• 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.

• 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.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1730-1731
• /
• 2007

In this paper, we present a direct adaptive control method using neural networks for the control of nonlinear systems. The weights of neural networks are trained by an adaptive backpropagation algorithm based on Lyapunov stability theory. We develop the parameter update-laws using the neural network input and the error between the desired output and the output of nonlinear plant to update the weights of a neural network in the sense that Lyapunove stability theory. Beside the output tracking error is asymptotically converged to zero.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1995.10b
• /
• pp.249-257
• /
• 1995

In this paper, we demeonstrate that neural networks can be used effectively for the control of nonlinear dynamical system. To adaptively control a plant, there are two distinct approach. these are direct control and indirect control. Both direct and Indirect adaptive control are trained using static back propagation. In indirect, using the resulting identification model, which contains neural networks and linear dynamical elements as subsystems, the parameters of the controller are adjusted.

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

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1186-1188
• /
• 1996

Neural networks and fuzzy systems have attracted the attention of many researehers recently. In general, neural networks are used to obtain information about systems from input/output observation and learning procedure. On the other hand, fuzzy systems use fuzzy rules to identify or control systems. In this paper we present a generalized FCMAC(Fuzzified Cerebellar Model Articulation Controller) networks, by integrating fuzzy systems with the CMAC(Cerebellar Model Articulation Controller) networks. We propose a direct adaptive controller design based on FCMAC(fuzzified CMAC) networks. Simulation results reveal that the proposed adaptive controller is practically feasible in nonlinear plant control.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.14-19
• /
• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.35 no.11
• /
• pp.990-998
• /
• 2007

Adaptive output feedback control technique using Neural Networks(NN) is proposed for uncertain nonlinear Multi-Input Multi-Output(MIMO) systems. Modified Dynamic Inversion Model(MDIM) is introduced to decouple uncertain nonlinearities from inversion-based control input. MDIM consists of approximated dynamic inversion model and inversion model error. One NN is applied to compensate the MDIM of the system. The output of the NN augments the tracking controller which is based upon a filtered error approximation with online weight adaptation laws which are derived from Lyapunov's direct method to guarantee tracking performance and ultimate boundedness. Several numerical results are illustrated in the simulation of Van der Pol system and unmanned helicopter with model uncertainties.

• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.400-402
• /
• 1997

This paper investigates the direct and adaptive control of mechatronic servo systems using modified chaotic neural networks (CNNs). For the performance evaluation of the proposed neural networks, we simulate the trajectory control of the X-Y table with direct control strategies. The CNN based controller demonstrates accurate tracking of the planned path and also shows superior performance on convergence and final error comparing with recurrent neural network(RNN) controller.

• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.903-905
• /
• 2003

In this paper, adaptive high-order neural network controller(AHONNC) is adopted to control of an induction servomotor. A algorithm is developed by combining compensation control and high-order neural networks. Moreover, an adaptive bound estimation algorithm was proposed to estimate the bound of approximation error. The weight of the high-order neural network can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the closed-loop system can be guaranteed. Simulation results for induction servomotor drive system are shown to confirm the validity of the proposed controller.