• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.12-15
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• 1988

A controller described in this paper is designed for implicit generalised minimum varience controller with variable time delays in which the weighting polynominals are calculated to reduce the output and control signals variances. This paper is based on the fact that the pole-assigment equation may have multiple solutions if the weighting polynominals are not of minimal order. It is shown that the larger order of the weighting polynominals increment the better is the stochastic behavior of the closed-loop system with variable time delays without changs in the deterministic behavior of the system. Based on this theory, the controller is applied to position control of a three-link manipulater with parameter uncertainty.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.491-495
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• 2016

We deal with the regulation problem of nonlinear systems with time-varying delays in both the states and input. A new state feedback controller with dynamic gains is developed based on matrix inequality and non-predictor methods. The proposed control scheme is analyzed using the Razumikhin theorem, and its effectiveness is demonstrated with simulation results.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.61-76
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• 2002

The robust non-fragile guaranteed cost control problem is studied in this paper for class of uncertain linear large-scale systems with time-varying delays in subsystem interconnections and given quadratic cost functions. The uncertainty in the system is assumed to be norm-hounded arid time-varying. Also, the state-feedback gains for subsystems of the large-scale system are assumed to have norm-bounded controller gain variations. The problem is to design state feedback control laws such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound far all admissible uncertainties. Sufficient conditions for the existence of such controllers are derived based on the linear matrix inequality (LMI) approach combined with the Lyapunov method. A parameterized characterization of the robust non-fragile guaranteed cost contrellers is 7iven in terms of the feasible solution to a certain LMI. Finally, in order to show the application of the proposed method, a numerical example is included.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.139-143
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• 1987

In this paper, the stability margins of LQ regulators for the systems with delays in both state and control are analyzed and represented explicitly in terms of system parameters when the systems are open-loop stable. And, the LQG/LTR method is considered as a robust control design method. The results in this paper generalize the well-known ones for ordinary systems.

• Advances in aircraft and spacecraft science
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• v.9 no.6
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• pp.553-569
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• 2022

In this article, we propose a criterion for ensuring the asymptotic stability of large multiple delays, based on the direct Lyapunov method. Based on this criterion and distributed control scheme, the controllers are synthesized by the PDC to stabilize these large-scale systems with multiple delays. And we focus on the results which shows the high effective by the proposed theory utilized for damage propagation for aircraft structural analysis of composite materials. Finally, the numerical simulations confirmed the effectiveness of the method.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.336-340
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• 2001

In a digital system, there are inevitable delays in calculations and applying the inverter output voltages to the motor terminals. Because of the delays, the conventional predictive current controller implemented in the digital system has large overshoot and large harmonics. A new predictive current controller, considering the delays, for a permanent magnet synchronous motor (PMSM) is presented. The effectiveness and feasibilities are shown by experimental results.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1147-1167
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• 2015

Wireless smart sensors, which have become popular for monitoring applications, are an attractive option for implementing structural control systems, due to their onboard sensing, processing, and communication capabilities. However, wireless smart sensors pose inherent challenges for control, including delays from communication, acquisition hardware, and processing time. Previous research in wireless control, which focused on semi-active systems, has found that sampling rate along with time delays can significantly impact control performance. However, because semi-active systems are guaranteed stable, these issues are typically neglected in the control design. This work achieves active control with smart sensors in an experimental setting. Because active systems are not inherently stable, all the elements of the control loop must be addressed, including data acquisition hardware, processing performance, and control design at slow sampling rates. The sensing hardware is shown to have a significant impact on the control design and performance. Ultimately, the smart sensor active control system achieves comparable performance to the traditional tethered system.

• Industrial Engineering and Management Systems
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• v.9 no.3
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• pp.262-274
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• 2010

This article investigates how to adaptively predict the time-varying metrology delay that could realistically occur in the semiconductor manufacturing practice. Metrology delays pose a great challenge for the existing run-to-run (R2R) controllers, driving the process output significantly away from target if not adequately predicted. First, the expected asymptotic double exponentially weighted moving average (DEWMA) control output, by using the EWMA and recursive least squares (RLS) prediction methods, is derived. It has been found that the relationships between the expected control output and target in both estimation methods are parallel, and six cases are addressed. Within the context of time-varying metrology delay, this paper presents a modified recursive least squares-linear trend (RLS-LT) controller, in combination with runs test. Simulated single input-single output (SISO) R2R processes subject to various time-varying metrology delay scenarios are used as a testbed to evaluate the proposed algorithms. The simulation results indicate that the modified RLS-LT controller can yield the process output more accurately on target with smaller mean squared error (MSE) than the original RLSLT controller that only deals with constant metrology delays.

• Smart Structures and Systems
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• v.31 no.1
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• pp.69-78
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• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.