• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2421-2427
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• 2012

This paper is discussed the design on non-linear adaptive attitude controller for quadrotor UAV. Quadrotor UAV featured to have four rotor, required the special controller to compensate for the model parameter uncertainties as the unstable nonlinear system. In this research, we designed the adaptive controller to compensate for the payload changes even though it is changed with industrial applications. Especially, based on the mathematical model of UAV, non-linear adaptive controller is suggested and the stability is verified using the Lyapunov function and finally proved its performance and effectiveness of update laws with various payload by simulation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.335-341
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• 2010

To improve the performance of inner loop based on PD controller for a unmanned helicopter, neural networks are applied. The performance of PD controller designed on the response characteristics of error dynamics decreases because of uncertain nonlinearities of the system. The nonlinearities are decoupled to modified dynamic inversion model(MDIM) and are compensated by the neural networks. For the training of the neural networks, online weight adaptation laws which are derived from Lyapunov's direct method are used to guarantee the stability of the controller. The results of the improved performance of PD controller by neural networks are illustrated in the simulation of unmanned helicopter with nonlinearities,

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.463-467
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• 2005

In this paper, the guidance law applicable to formation flight of UAV in three-dimensional space is proposed. The concept of miss distance, which is commonly used in the missile guidance laws, and Lyapunov stability theorem are effectively combined to obtain the guidance commands of the wingmen. The propose guidance law is easily integrated into the existing flight control system because the guidance commands are given in terms of velocity, flight path angle and heading angle to form the prescribed formation. In this guidance law, communication is required between the leader and the wingmen to achieve autonomous formation. The wingmen are only required the current position and velocity information of the leader vehicle. The performance of the proposed guidance law is evaluated using the complete nonlinear 6-DOF aircraft system. This system is integrated with nonlinear aerodynamic and engine characteristics, actuator servo limitations for control surfaces, various stability and control augmentation system, and autopilots. From the nonlinear simulation results, the new guidance law for formation flight shows that the vehicles involved in formation flight are perfectly formed the prescribed formation satisfying the several constraints such as final velocity, flight path angle, and heading angle.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2254-2259
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• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.487-492
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• 2007

This paper presents the development of digital robust fuzzy controller for uncertain nonlinear systems. The proposed approach is based on the intelligent digital redesign(IDR) method with considering the relaxed stability condition of fuzzy control system. The term IDR in the concerned system is to convert an existing analog robust control into an equivalent digital counterpart in the sense of the state-matching. We shows that the IDR problem can be reduced to find the digital fuzzy gains minimizing the norm distance between the closed-loop states of the analog and digital robust control systems. Its constructive conditions are expressed as the linear matrix inequalities(LMIs) and thereby easily tractable by the convex optimization techniques. Based on the nonquadratic Lyapunov function, the robust stabilization conditions are given for the sampled-data fuzzy system, and hence less conservative. A numerical example, chaotic Lorentz system, is demonstrated to visualize the feasibility of the proposed methodology.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.1 s.307
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• pp.13-20
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• 2006

In this paper, we propose a motion control scheme of robot manipulators based on visual feedback under camera-in-hand configuration. The desired joint velocity and acceleration for motion control is made by the feature-based visual data in the outer loop. The control input for tracking feature points on the image plane uses robot kinematics dynamic. The proposed control input consists of the image feature and the joint velocity error to achieve robustness to the parametric uncertainty. The stability of the closed-loop system is proved by Lyapunov approach. Computer simulations and experiments on a two degree of freedom manipulator with 5 links are presented to illustrate the performance of proposed control system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.6
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• pp.15-24
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• 2000

This paper presents an output feedback robust H$\infty$ control problem for a class of uncertain nonlinear systems, which can be represented by an fuzzy dynamic model. The nonlinear system is represented by Takagi-Sugeno fuzzy model, and the control design is carried out on the basis of the fuzzy model. Using a single quadratic Lyapunov function, the globally exponential stability and disturance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of robust H$\infty$ controllers are given in terms of linear matrix inequalities(LMIs). Constructive algorithm for design of robust H$\infty$ controller is also developed. The resulting controller is nonlinear and automatically tuned based on fuzzy operation.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.270-275
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• 2005

This paper presents a fuzzy model-based adaptive approach for synchronization of chaotic systems which consist of the drive and response systems. Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, we design the response system that estimates the parameters of the drive system by adaptive strategy. The adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. In addition, the controller in the response system contains two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples, including Doffing oscillator and Lorenz attractor, are given to demonstrate the validity of the proposed adaptive synchronization approach.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.211-216
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• 2004

In this thesis, we present the Takagi-Sugeno-Kang (TSK) fuzzy model based adaptive controller and adaptive identification for a general class of uncertain nonlinear dynamic systems. We use an estimated model for the unknown plant model and use this model for designing the controller. The hybrid adaptive control combined direct and indirect adaptive control based on TSK fuzzy model is constructed. The direct adaptive law can be showed by ignoring the identification errors and fails to achieve parameter convergence. Thus, we propose an TSK fuzzy model based hybrid adaptive (HA) law combined of the tracking error and the model ins error to adjust the parameters. Using a Lyapunov synthesis approach, the proposed hybrid adaptive control is proved. The hybrid adaptive law (HA) is better than the direct adaptive (DA) method without identifying the model ins error in terms of faster and improved tracking and parameter convergence. In order to show the applicability of the proposed method, it is applied to the inverted pendulum system and the performance is verified by some simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.571-576
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• 2004

This paper presents a delay dependent fuzzy $H_{\infty}$ controller design method for delayed fuzzy dynamic systems. Using delay-dependent Lyapunov function, the global exponential stability and $H_{\infty}$ performance problem arc discussed. A sufficient conditions for the existence of fuzzy controller is presented in terms of linear matrix inequalities(LMIs). A simulation example is given to illustrate the design procedures and performances of the proposed methods.