• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.479-479
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• 2000

Recently, IVS(Intelligent Vehicle Systems) or ITS(Intelligent Traffic Systems) are much concerned subjects of automotive industry. In this paper, we will introduce an Intelligent Online Driving System for a car. This system allows the driver to be able to drive the car just by operating an integrated joystick. The proposed driving system could be implemented into any car and the key point of the design is that the driver still can drive the car as normal without using the joystick. Our Intelligent Online Driving System includes the integrated joystick, steering wheel control system, brake and acceleration (B&A)pedals control system, and the central control computer system. Steering wheel and B&A pedals are controlled by AC servo-motors. The integrated joystick generates the desired positions and the embedded computer controls these two servomotors to track the commands given by joystick. The control method for two servo-motors is PID control.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.10
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• pp.1905-1919
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• 2000

The main motivation of this research is to develop an intelligent control strategy for Activated Sludge Process (ASP). ASP is a complex and nonlinear dynamic system because of the characteristic of wastewater, the change in influent flow rate, weather conditions, and etc. The mathematical model of ASP also includes uncertainties which are ignored or not considered by process engineer or controller designer. The ASP is generally controlled by a PID controller that consists of fixed proportional, integral, and derivative gain values. The PID gains are adjusted by the expert who has much experience in the ASP. The ASP model based on $Matlab^{(R)}5.3/Simulink^{(R)}3.0$ is developed in this paper. The performance of the model is tested by IWA(International Water Association) and COST(European Cooperation in the field of Scientific and Technical Research) data that include steady-state results during 14 days. The advantage of the developed model is that the user can easily modify or change the controller by the help of the graphical user interface. The ASP model as a typical nonlinear system can be used to simulate and test the proposed controller for an educational purpose. Various control methods are applied to the ASP model and the control results are compared to apply the proposed intelligent control strategy to a real ASP. Three control methods are designed and tested: conventional PID controller, fuzzy logic control approach to modify setpoints, and fuzzy-PID control method. The proposed setpoints changer based on the fuzzy logic shows a better performance and robustness under disturbances. The objective function can be defined and included in the proposed control strategy to improve the effluent water quality and to reduce the operating cost in a real ASP.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.439-447
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• 2003

This paper proposes a proportional-integral-derivative (PID) evaluation method for enhancing performance of genetic algorithms. In PID evaluation, the fitness of individuals is evaluated by not only the fitness derived from an evaluation function, but also the parents fitness of each individual and the minimum and maximum fitness from initial generation to previous generation. This evaluation decreases the probability that the genetic algorithms fall into a premature convergence phenomenon and results in enhancing the performance of genetic algorithms. We experimented our evaluation method with typical numerical function optimization problems. It was found from extensive experiments that out evaluation method can increase the performance of genetic algorithms greatly. This evaluation method can be easily applied to the other types of genetic algorithms for improving their performance.

• Journal of Navigation and Port Research
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• v.29 no.6 s.102
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• pp.555-560
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• 2005

Recently, many control systems are replaced with digital control systems in an effort to optimize the overall performance. In order to operate these systems efficiently, the conventional point-to-point connection method must be changed to the signal exchange via a communication network. This paper investigates the technical feasibility of the crane system using CAN protocol which is a part NMEA 2000 by implementing a network-based control system emulating the crane control system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.525-532
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• 2004

Telemanipulator is distingushed from industrial robot by iterating same specified work. Manipulator operator is included in control loop for controlling the telemanipulator because he decide directly during the work and order controllabily. We implement fuzzy controller for reducing the modelling error of telemanipulator which depend on the PID controller. But position-force control method of bidirectional control impose unsafety of vibiration and Analytic Hierchy method can stabilize for reducing nonlinear modelling error by expert operator because of transformation empirical control rule to linear model.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.106-116
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• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.648-654
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• 2004

The Mass Flow Controller(MFC) has become crucial in semiconductor manufacturing equipments. It is an important element because the quality and the yield of a semiconductor process are decided by the accurate flow control of gas. Therefore, the demand for implementing the high speed and the highly accurate control of MFCs has been increasing. It is hard to find an article of the control algorithm applied to MFCs. But, it is known that commercially available MFCs adopt PID control algorithms. Particularly, when the system detects the flow by way of heat transfer, the MFC control problem includes the slow response and the nonlinearity. In this paper, MFC control algorithm with a superior performance to the conventional PID algorithm is discussed and the superiority is demonstrated through the experiment. A fuzzy controller was utilized in order to compensate the nonlinearity and the slow response, and the performance is compared with that of an MFC currently available in the market. The control system, in this paper, consists of a personal computer, the data acquisition board and the control algorithm carried out by LabWindows/CVI program on the PC. In addition, a method of estimating the actual flow from the sensor output with the slow response is presented. In conclusion, according to the result of the experiment, the proposed algorithm shows better accuracy and is faster than the conventional controller.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.78-91
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• 2002

In the thermal power plant, there are six manipulated variables: main steam flow, feedwater flow, fuel flow, air flow, spray flow, and gas recirculation flow. There are five controlled variables: generator output, main steam pressure, main steam temperature, exhaust gas density, and reheater steam temperature. Therefore, the thermal power plant control system is a multinput and output system. In the control system, the main steam temperature is typically regulated by the fuel flow rate and the spray flow rate, and the reheater steam temperature is regulated by the gas recirculation flow rate. However, strict control of the steam temperature must be maintained to avoid thermal stress. Maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature versus changes in fuel flow rate, difficulty of control of the main steam temperature control and the reheater steam temperature control system owing to the dynamic response characteristics of changes in steam temperature and the reheater steam temperature, and the fluctuation of inner fluid water and steam flow rates during the load-following operation. Up to the present time, the Proportional-Integral-Derivative Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on the characteristic comparison of the PID controller and the modified 2-DOF PID Controller (Two-Degrees-Freedom Proportional-Integral-Derivative) on the DCS (Distributed Control System). The method is to design an optimal controller that can be operated on the thermal generating plant in Seoul, Korea. The modified 2-DOF PID controller is designed to enable parameters to fit into the thermal plant during disturbances. To attain an optimal control method, transfer function and operating data from start-up, running, and stop procedures of the thermal plant have been acquired. Through this research, the stable range of a 2-DOF parameter for only this system could be found for the start-up procedure and this parameter could be used for the tuning problem. Also, this paper addressed whether an intelligent tuning method based on immune network algorithms can be used effectively in tuning these controllers.

• Journal of Power Electronics
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• v.7 no.2
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• pp.159-173
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• 2007

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.