• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.453-456
• /
• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. In order to evaluate the performance of our controller design method, two direct adaptive control methods are applied to a Duffing's equation and a Lorenz equation which are continuous-time chaotic systems. Our simulation results show the effectiveness of the controllers.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.1 no.1
• /
• pp.29-34
• /
• 2001

This paper is devoted to investigating direct adaptive neural control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neural networks control area, theoretical issues of the existing backpropagation-based adaptive neural networks control schemes. The major contribution is proposing the variable index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neural network control schemes. This new schemes possess inherent robustness to system model uncertainty, which is not required to satisfy any matching condition. To demonstrate the feasibility of the proposed leaning algorithms and direct adaptive neural networks control schemes, intensive computer simulations were conducted based on the flexible joint robot systems and functions.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.53 no.4
• /
• pp.201-204
• /
• 2004

In this paper, a direct adaptive tracking controller based Lyapunov method is designed for a wheeled mobile robots. A wheeled mobile robots have three degrees of freedom and two control variables. Therefore, it is difficult to control a mobile robot using the general linear control. We introduce two kinds of Lyapunov function for the design of the controller and verify the controller. A mobile robots using the designed adaptive direct tracking controller is well-behaved and is easily implemented.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.8
• /
• pp.140-146
• /
• 1995

In case the uncertainty existing in a system is assumed to satisfy the matching condition and to be come-bounded. Y. H. Chen proposed an adaptive robust control algorithm which introduced adaptive sheme for a design parameter into robust deterministic controls. In this paper, the adaptive robust control algorithm is applied to the position tracking control of direct drive robots, and simulation and experimental studies are conducted to evaluate control performance.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.337-340
• /
• 1997

In this paper we propose direct and indirect adaptive controllers for a nonlinear multivariable steam generating unit(200MW). In the direct adaptive scheme the estimation of the controller parameter are achieved from tracking error, while in the indirect approach the unknown parameter of the boiler system is estimated by the Hopfield network-based identifier. The performance of two proposed adaptive controllers is shown through simulations.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.12
• /
• pp.2926-2934
• /
• 2000

This study presents a direct adaptive controller which is derived by using Lyapunovs direct methods for trajectory tracking control of a pneumatic cylinder. The structure of the controller is very simple and computationally efficient because it does not use either the dynamic model or the parameter values of the pneumatic system. The bounded stability of the system is shown in the presence of the bounded unmodeled dynamics. The bounded size of tracking errors can be made arbitrarily small without giving andy influences on either input or output variables. The trajectory tracking performance and the stability of the control system is verified experimentally. The results of the experiments show that the proposed controller tracks the given trajectories, sine function and cycloidal function trajectories, more accurately than PD controller does, and it stabilizes the system and adaptive variables.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.6
• /
• pp.564-572
• /
• 2008

The performance of the direct adaptive time-delay command shaping filter depends on the select time-delay. In the previously introduced direct adaptive command shaping filter, however, the time-delay value is fixed and only the magnitudes of the impulses are learned. In this paper, the authors introduce a new scheme to adapt the time-delay which is to be used in conjunction with the direct adaptive command shaping for the improved vibration suppression in flexible motion system. In order to formulate the time-delay adaptation scheme, the authors have analyzed the effect of the time-delay value on the performance of the direct adaptive command shaping filter. By modifying the direct adaptation formula based on the analysis result the authors have established a set of equations to adapt the time-delay toward the optimal value. Simulation results show the effectiveness of the proposed time-delay adaptation approach for the improved vibration suppression.

• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2187-2189
• /
• 2003

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of chaotic systems. The conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on a direct adaptive control method is proposed to control chaotic systems whose mathematical models are not available. The gradient-descent method is used for training a wavelet neural network controller. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic system.

• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2190-2192
• /
• 2003

In this paper, a state observer based direct adaptive fuzzy controller for unknown nonlinear dynamical system is presented. The adaptive parameters of the direct adaptive fuzzy controller can be tuned by using a projection algorithm on-line based on the Lyapunov synthesis approach. A maximum control is used to guarantee the robustness of system. A stability analysis of the overall adaptive scheme is discussed based on the sense of Lyapunov. The inverted pendulum simulation example shows that proposed control algorithm can be used for the tracking problem of nonlinear system.

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