• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.792-799
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• 2008

Motion estimation is crucial in a remote control for its convenience or accuracy. Time delays, however, can occur in the problem because data communication is required through a network. In this paper, state estimation problem with uncertain time delayed measurements is addressed. In dynamic system with noise, after taking measurements, it often requires some time until that is available in the filter algorithm. Standard filters not considering this time delays cannot be used since the current measurement is related with a past state. These delayed measurements are solved with augmented extended Kalman filter, and the uncertainty of delayed time is also resolved based on an explicit formulation. The proposed method is analyzed and verified by simulations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.10a
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• pp.204-208
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• 2001

Position control of the electro-hydraulic servo indexing system in a flexible forging machine was investigated Flexible forging machine forges an axial type workpiece in the radial direction as well as in the axial direction. The role of the indexing system is to rotate a workpiece fast and accurately to a desired position for continuous shaping. Since the inertia of a workpiece changes during each forging step, a control technique which is robust to inertia variation should be adopted to the position control of the workpiece. In this study, time delayed control technique is applied to the servo system. Time delayed control method does not depend on estimation of specific parameters. Rather, it depends on the direct estimation of a function representing the effect of uncertainties. Direct estimation is accomplished using time delay and the gathered information is used to cancel the unknown dynamics is accomplished using disturbances simultaneously. Experimental result show that the time delayed controller is robust to inertia variation of the load, and satisfactory performance on the sposition accuracy is obtained compared to the contentional feedback control.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.607-614
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• 1999

This paper deals with the design problem of the state estimator for servo system. The servo system has input time delay which depends on the computational time of control algorithm. The delayed input is a factor that brings out the state estimation error. So in order to reduce the state estimation error of the system, we propose a state estimator in which the delayed input of the system is considered. For this purpose, discrete time state space model is established accounting for the delayed input and a state estimator is designed based on this model. Kalman filter algorithm is employed in the design of the state estimator. The proposed estimator is used in the speed control of servo system with delayed input. Performance of the proposed state estimator is exemplified via simulations and experiments for servo system. Also, robustness of the proposed estimator to modeling error by variation of the system parameters is also shown in simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.148-154
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• 2009

In this paper, an embedded Kalman filter for a time-delayed controller is designed on an FPGA to estimate accelerations of the robot arm. When the time-delayed controller is used as a controller, the inertia estimation along with accelerations is needed to form the control law. Although the time-delayed controller is known to be robust to cancel out uncertainties in the nonlinear systems, performances are very much dependent upon estimating the acceleration term ${\ddot{q}}(t-{\lambda})$ along with inertia estimation ${\hat{D}}(t-{\lambda})$. Estimating accelerations using the finite difference method is quite simple, but the accuracy of estimation is poor specially when the robot moves slowly. To estimate accelerations more accurately, various filters such as the least square fit filter and the Kalman filter are introduced and implemented on an FPGA chip. Experimental studies of following the desired trajectory are conducted to show the performance of the controller. Performances of different filters are investigated experimentally and compared.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.117-121
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• 2005

This paper deals with the state estimation of linear time-invariant discrete systems with unknown inputs. The forward sequences of the output are treated as additional outputs. In this case, the rank condition for designing the unknown input estimator is relaxed. The gain for minimal estimation error variance is presented, and a numerical example is given to verify the proposed unknown input estimator.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.537-540
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• 1998

This thesis deals with the design problem of the state estimator for digital servo system. Digital servo system has input time delay, which depends on the size of control algorithm. The delayed input is a factor that brings out the state estimation error. So, in order to reduce this state estimation error of the system, we proposes a state estimator in which the delayed input of the system is considered. At first, a discrete-time state-space model is established accounting for the delayed input. Next, the state estimator is designed based on this model. we employ Kalman filter algorithm in design of the state estimator. The performance of proposed state estimator is exemplified via some simulations and experiment for servo system. And robustness of the proposed estimator to modelling error by variation of the system parameter is also shown in these simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.171-178
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• 2017

This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with $H_{\infty}$ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.307-312
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• 2021

This paper proposes an adaptive time-delayed control approach with the integral sliding-mode surface for the fast convergence rate of robot manipulators. Adaptive switching gain aims to guarantee the system stability in such a way as to suppress time-delayed estimation error in the proposed control approach. Moreover, it makes an effort to increase the convergence ability in reaching the phase. An integral sliding-mode surface is employed to achieve a fast convergence rate in the sliding phase. The stability of the proposed one is proved to be asymptotically stable in the Lyapunov stability. The efficiency of the proposed control approach is illustrated with a tutorial example in robot manipulator, which is compared to that of the existing control approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.23-29
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• 2016

This paper presents balancing control of a two-wheel mobile robot (TWMR). TWMR is aimed to maintain balance while moving. Although TWMR can be controlled by linear controllers such as PD controller, time-delayed controller is employed for robustness. Performances of PD controllers and time-delayed controllers are compared. Especially, experimental studies on different acceleration estimation for the time-delayed controller are conducted. Performances by different acceleration estimations of the balancing angle, of the position, and of both angle and position are compared empirically.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.76-81
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• 1998

This paper presents an on-line scheme for parameter estimation of continuous-time systems, based on the model adjustment technique and the genetic algorithm technique. To deal with the initialisation and unmeasurable signal problems in on-line parameter estimation of continuous-time systems, a discrete-time model is obtained for the linear differential equation model and approximations of unmeasurable states with the observable output and its time-delayed values are obtained for the nonlinear state space model. Noisy observations may affect these approximation processes and degrade the estimation performance. A digital prefilter is therefore incorporated to avoid direct approximations of system derivatives from possible noisy observations. The parameters of both the model and the designed filter are adjusted on-line by a genetic algorithm, A set of simulation works for linear and nonlinear systems is carried out to demonstrate the effectiveness of the proposed method.