존재를 보장하는 충분조건을 리아프노프 안정성 해석법을 이용하여 선형행렬 부등식으로 표현하였다. 이 부등식의 해는 다양한 최적화 알고리즘을 이용하여 쉽게 찾을 수 있으며, 이 부등식의 해로부터 제어기의 게인 행렬도 쉽게 구할 수 있다. 제안된 방법을 예제를 통하여 살펴보았다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.724-727
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• 1999

In this paper, the direct and indirect neural adaptive controller are combined based on the Lyapunov synthesis approach. The proposed adaptive controller is constructed from RBF neural network and a set of fuzzy IF-THEN rules. And the weighting parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. In this scheme, fuzzy IF-THEN rules are used to decide the combined weighting factor. It is shown that all the signals in the closed-loop system are uniformly bounded under mild assumptions. The effectiveness of the proposed control scheme is demonstrated through the control of one-link rigid robotics manipulator.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.519-521
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• 1999

An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. Adaptive fuzzy controller of this paper is designed based on the Lyapunov synthesis approach The adaptive fuzzy controller is designed through the following steps: first, construct an initial controller based on linguistic descriptions(in the form of fuzzy IF-THEN rules) about the unknown plant from human experts; then, develop an adaptation law to adjust the parameters of the fuzzy controller on-line, the adaptive fuzzy controllers are used to control the inverted pendulum to track a given trajectory.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.58-62
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• 1988

Recently many adaptive control schemes for the industrial robot manipulator have been developed. Especially, learning control utilizing the repetitive motion of robot and based on iterative signal synthesis attracts much interests. However, since most of these approaches excludes the boundness of the input torque supplied to the manipulator, its effectiveness may be limited and also the full dynamic capacity of the robot manipulator can not be utilized. To overcome the above-mentioned difficulties and meet the desired performance, we propose an approach which yields the effective learning control schemes in this paper. In this study, some stability conditions derived from applying the Lyapunov theory to the discrete linear time-varying dynamic system are established and also an optimization scheme considering the bounded input torque is introduced. These results are simulated on a digital computer using a three-joint revolute manipulator to show their effectiveness.

• 제어로봇시스템학회논문지
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• 제20권3호
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• pp.289-297
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• 2014

This article surveys the control theoretic study on time delay systems. Since time delay systems are infinite dimensional, there are not analytic but numerical solutions on almost analysis and synthesis problems, which implies that there are a tremendous number of approximated solutions. To show how to find such solutions, several results are summarized in terms of two different axes: 1) theoretic tools like integral inequality associated with the derivative of delay terms, Jensen inequality, lower bound lemma for reciprocal convexity, and Wirtinger-based inequality and 2) various candidates for Laypunov-Krasovskii functionals.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.418-421
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• 1997

A control synthesis scheme is presented for nonlinear single-input-single-output (SISO) systems with parametric uncertainty which have completely unstable zero dynamics. The approach involves the derivation of an input-output linearizing control law which achieves internal stability for a nonlinear minimum phase approximation to the original system using Fliess normal form. A vector of unknown constant parameters is also considered. A Lyapunov-based additional control law is shown to stabilize the full system.

• KIEE International Transaction on Systems and Control
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• 제2D권2호
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.