• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.381-388
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• 2014

In this paper, the attitude of a quadrotor system is controlled by a time-delayed control method which uses the previous information to cancel out uncertainties in the system. Although the linear controller works for the attitude control, the robust performance against disturbance is relatively poor. Therefore, a time-delayed controller as a robust controller is used. Experimental studies are conducted to validate the performance by the time-delayed control method. The performances of both a linear controller and a time-delayed controller are compared.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.649-655
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• 2007

In this paper, a time-delayed controller for position control of humanoid robot arms is designed and implemented on a field programmable gate array(FPGA) chip. The time-delayed control algorithm is simple to implement, and robust to reject disturbances. The time-delayed control method uses the one sample time-delayed previous information to cancel out uncertainties in the system. Since the sampling time is so fast with the current hardware technology, the time-delayed controller can be implemented. However, inertia values should be correctly estimated to have the better performance. The position tracking tasks of humanoid robot arms are tested to compare performances of several control algorithms including the time-delayed controller.

• 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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.463-468
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• 2002

In this paper, we proposed a recurrent time delayed neural network controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed recurrent time delayed neural network controller get a good response compare with a time delayed neural network controller.

• 전기의세계
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• v.30 no.9
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• pp.579-581
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• 1981

A rendezvous method via delayed state feedback is presented for a class of linear systems with time-delays. The property of pointwise degeneracy of delay differential system is utilized and it is shown that the controller is a minimun-time suboptimal controller.

• 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.20 no.7
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• pp.724-729
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• 2014

This paper presents the altitude control of a quadrotor system under the disturbance. The altitude is measured by an ultra sonic sensor attached at the bottom of the quadrotor system and the measured altitude data are used in the time-delayed controller. To test the robustness of the controller, a weight attached to the center of the system is dropped intentionally several times to cause disturbances to the system. Performances of the altitude control by the PID control and time-delayed control method are compared experimentally. Experimental studies are conducted to verify the outperformance of the time-delayed controller for controlling the altitude of the quadrotor system under disturbances.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.570-575
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• 2000

This paper presents a delayed time path planning method of the Autonomous Mobile Robot using fuzzy logic controller for avoidance of obstacles in unknown environment. It is the objective of this paper to develop fuzzy control algorithms using delayed time techniques to deal with moving obstacles randomly. This control method gives the benefit of the collision free movement in real time and optimal path to the pre-settled goal. The computer simulations are demonstrated the effective of the suggested control method in obstacle avoidance.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.19-23
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• 2000

In this paper, we deal with the problem of designing guaranteed cost state feedback controller for the generalized time-varying delay systems with delayed state and control input. The generalized time delay system problems solved on the basis of LMI(linear matrix inequality) technique considering time-varying delays. The sufficient condition for the existence of controller and guaranteed cost state feedback controller design methods are presented. Also, using some changes of variables and Schur complements, the obtained sufficient condition can be reformulated as LMI forms in terms of transformed variables. Therefore, all solutions of LMIs, guaranteed cost controller gain, and guaranteed cost are obtained at the same time. The proposed controller design method can be extended into the problem of robust guaranteed cost controller design method for parameter uncertain systems with time-varying delays easily.

• 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.