• International Journal of Control, Automation, and Systems
• /
• v.4 no.3
• /
• pp.308-317
• /
• 2006

To control the tip position of a flexible-link manipulator, a neural network (NN) controller is proposed in this paper. The dynamics error used to construct NN controller is derived based on output redefinition approach. Without the filtered tracking error, the proposed NN controller can still guarantee the closed-loop system uniformly asymptotically stable as well as NN weights bounded. Furthermore, the tracking error of desired trajectory can converge to zero with the proposed controller. For comparison an NN controller with filtered tracking error is also designed for the flexible-link manipulator. Finally, simulation studies are carried out to verify the theoretic results.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.2
• /
• pp.223-233
• /
• 2008

A new output feedback controller design approach for flexible-joint (FJ) robots via the observer dynamic surface design technique is presented. The proposed approach only requires the feedback of position states. We first design an observer to estimate the link and actuator velocity information. Then, the link position tracking controller using the observer dynamic surface design procedure is developed. Therefore, the proposed controller can be simpler than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop system are uniformly ultimately bounded. Finally, the simulation results of a three-link FJ robot are presented to validate the good position tracking performance of the proposed control system.

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.6
• /
• pp.391-402
• /
• 2015

In this paper, we present implementation of backward movement control of truck-trailer vehicles using a fuzzy mode-based control scheme considering practical constraints and computational overhead. We propose a fuzzy feedback controller where output is predicted with the delay of a unit sampling period. Analysis and design of the proposed controller is very easy, because it is synchronized with sampling time. Stability analysis is also possible when quantization exists in the implementation of fuzzy control architectures, and we show that if the trivial solution of the fuzzy control system without quantization is asymptotically stable, then the solutions of the fuzzy control system with quantization are uniformly ultimately bounded. Experimental results using a toy truck show that the proposed control system outperforms a conventional system.

• KIEE International Transaction on Systems and Control
• /
• v.2D no.2
• /
• pp.108-114
• /
• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.1
• /
• pp.1-8
• /
• 2003

In this paper, an observer-based adaptive controller is proposed to control the longitudinal motion of vehicles. The standard gradient method is used to estimate the vehicle parameters, mass, time constant, etc. The inter-vehicle spacing and its derivatives are estimated by using the sliding mode cascade observer introduced in this paper. It is shown that the proposed adaptive controller is uniformly ultimately bounded. It is also shown that the errors of the relative distance, the relative velocity and the relative acceleration asymptotically converge to zero. The simulation results are presented to investigate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2753-2755
• /
• 2000

In this paper, the robust position tracking cotroller for a brushless DC motor driving a one-link robot manipulator is proposed. By using the backstepping approach, the adaptive and robust controller is appropriately designed to ensure global stability. The proposed robust backstepping controller can compensate for estimation errors in system parameters in the system with no structural changes in the controller and without destruction of the stability. The closed-loop stability of the system is shown using Lyapunov techniques. The tracking errors are shown to be globally uniformly bounded.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.3
• /
• pp.269-282
• /
• 2007

This paper presents a robust control method via the intelligent adaptive backstepping design technique for stable walking of nine-link biped robots with unknown model uncertainties and external disturbances. In our control structure, the self recurrent wavelet neural network(SRWNN) which has the information storage ability is used to observe the uncertainties of the biped robots. The adaptation laws for all weights of the SRWNN are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Also, we prove that all signals in the closed-loop adaptive system are uniformly ultimately bounded. Through computer simulations of a nine-link biped robot with model uncertainties and external disturbances, we illustrate the effectiveness of the proposed control system.

• Proceedings of the KIEE Conference
• /
• 1996.11a
• /
• pp.58-61
• /
• 1996

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1644-1645
• /
• 2007

This paper proposes an adaptive sliding mode control method for trajectory tracking of nonholonomic mobile robots with model uncertainties and external disturbances. The kinematic model represented by polar coordinates are considered to design a robust control system. Wavelet neural networks (WNNs) are employed to approximate arbitrary model uncertainties in dynamics of the mobile robot. From the Lyapunov stability theory, we derive tuning algorithms for all weights of WNNs and prove that all signals of an adaptive closed-loop system are uniformly ultimately bounded.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.16 no.3
• /
• pp.151-167
• /
• 2012

In this paper, we investigate the dynamic behaviors of a one-prey and two-predator system with Holling-type II functional response and defensive ability by introducing a proportion that is periodic impulsive harvesting for all species and a constant periodic releasing, or immigrating, for predators at different fixed time. We establish conditions for the local stability and global asymptotic stability of prey-free periodic solutions by using Floquet theory for the impulsive equation, small amplitude perturbation skills. Also, we prove that the system is uniformly bounded and is permanent under some conditions via comparison techniques. By displaying bifurcation diagrams, we show that the system has complex dynamical aspects.