• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.280-285
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• 1991

Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, a learning control architecture for the dynamic control of a robot manipulator is developed using inverse dynamic neurocontroller and linear neurocontroher. The inverse dynamic neurocontrouer consists of a MLP (multi-layer perceptron) and the linear neurocontroller consists of SLPs (single layer perceptron). Compared with the previous type of neurocontroller which is using an inverse dynamic neurocontroller and a fixed PD gain controller, proposed architecture shows the superior performance over the previous type of neurocontroller because linear neurocontroller can adapt its gain according to the applied task. This superior performance is tested and verified through the control of PUMA 560. Without any knowledge on the dynamic model, its parameters of a robot , (The robot is treated as a complete black box), the neurocontroller, through practice, gradually and implicitly learns the robot's dynamic properties which is essential for fast and accurate control.

• 한국생산제조학회지
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• 제23권2호
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• pp.157-163
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• 2014

In this paper, a robust MRAC (model reference adaptive control) scheme is applied to control an electrohydraulic positioning system under various loads. The inverse dead-zone compensator in the control system cancels out the dead-zone response, and an integrator added to the controller provides good position-tracking ability. LQG/LTR (linear quadratic Gaussian control with loop transfer recovery) closed-loop model is used as the reference model for learning the MRAC system. LQG/LTR provides a systematic technique to design the linear controller that optimizes the objective function using some compromise between the control effort and the system performance in the frequency domain. Different external load tests are performed to investigate the effectiveness of the designed MRAC system in real time. The experimental results show that the tracking performance of the proposed system is highly accurate, which offers considerable robustness even with a large change in the load.

• Structural Engineering and Mechanics
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• 제72권6호
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• pp.689-712
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• 2019

Time-series models like AR-ARX and ARMAX, provide a robust way to capture the dynamic properties of structures, and their residuals can be effectively used as features for damage detection. Even though several research papers discuss the implementation of AR-ARX and ARMAX models for damage diagnosis, they are basically been exploited so far for detecting the time instant of damage and also the spatial location of the damage. However, the inverse problem associated with damage quantification i.e. extent of damage using time series models is not been reported in the literature. In this paper, an approach to detect the extent of damage by combining the ARMAX model by formulating the inverse problem as a multi-constrained optimization problem and solving using a newly developed hybrid adaptive differential search with dynamic interaction is presented. The proposed variant of the differential search technique employs small multiple populations which perform the search independently and exchange the information with the dynamic neighborhood. The adaptive features and local search ability features are built into the algorithm in order to improve the convergence characteristics and also the overall performance of the technique. The multi-constrained optimization formulations of the inverse problem, associated with damage quantification using time series models, attempted here for the first time, can considerably improve the robustness of the search process. Numerical simulation studies have been carried out by considering three numerical examples to demonstrate the effectiveness of the proposed technique in robustly identifying the extent of the damage. Issues related to modeling errors and also measurement noise are also addressed in this paper.

• 오토저널
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• 제14권5호
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• pp.95-106
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• 1992

Active control of acoustic noise is an application area of adaptive digital signal processing with increasingly interest along the last year. This work studies the implementation of the multichannel LMS filter and the application of this algorithm for the reduction of the noise inside a vechicle cabin using a number of 'secondary sources' drived by adaptive filtering of a reference noise source. Firstly, we propose the use of an adaptive method for the time-varient optimal convergence factor. Secondly, we propose the use of adaptive delayed inverse model to estimate the elastic-acoustic transfer function presented in vechicle cabin. The original, primary source is often periodic, with a known fundamental frequency. A suitably filtered reference signal can thus be used to drive the secondary sources. An algorithm is presented for adapting the coefficients of an FIR filter feeding such a secondary source in such a way as to minimize the output of a suitably placed microphone. In this algorithm, the coefficients of adaptive filter driving an array of secondary sources can be adapted to minimize the sum of the squares of the outputs of a number of error microphones. The multichannel LMS algorithm displays that such an algorithm is considered suitable to used for the global suppression of noise in vehicle cabin.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 추계학술대회 논문집 학회본부
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• pp.397-400
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• 1991

This paper presents a cartesian space decentralized adaptive controller design for the end effector of the robot manipulator to track the given desired trajectory in the cartesian coordinate. By the cartesian based control scheme, the task related high level motion command is directly executed without solving the complex inverse kinematic equations. The controller does not require the complex manipulator dynamic model, and hence it is computationally very efficient. Each degree of freedom of the end effector on the cartesian space is controlled by a PID feedback controller and a velocity acceleration feed forward conpensation part. Simulation results for a two-link direct drive manipulator conform that the present cartesian based decentralized scheme is feasible.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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• pp.27-50
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• 2009

This paper describes an adaptive management approach for predicting, monitoring, and controlling ground movements associated with excavations in urban areas. Successful use of monitoring data to update performance predictions of supported excavations depends equally on reasonable numerical simulations of performance, the type of monitoring data used as observations, and the optimization techniques used to minimize the difference between predictions and observed performance. This paper summarizes each of these factors and emphasizes their inter-dependence. Numerical considerations are described, including the initial stress and boundary conditions, the importance of reasonable representation of the construction process, and factors affecting the selection of the constitutive model. Monitoring data that can be used in conjunction with current numerical capabilities are discussed, including laser scanning and webcams for developing an accurate record of construction activities, and automated and remote instrumentations to measure movements. Self-updating numerical models that have been successfully used to compute anticipated ground movements, update predictions of field observations and to learn from field observations are summarized. Applications of these techniques from case studies are presented to illustrate the capabilities of this approach.

• Journal of Electrical Engineering and Technology
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• 제11권1호
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• pp.215-226
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• 2016

Modeling, control and implementation of a real redundant robot with five Degrees Freedom (DOF) of the SCARA (Selective Compliant Assembly Robot Arm) manipulator type is presented. Through geometric methods and structural and functional considerations, the inverse kinematics for redundant robot can be obtained. By means of a modification of the classical sliding mode control law through a hyperbolic function, we get a new algorithm which enables reducing the chattering effect of the real actuators, which together with the learning and adaptive controllers, is applied to the model and to the real robot. A simulation environment including the actuator dynamics is elaborated. A 5 DOF robot, a communication interface and a signal conditioning circuit are designed and implemented for feedback. Three control laws are executed in: a simulation structure (together with the dynamic model of the SCARA type redundant manipulator and the actuator dynamics) and a real redundant manipulator of the SCARA type carried out using MatLab/Simulink programming tools. The results, obtained through simulation and implementation, were represented by comparative curves and RMS indices of the joint errors, and they showed that the redundant manipulator, both in the simulation and the implementation, followed the test trajectory with less pronounced maximum errors using the adaptive controller than the other controllers, with more homogeneous motions of the manipulator.

• 한국멀티미디어학회논문지
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• 제10권6호
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• pp.716-725
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• 2007

In this paper we describe a motion tracking algorithm for 3D human animation using stereo vision system. This allows us to extract the motion data of the end effectors of human body by following the movement through segmentation process in HIS or RGB color model, and then blob analysis is used to detect robust shape. When two hands or two foots are crossed at any position and become disjointed, an adaptive algorithm is presented to recognize whether it is left or right one. And the real motion is the 3-D coordinate motion. A mono image data is a data of 2D coordinate. This data doesn't acquire distance from a camera. By stereo vision like human vision, we can acquire a data of 3D motion such as left, right motion from bottom and distance of objects from camera. This requests a depth value including x axis and y axis coordinate in mono image for transforming 3D coordinate. This depth value(z axis) is calculated by disparity of stereo vision by using only end-effectors of images. The position of the inner joints is calculated and 3D character can be visualized using inverse kinematics.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1911-1916
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• 2005

In this study, the basic motor control system had been investigated. The Discrete-Time Feedback Error Learning (DTFEL) method is used to control this system. This method is anologous to the original continuous-time version Feedback Error Learning(FEL) control which is proposed as a control model of cerebellum in the field of computational neuroscience. The DTFEL controller consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such the tracking perfect, the adaptive law is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The PD control theory is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Smart Structures and Systems
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• 제31권3호
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• pp.259-273
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• 2023

Real-time hybrid simulation (RTHS) is an effective experimental technique for structural dynamic assessment. However, time delay causes displacement de-synchronization at the interface between the numerical and physical substructures, negatively affecting the accuracy and stability of RTHS. To this end, the authors have proposed a model-based adaptive control strategy with a Kalman filter (MAC-KF). In the proposed method, the time delay is mainly mitigated by a parameterized feedforward controller, which is designed using the discrete inverse model of the control plant and adjusted using the KF based on the displacement command and measurement. A feedback controller is employed to improve the robustness of the controller. The objective of this study is to further validate the power of dealing with a nonlinear control plant and to investigate the potential challenges of the proposed method through actual experiments. In particular, the effect of the order of the feedforward controller on tracking performance was numerically investigated using a nonlinear control plant; a series of actual RTHS of a frame structure equipped with a magnetorheological damper was performed using the proposed method. The findings reveal significant improvement in tracking accuracy, demonstrating that the proposed method effectively suppresses the time delay in RTHS. In addition, the parameters of the control plant are timely updated, indicating that it is feasible to estimate the control plant parameter by KF. The order of the feedforward controller has a limited effect on the control performance of the MAC-KF method, and the feedback controller is beneficial to promote the accuracy of RTHS.