• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1441-1444
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• 1996

This paper presents the position control of a double-rod cylinder system activated by an electrotheological(ER) valve unit. Following the composition of a silicone oil-based ER fluid, theological properties of the ER fluid are experimentally tested as a function of imposed electric fields to determine appropriate design parameters of the ER valve. The ER valves are then designed and manufactured. Subsequently, the pressure drop of the ER valve is evaluated with respect to the intensity of the electric field. Four ER valves bridge-cylinder system is formulated, and the governing equations for the system are derived. A neural network control scheme is then synthesized to perform the position control of the cylinder system. Tracking control responses are experimentally evaluated and presented in order to demonstrate the effectiveness of the proposed control system.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.480-487
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• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.209-219
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• 1996

Fieldbus provides a real-time data communication among field devices in the process control and manufacturing automation systems. In this paper, a Petri Net model of the 1993 draft of IEC/ISA fieldbus which is proposed as an international standard of fieldbus network is developed. Based on the Petri Net model, discrete-event simulation model of IEC/ISA fieldbus network is developed. This paper evaluates the network induced delay in the data link layer of IEC/ISA fieldbus using the simulation model. In addition, an integrated discrete-event/continuous-time simulation model of fieldbus system and distributed control system is developed. This paper investigates the real-time data processing capability of IEC/ISA fieldbus and the effect of network-induced delay to the performance of control system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.286-293
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• 1999

A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer-based torque estimator with low pass filter should dbjust the time constant of the adopted filter according to the natural resonance frequency detemined by considering the system parameters varied. The simulation results shows the validity of the proposed control scheme.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.262-266
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• 1999

In this paper, we consider to apply of 2-DOF (Degree of Freedom) PID controller at D.C servo motor system. Many control system use I-PD, PID control system, but the position control system have difficulty in controling variable load and changing parameter. We propose neural network 2-DOF PID control system having feature for removal disturbrances and tracking function in the target value point. The back propagation algorithm of neural network used for tuning the 2-DOF parameter ($\alpha$, $\beta$, ${\gamma}$, η). We investigate the 2-DOF PID control system in the position control system and verify the effectiveness of proposal method through the result of computer simulation.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.6
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• pp.295-301
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• 2013

In this paper, we propose a real-time centralized soft motion control system for high speed and precision robot control. The system engages EtherCAT as high speed industrial motion network to enable force based motion control in real-time and is composed of software-based master controller with PC and slave interface modules. Hard real-time control capacity is essential for high speed and precision robot control. To implement soft based real time control, The soft based master controller is designed using a real time kernel (RTX) and EtherCAT network, and servo processes are located in the master controller for centralized motion control. In the proposed system, slave interface modules just collect and transfer all sensor information of robot to the master controller via the EtherCAT network. It is proven by experimental results that the proposed soft motion control system has real time controllability enough to apply for various robot control systems.

• Journal of Power Electronics
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• v.11 no.3
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• pp.279-284
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• 2011

Network-based controls for ac motor drive systems are becoming increasingly important. In this paper, an ac motor control system is implemented by a motor control module and three sensor modules such as a voltage sensor module, a current sensor module, and an encoder module. There will inevitably be network time delays from the sensor modules to the motor control system, which often degrades and even destabilizes the motor drive system. As a result, it becomes very difficult to estimate the network delayed ac sensor data. An algorithm to reduce the effects of network time delays on sensor data is proposed, using both a synchronization signal and a simple method for estimating the sensor data. The algorithm is applied to a vector controlled induction motor drive system, and the performance of the proposed algorithm is verified with experiments.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1743_1744
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• 2009

In this paper, we propose a double network control approach for linear systems. Generally, there are two network control system structures: the direct structure and the hierarchical structure. Here, the hierarchical structure consists of a main controller and a remote controller. The network delay of the structure only appears in the closed loop between the main controller and the remote system. However, the delay can exist between the remote controller and the actuator. Therefore, we design the double network system with delays between the main controller and the remote system, and the remote controller and the actuator. Finally, we carry out simulations on the linear system to illustrate the effectiveness of the proposed control method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.494-497
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• 2002

In this paper, we proposed a control method of an unknown nonlinear system using a dynamical neural network. The proposed method performs for a nonlinear system with unknown system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed method, we simulated one-link manipulator. The simulation results showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.