• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.64-72
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• 2010

In general, due to the interactions among subsystems, it is difficult to design an decentralized controller for nonlinear interconnected systems. In this study, the model of nonlinear interconnected systems is studied via decentralized fuzzy control method with time delay and polytopic uncertainty. First, the nonlinear interconnected system is represented by an equivalent Takagi-Sugeno type fuzzy model. And the represented model can be rewritten as Parameterized Linear Matrix Inequalities(PLMIs), that is, LMIs whose coefficients are functions of a parameter confined to a compact set. We show that the resulting fuzzy controller guarantees the asymptotic stability and disturbance attenuation of the closed-loop system in spite of controller gain variations within a resulted polytopic region by example and simulations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.49-53
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• 2011

Path following of the mobile robot is one research hot for the mobile robot navigation. For the control system of the wheeled mobile robot(WMR) being in nonhonolomic system and the complex relations among the control parameters, it is difficult to solve the problem based on traditional mathematics model. In this paper, we presents a simple and effective way of implementing an adaptive following controller based on the PID for mobile robot path following. The method uses a non-linear model of mobile robot kinematics and thus allows an accurate prediction of the future trajectories. The proposed controller has a parallel structure that consists of PID controller with a fixed gain. The control law is constructed on the basis of Lyapunov stability theory. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity and orientation tracking control of the nonholonomic WMR. The simulation results of wheel type mobile robot platform are given to show the effectiveness of the proposed algorithm.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.282-287
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• 2003

DTTS(Dynamic Track Tensioning System) system requires robust control performance for the various maneuvering tasks. However, it is very difficult to tune the controller gains in experiments. In this paper, the hydraulic unit is modeled and constructed into the DTTS control module in Matlab/Simulink The control module is interfaced to the vehicle dynamics module so that the control performance of the DTTS system can be evaluated in simulations. The dynamics data and control input data are exchanged between two modules at each control time-step. The gains in the fuzzy-logic controller are varied and the control performance is evaluated in simulations. The proposed simulation tool can be very useful for the gain tuning of track tension controller in bucked vehicles

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.372-374
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• 2001

An automatic operation of train is executed by an automatic train operation(ATO) system. The main function of ATO system is to control train speed as well as train brake system. So that train can stop at its programed position on platform and run with passenger comfort. In this paper, the gain scheduled control is designed for speed control considering the disturbances. The simulation is executed to verify the speed control performance and to compare its performance with the conventional PI and general Fuzzy control.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.201-204
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• 2000

This paper describes a study on the time-varying control of parameters in learning of the neural network. Elman recurrent neural network (RNN) is used to implement the control of parameters. The parameters of learning and momentum rates In the error backpropagation algorithm ate updated at every iteration using fuzzy rules based on performance index. In addition, the gain and slope of the neuron's activation function are also considered time-varying parameters. These function parameters are updated using the gradient descent algorithm. Simulation results show that the auto-tuned learning algorithm results in faster convergence and lower system error than regular backpropagation in the system identification.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.200-207
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• 2015

This paper proposes an active damping control method for a grid-side converter that has an LCL grid filter in the back-to-back converter. To remove the resonant frequency components produced by the LCL filter, it is necessary to measure the grid current. To do this, sensors must be added. However, it is not necessary to add sensors because the grid current is estimated by designing a suboptimal observer. In order to remove the nonlinearity and to gain fast response of control, both feedback linearization and sliding mode control are applied. The proposed method is verified through a simulation.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.29-39
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• 2015

This paper describes a design and implementation of fuzzy-based algorithm for hand-shake state detection and error compensation in the mobile optical image stabilization(OIS) motion detector. Since the gyro sensor output of the OIS motion detector includes inherent error signals, accurate error correction is required for prompt hand-shake error compensation and stable hand-shake state detection. In this research with a little computation overhead of fuzzy-based algorithm, the hand-shake error compensation could be improved by quickly reducing the angle and phase error for the hand-shake frequencies. Further, stability of the OIS system could be enhanced by the hand-shake states of {Halt, Little vibrate, Big vibrate, Pan/Tilt}, classified by subdividing the hand-shake angle. The performance and stability of the proposed algorithm in OIS motion detector is quantitatively and qualitatively evaluated with the emulated hand-shaking of ${\pm}0.5^{\circ}$, ${\pm}0.8^{\circ}$ vibration and 2~12Hz frequency. In experiments, the average error compensation gain of 3.71dB is achieved with respect to the conventional BACF/DCF algorithm; and the four hand-shake states are detected in a stable manner.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.57-66
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper proposes hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme, The validity of the proposed controller is verified by results at different dynamic operating conditions.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.4
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• pp.352-357
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• 2000

The vector control scheme is usually applied to the high performance induction motor drives. The PI controller is adopted traditionally to control the motor speed and currents in the vector control scheme. In this case, the dynamic performance of the induction motor is dependent on the PI gains and the gain optimization is necessary in order to get a good dynamic performance. But, it is very hard to optimize the PI gains uniquely within the speed control range because the equivalent model of the motor control system should be known exactly. In this paper, we propose the hybrid control scheme to remove the defects of PI controller. The hybrid control scheme includes the simplified fuzzy controller which operates in the transient state and the PI controller which operates in the steady state. The proposed scheme is applied to the vector control for induction motor, and the digital simulation and the experimental results are given to verify the proposed scheme.