Un-Chul Moon

A self-organizing fuzzy logic controller for dynamic systems using a fuzzy auto-regressive moving average (FARMA) model

The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules, however, the proposed new fuzzy logic controller needs no expert in making control rules, Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are stored in the fuzzy rule space and updated online by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum. >

A self-organizing power system stabilizer using fuzzy auto-regressive moving average (FARMA) model

This paper presents a self-organizing power system stabilizer (SOPSS) which use the fuzzy auto-regressive moving average (FARMA) model. The control rules and the membership functions of the proposed fuzzy logic controller are generated automatically without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. To show the effectiveness of the proposed controller, comparison with a conventional controller for a one-machine infinite-bus system is presented.

Application of dynamic matrix control to a boiler-turbine system

This paper presents an application of dynamic matrix control (DMC) to a drum-type boiler-turbine system of a fossil power plant. Two possible kinds of step response models are investigated in designing the DMC, one is developed with the linearization of theoretical model and the other is developed with the process step-test data. Then, the control performances of each model-based DMC are simulated and evaluated. It is observed that the simulation results with the step-response model based on the test data show satisfactory results, while the linearized model is not suitable for the control of boiler-turbine system.

Temperature Control of Ultrasupercritical Once-through Boiler-turbine System Using Multi-input Multi-output Dynamic Matrix Control

Multi-input multi-output (MIMO) dynamic matrix control (DMC) technique is applied to control steam temperatures in a large-scale ultrasupercritical once-through boiler?turbine system. Specifically, four output variables (i.e., outlet temperatures of platen superheater, finish superheater, primary reheater, and finish reheater) are controlled using four input variables (i.e., two spray valves, bypass valve, and damper). The step-response matrix for the MIMO DMC is constructed using the four input and the four output variables. Online optimization is performed for the MIMO DMC using the model predictive control technique. The MIMO DMC controller is implemented in a full-scope power plant simulator with satisfactory performance.

Temperature control of glass melting furnace with fuzzy logic and conventional PI control

This paper presents a practical application of fuzzy logic control of the temperature of glass-melting furnace. Because of the complexity and nonlinearity, temperature control of glass-melting furnace is still delegated to human operator. Though the overall characteristics of glass-melting furnace are complex and nonlinear, one portion of the furnace characteristics can be modeled as a linear system. The linear portion of the furnace dynamics is modeled with a first-order-plus-dead-time (FOPDT) system and a PI controller is applied to the FOPDT model. The remaining complex and nonlinear portion of the furnace dynamics is covered by the fuzzy system, i.e., rules of human experts. The PI controller and fuzzy system are combined in cascade. Practical implementation results of Samsung-Corning Company showed the effectiveness of proposed control algorithm.

Once-through boiler steam temperature control using Dynamic Matrix Control technique

This paper presents an application of Dynamic Matrix Control (DMC) for controlling steam temperatures in a large-scale once-through boiler-turbine system. In order to control the steam temperatures, we choose a spray and a damper as two controllers. The step response model for the DMC is generated for the two major output variables, superheater and reheater temperatures, by performing step-input tests. On-line optimization is performed for the DMC using the step response model. Proposed controller is implemented in a large-scale power plant simulator and the simulation results show satisfactory performance of the proposed DMC technique.

An Adaptive Dynamic Matrix Control of a Boiler-Turbine System

This paper proposes an adaptive dynamic matrix control (DMC) using fuzzy inference and its application to boiler-turbine system. In a conventional DMC, object system is described as a step response model (SRM). However, a nonlinear system is not effectively described as a single SRM. In this paper, nine SRMs at various operating points are represented as fuzzy inference rules. On-line fuzzy inference is performed at every sampling step to find the suitable SRM. Therefore, the proposed adaptive DMC can consider the nonlinearity of boiler-turbine system. The simulation results show satisfactory result with wide range operation of boiler-turbine system.

An Adaptive Dynamic Matrix Control of a Boiler-Turbine System Using Fuzzy Inference

This paper proposes an adaptive dynamic matrix control (DMC) using fuzzy inference and its application to boiler-turbine system. In a conventional DMC, object system is described as a step response model (SRM). However, a nonlinear system is not effectively described as a single SRM. In this paper, nine SRMs at various operating points are represented as fuzzy inference rules. On-line fuzzy inference is performed at every sampling step to find the suitable SRM. Therefore, the proposed adaptive DMC can consider the nonlinearity of boiler-turbine system. The simulation results show satisfactory result with wide range operation of boiler-turbine system.

A Water-Wall Model of Supercritical Once-Through Boilers Using Lumped Parameter Method

This paper establishes a compact and practical model for a water-wall system comprising supercritical once-through boilers, which can be used for automatic control or simple analysis of the entire boiler-turbine system. Input and output variables of the water-wall system are defined, and balance equations are applied using a lumped parameter method. For practical purposes, the dynamic equations are developed with respect to pressure and temperature instead of density and internal energy. A comparison with results obtained using APESS, a practical thermal power plant simulator developed by Doosan Heavy Industries and Construction, is presented with respect to steady state and transient responses.

A Comparison Study of MIMO Water Wall Model with Linear, MFNN and ESN Models

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.