• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.4
• /
• pp.15-26
• /
• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• Ecology and Resilient Infrastructure
• /
• v.3 no.2
• /
• pp.117-125
• /
• 2016

Current cities encounter various types of water problems due to rapid urbanization and climate change. The increasing significance of urban water problems calls for the establishment of resilient alternatives to prevent and minimize social loss that results from these phenomena. As a background research for establishing resilient infrastructures for the mitigation of urban water problems, we evaluated the robustness of structural alternatives for urban flood as a representative case. Combining the robustness index (RI) and the cost index (CI), we suggested the robustness-cost index (RCI) as an indicator of the robustness of structural alternatives, and applied the index to assess the existing infrastructures and structural alternatives (i.e., sewer network expansion, additional storage tank construction, and green roof construction) at a site prone to floods located around Gangnam-station, Seoul, Korea. At a rainfall intensity frequency range of 2 to 20 years, the usage of a storage tank and a green roof showed relatively high RCI value, with a variation of an alternative showing greater RCI between the two depending on the size of design rainfall. For a rainfall intensity frequency of 30 years, installing a storage tank with some green roofing was the most resilient alternative based on the RCI value. We proposed strategies for establishing resilient infrastructures for the mitigation of urban floods by evaluating the robustness of existing infrastructures and selecting optimal structural alternatives with the consideration of scales of design disaster.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.548-551
• /
• 1997

Performance of force tracking impedance control of robot manipulators is degraded by the uncertainties in the robot and environment dynamic model. The purpose of this paper is to improve the controller robustness by applying neural network. Neural networks are designed to learn the uncertainties in robot and environment model for compensating the uncertainties. The proposed scheme is verified through the simulation of 20DOF robot manipulator.

• Journal of Information Processing Systems
• /
• v.5 no.4
• /
• pp.221-228
• /
• 2009

Due to low cost, low-power, and scalability, ZigBee is considered an efficient wireless AMR infrastructure. However, these characteristics of ZigBee can make the devices more vulnerable to unexpected error environments. In this paper, a fault-tolerant wireless AMR network (FWAMR) is proposed, which is designed to improve the robustness of the conventional ZigBee-based AMR systems by coping well with dynamic error environments. The experimental results demonstrate that the FWAMR is considerably fault-tolerant compared with the conventional ZigBee-based AMR network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.263-266
• /
• 1997

In this paper we will develope the immnized recurrent neural network control system of a robot manipulator with high robustness in dynamically changing environment conditions. Immune system detects and eliminates the non-self materials called antigen such as virus, bacteria and so on which come from inside and outside of the living system, so plays an important role in maintaining its own system against dynamically changing environments. We apply this concept to a robot manipulator and evaluate the effectiveness of the above proposed system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.267-270
• /
• 1997

As an approach to develope a control system with robustness in changing control environment conditions, this paper will propose a robot manipulator control system using multilayer neural network and immune algorithm. The proposed immune algorithm which has the characteristics of immune system such as distributed and anomaly detection, probabilistic detection, learning and memory, consists of the innate immune algorithm and the adaptive immune algorithm. We will demonstrate the effectiveness of the proposed control system with simulations of a 2-link robot manipulator.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.161.4-161
• /
• 2001

This paper presents adaptive control of robot manipulator using neuro-fuzzy controller Fuzzy logic is control incorrect system without correct mathematical modeling. And, neural network has learning ability, error interpolation ability of information distributed data processing, robustness for distortion and adaptive ability. To reduce the number of fuzzy rules of the FLS(fuzzy logic system), we consider the properties of robot dynamic. In fuzzy logic, speciality and optimization of rule-base creation using learning ability of neural network. This paper presents control of robot manipulator using neuro-fuzzy controller. In proposed controller, fuzzy input is trajectory following error and trajectory following error differential ...

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.133.2-133
• /
• 2001

This paper focuses on the feasibility of fuzzy logic control for networked control systems. In order to evaluate its feasibility, a networked control system for motor speed control is implemented on a Profibus-DP network. The NCS consists of several independent, but interacting processes running on two separate stations. By using this NCS, the network delay is analyzed to find the cause of the delay. Furthermore, in order to prove the feasibility, the fuzzy logic controllers performance is compared with those of conventional PID controllers. Based on the experimental results, the fuzzy logic controller can be a viable choice for NCS due to its robustness against parameter uncertainty.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.224-229
• /
• 1992

We investigate a neural network as a dynamic system controller when system characteristics are abruptly changing. The shape of sigmoid functions are determined by autotuing method for the optimum sigmoid function of the neural networks. By using information stored in the identifying network a novel algorithm that can adapt the control action of the controller has been developed. Robustness can be seen from its ability to adjust large variations of parameters. The potential of the proposed method is demonstrated by simulations.

• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2149-2151
• /
• 2002

In this paper, a novel speed estimation method of an induction motor using neural networks(NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The neural network based vector controller has the advantage of robustness against machine parameter variation. The simulation results using Matlab/Simulink verify the useful of the proposed method.