• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.51-57
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• 2022

The problem of optimal stochastic GA control of the system with uncertain parameters and unsure noise covariates is studied. First, without knowing the explicit form of the dynamic system, the open-loop determinism problem with path optimization is solved. Next, Gaussian linear quadratic controllers (LQG) are designed for linear systems that depend on the nominal path. A robust genetic neural network (NN) fuzzy controller is synthesized, which consists of a Kalman filter and an optimal controller to assure the asymptotic stability of the discrete control system. A simulation is performed to prove the suitability and performance of the recommended algorithm. The results indicated that the recommended method is a feasible method to improve the performance of active tuned mass damper (ATMD) shear buildings under random earthquake disturbances.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.62-74
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• 2002

The tracking of a reference temperature trajectory in a polymerization batch reactor is a common problem and has critical importance because the quality control of a batch reactor is usually achieved by implementing the trajectory precisely. In this study, only energy balances around a reactor are considered as a design model for control synthesis, and material balances describing concentration variations of involved components are treated as unknown disturbances, of which the effects appear as time-varying parameters in the design model. For the synthesis of a tracking controller, a method combining the input-output linearization of a time-variant system with the parameter estimation is proposed. The parameter estimation method provides parameter estimates such that the estimated outputs asymptotically follow the measured outputs in a specified way. Since other unknown external disturbances or uncertainties can be lumped into existing parameters or considered as another separate parameters, the method is useful in practices exposed to diverse uncertainties and disturbances, and the designed controller becomes robust. And the design procedure and setting of tuning parameters are simple and clear due to the resulted linear design equations. The performances and the effectiveness of the proposed method are demonstrated via simulation studies.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.675-681
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• 1989

As an adaptive control function generator, the CMAC (Cerebellar Model Arithmetic or Articulated Controller) based learning control has drawn a great attention to realize a rather robust real-time manipulator control under the various uncertainties. There remain, however, inherent problems to be solved in the CMAC application to robot motion control or perception of sensory information. To apply the CMAC to the various unmodeled or modeled systems more efficiently, It is necessary to analyze the effects of the CMAC control parameters an the trained net. Although the CMAC control parameters such as size of the quantizing block, learning gain, input offset, and ranges of input variables play a key role in the learning performance and system memory requirement, these have not been fully investigated yet. These parameters should be determined, of course, considering the shape of the desired function to be trained and learning algorithms applied. In this paper, the interrelation of these parameters with learning performance is investigated under the basic learning schemes presented by authors. Since an analytic approach only seems to be very difficult and even impossible for this purpose, various simulations have been performed with prespecified functions and their results were analyzed. A general step following design guide was set up according to the various simulation results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.122-129
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• 2008

The direct torque control method of an inverter fed squirrel cage typed induction motor using fuzzy logic controller has been proposed. This method is suitable for the traction which requires a fast torque response during the star-up and step change. The fuzzy control algorithm based upon the control principles of conventional DSC(Direct Self Controller) is developed. The fuzzy algorithm is tarried out by defuzzification strategy of the fuzzy output extracted from the possibility distribution of an inferred fuzzy control rule. The flux and torque of an induction motor are estimated by the dynamic model of the rotor flux field-oriented scheme which has decoupling characteristics and excellent dynamic response over a wide speed range. The proposed controller shows a good dynamic response. Moreover, since the fuzzy controller possesses highly adaptive capability, the performance of fuzzy controller is quite robust and insensitive to the motor parameters and change of operation conditions.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.252-261
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• 2013

In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have the characteristics of the conventional PID, but adjust the parameters of PID controller online using identified Jacobian information from RBFNN. Hence, it has strong adaptability to the variation of the system operating condition. The effectiveness of the proposed controller is tested on a two-machine five-bus power system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency oscillations under different operating conditions and is superior to the lead-lag damping controller tuned by EA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.67-69
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• 2007

PMSM (Permanent Magnet Synchronous Motor) current control is a most inner loop of electromechanical driving systems and it plays a foundation role in the hierarchy's control loop of several mechanical machine systems. In this paper, a simple RMRAC control scheme for the PMSM is proposed in the synchronous frame. In the synchronous current model, the input signal is composed of as a calculated voltage by adaptive laws and system disturbances. The gains of feed-forward and feed-back controller are estimated by the proposed e-modification methods respectively, where the disturbances are assumed as filtered current tracking errors. After the estimation of the disturbances from the tracking errors, the corresponding voltage is fed forward to control input to compensate for the disturbances. The proposed method is robust to high frequency disturbances and has a fast dynamic response to time varying reference current trajectory. It also shows a good real-time performance duo to it's simplicity of control structure. Through the simulations considering several cases of external disturbances and experimental results, efficiency of the proposed method is verified

• 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.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1572-1582
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• 2011

The rapid growth of UnderWater Acoustic Sensor Networks (UW-ASNs) has led researchers to enhance underwater MAC protocols against limitations existing in underwater environment. We propose the customized robust real-time packet inspection mechanism with addressing the problem of the search for the data packet loss and network performance quality analysis in UW-ASNs, and describe our experiences using this approach. The goal of this work is to provide a framework to assess the network real-time performance quality. We propose a customized and adaptive mechanism to detect, monitor and analyze the data packets according to the MAC protocol standards in UW-ASNs. The packet analyzing method and software we propose is easy to implement, maintain, update and enhance. We take input stream as real data packets from sniffer node in capture mode and perform fully analysis. We were interested in developing software and hardware designed tool with the same capabilities which almost all terrestrial network packet sniffers have. Experimental results confirm that the best way to achieve maximum performance requires the most adaptive algorithm. In this paper, we present and offer the proposed packet analyzer, which can be effectively used for implementing underwater MAC protocols.

• Journal of KIISE:Information Networking
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• v.30 no.1
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• pp.30-37
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• 2003

In this paper, a discrete wavelet transform (DWT)-based watermarking system is proposed. The main feature of proposed system is that the embedding system uses adaptive thresholds to control the trade-off between the qualify of the watermarked image and the capacity of the watermark, and the trade-off between the quality and robustness of the watermarked image. Also, the extracting system rebuilds threshold according to various attacks and decides a watermark bit from the least distorted coefficient after measuring the distortion of coefficient. Finally, a new measure to detect the uniqueness of watermark is proposed. The experimental result shows that the proposed watermarking system is robust against conventional signal processing and intentional attacks.