Jin-Bae Park

Generalized Predictive Control of Discrete-Time Chaotic Systems

A generalized predictive control method based on an ARMAX model is suggested for chaos control in discrete-time systems. Both control performance and system sensitivity to initial conditions of this approach are compared with the conventional model-referenced adaptive control via numerical simulations. Simulation results show that this controller yields faster settling time, more accurate target tracking, and less initial sensitivity.

A New Sliding-Surface-Based Tracking Control of Nonholonomic Mobile Robots

This paper proposes a new sliding-surface-based tracking control system for nonholonomic mobile robots with disturbance. To design a robust controller, we consider the kinematic model and the dynamic model of mobile robots with disturbance. We also propose a new sliding surface to solve the problem of previous study. That is, since the new sliding surface is composed of differentiable functions unlike the previous study, we can obtain the control law for arbitrary trajectories without any constraints. From the Lyapunov stability theory, we prove that the position tracking errors and the heading direction error converge to zero. Finally, we perform the computer simulations to demonstrate the performance of the proposed control system.

Control of discrete-time chaotic systems using generalized predictive control

In this study, a controller design method is proposed for controlling the discrete-time chaotic systems. The proposed method is based on generalized predictive control and uses NARMAX models as controlled models. In order to evaluate the performance of the proposed method, a proposed controller is applied to discrete-time chaotic systems, and then the control performance and initial sensitivity of the proposed controller are compared with those of the conventional model-based controller through computer simulations.

Leader-following Approach Based Adaptive Formation Control for Mobile Robots with Unknown Parameters

In this paper, a formation control method based on the leader-following approach for nonholonomic mobile robots is proposed. In the previous works, it is assumed that the followers know the leaders velocity by means of communication. However, it is difficult that the followers correctly know the leaders velocity due to the contamination or delay of information. Thus, in this paper, an adaptive approach based on the parameter projection algorithm is proposed to estimate the leaders velocity. Moreover, the adaptive backstepping technique is used to compensate the effects of a dynamic model with the unknown time-invariant and time-varying parameters. From the Lyapunov stability theory, it is proved that the errors of the closed-loop system are uniformly ultimately bounded. Simulation results illustrate the effectiveness of the proposed control method.

Design of Path Tracking Controller for Underactuated Autonomous Underwater Vehicle Using Approach Angle Concept

In this paper, we propose a method for designing the path tracking controller using an approach angle concept for an underactuated autonomous underwater vehicle (AUV). The AUV is controlled by the surge speed and yaw rate: there is no side thruster. To solve this underactuated AUV problem in the path tracking, we introduce an approach angle concept which makes the AUV converge to the reference path. And we design the path tracking controller using the proposed approach angle. To design the path tracking controller, we obtain the new vehicle`s error dynamics in the body-fixed frame, and then design the path tracking controller based on Lypunov direct method. Finally, some simulation results demonstrate the effectiveness of the proposed controller.

Hybrid Sliding Mode Control of 5-link Biped Robot in Single Support Phase Using a Wavelet Neural Network

Generally, biped walking is difficult to control because a biped robot is a nonlinear system with various uncertainties. In this paper, we propose a hybrid sliding-mode control method using a WNN uncertainty observer for stable walking of the 5-link biped robot with model uncertainties and the external disturbance. In our control system, the sliding mode control is used as main controller for the stable walking and a wavelet neural network(WNN) is used as an uncertainty observe. to estimate uncertainties of a biped robot model, and the error compensator is designed to compensate the reconstruction error of the WNN. The weights of WNN are trained by adaptation laws that are induced from the Lyapunov stability theorem. Finally, the effectiveness of the proposed control system is verified through computer simulations.

Identification of Dynamic Systems Using a Self Recurrent Wavelet Neural Network: Convergence Analysis Via Adaptive Learning Rates

This paper proposes an identification method using a self recurrent wavelet neural network (SRWNN) for dynamic systems. The architecture of the proposed SRWNN is a modified model of the wavelet neural network (WNN). But, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. Thus, in the proposed identification architecture, the SRWNN is used for identifying nonlinear dynamic systems. The gradient descent method with adaptive teaming rates (ALRs) is applied to 1.am the parameters of the SRWNN identifier (SRWNNI). The ALRs are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of an SRWNNI. Finally, through computer simulations, we demonstrate the effectiveness of the proposed SRWNNI.

Adaptive Sliding-Mode Formation Control and Collision Avoidance for Multi-agent Nonholonomic Mobile Robots with Model Uncertainty and Disturbance

In this paper, an adaptive sliding-mode formation control and collision avoidance are proposed for electrically driven nonholonomic mobile robots with model uncertainties and external disturbances. A sliding surface based on the leader-follower approach is developed to achieve the desired formation in the presence of model uncertainties and disturbances. Moreover, by using the collision avoidance function, the mobile robots can avoid the obstacles successfully. Finally, simulations illustrate the effectiveness of the proposed control system.

Self-Recurrent Wavelet Neural Network Based Adaptive Backstepping Control for Steering Control of an Autonomous Underwater Vehicle

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.

Terminal Sliding Mode Control of Nonlinear Systems Using Self-Recurrent Wavelet Neural Network

In this paper, we design a terminal sliding mode controller based on self-recurrent wavelet neural network (SRWNN) for the second-order nonlinear systems with model uncertainties. The terminal sliding mode control (TSMC) method can drive the tracking errors to zero within finite time in comparison with the classical sliding mode control (CSMC) method. In addition, the TSMC method has advantages such as the improved performance, robustness, reliability and precision. We employ the SRWNN to approximate model uncertainties. The weights of SRWNN are trained by adaptation laws induced from Lyapunov stability theorem. Finally, we carry out simulations for Duffing system and the wing rock phenomena to illustrate the effectiveness of the proposed control scheme.