• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.5-8
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• 1996

This paper proposea a new adaptive algorithm to improve the performance fo a robust adaptive control system. This adaptive algorithm counteracts the effects of disturbances and makes the time derivative of Lyapunov function non-positive, $V{\leq}0$. As a result, the output error approaches zero as $t\;\rightarrow\;\infty$ not only in the presence of bounded disturbances, but also in the ideal case. The effectiveness of this proposed algorithm is demonstrated by the stability analysis and simulation results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.308-313
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• 2018

In this paper, we propose a sampled-data model predictive tracking control deign for leader-following control of multi-mobile robot system. The error dynamics of leader-following robots is modeled as a Linear Parameter Varying (LPV) model. Also, the Lyapunov function is presented to guarantee stability of the networked control system. Based on the stabilization condition using a quadratic Lyapunov function approach, model predictive sampled-data controller is designed. Finally, the leader-following control of multi mobile robots is simulated to show effectiveness of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2399-2410
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• 2017

In this paper, a linear matrix inequality-based sampled-data fuzzy observer design method is proposed based on the exact discretization approach. In the proposed design technique, a numerically relaxed observer design condition is obtained by using the discrete-time fuzzy Lyapunov function. Unlike the existing studies, the designed observer is robust to the uncertain premise variable because the fuzzy observer is designed under the imperfect premise matching condition, in which the membership functions of the system and observer are mismatched. In addition, we apply the proposed method to the state estimation problem of the attitude and heading reference system (AHRS). To do this, we derive a Takagi-Sugeno fuzzy model for the AHRS system, and validate the proposed method through the hardware experiment.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.594-600
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• 2007

A sufficient condition for the robust D-stability of dynamic interval systems is proposed in this paper. This D-stability condition is based on a parameter-dependent Lyapunov function obtained from the feasibility of a set of matrix inequalities defined at a series of partial-vertex-based interval matrices other than the total vertex matrices as previous results. This condition is also extended to the robust D-stabilization problem of dynamic interval systems, which supplies an effective synthesis procedure for any LMI D-region. The proposed conditions can be simplified to a set of LMIs, which can be solved by efficient interior point methods in polynomial time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.127-137
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• 2006

This paper presents an $H_{\infty}$ controller synthesis method for discrete time fuzzy dynamic systems based on a piecewise smooth Lyapunov function. The basic idea of the proposed approach is to construct controllers for the fuzzy dynamic systems in such a way that a Piecewise smooth Lyapunov function can be used to establish the global stability with $H_{\infty}$ performance of the resulting closed loop fuzzy control systems. It is shown that the control laws can be obtained by solving a set of Bilinear Matrix Inequalities (BMIs). An example is given to illustrate the application of the proposed method.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.394-396
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• 2005

In this paper, we propose an adaptive tracking control method of a mobile manipulator for contour following with a kinematic model to have several unknown dimension parameters. Moreover, we will use the decentralized control method to design two independent controllers for two subsystems. The proposed controllers in this paper are based on the Lyapunov function in order to guarantee the stability of whole system for contour following task. The updated laws are also designed to estimated the unknown dimension parameters. Finally, the simulation results are presented to show the validity of the proposed controllers in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.85-86
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• 2006

In this paper, a nonlinear controller based on adaptive sliding-mode method which has a sliding surface vector including new boundary function is proposed and applied to a two-wheeled voiding mobile robot (WMR). This controller makes the welding point of WMR achieve tracking a reference point which is moving on a smooth curved welding path with a desired constant velocity. The mobile robot is considered in view of a kinematic model and a dynamic model in Cartesian coordinates. The proposed controller can overcome uncertainties and external disturbances by adaptive sliding-mode technique. To design the controller, the tracking error vector is defined, and then the new sliding is proposed to guarantee that the error vector converges to zero asymptotically. The stability of the dynamic system will be shown through the Lyapunov method. The simulations is shown to prove the effectiveness of the proposed controller.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.235-243
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• 2000

The probabilistic data association filter(PDAF) is known to provide better tracking performance than the standard Kalman filter(KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al[7], in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to prove the stability of the PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom[7], is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.2
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• pp.101-101
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• 1996

This paper presents the linearized fuzzy modeling technique of nonlinear dynamical system and the stability analysis of fuzzy control system. Firstly, the nonlinear system is partitionized by multiple linear fuzzy subcontrol systems based on fuzzy linguistic variables and fuzzy rules. Secondly, the disturbance adaptaion controllers which guarantee the global asymptotic stability of each fuzzy subsystem by an optimal feedback control law are designed and the stability analysis procedures of the total fuzzy control system using Lyapunov functions and eigenvalues are discussed in detail through a given illustrative example.