Marjan Golob

Decomposed fuzzy proportional–integral–derivative controllers

Abstract In this paper, several types of decomposed proportional–integral–derivative fuzzy logic controllers (PID FLCs) are tested and compared. An important feature of decomposed PID FLCs are their simple structures. In its simplest version, the decomposed PID FLC uses three one-input one-output inferences with three separate rule bases. Behaviours of proportional, integral and derivative PID FLC parts are defined with simple rules in proportional rule base, integral rule base and derivative rule base. The proposed decomposed PID FLC has been compared with several PID FLCs structures. All PID FLCs have been realised by the same hardware and software tools and have been applied as a real-time controller to a simple magnetic suspension system.

Modeling and control of the magnetic suspension system.

A fuzzy logic based controller applied to a simple magnetic suspension is presented in this paper. The simple electromagnet-ball system and the contactless optical position measurement system are developed as a physical model of the magnetic suspension. A nonlinear mathematical model is presented and linearized. This model has been used to design a discrete linear PID controller with optimal parameters. The physical real-time model was constructed in order to compare the performance of the linear discrete PID controller and the proposed fuzzy logic based PID controller. The decomposed fuzzy PID controller has proportional, integral, and derivative separate parts which are tuned independently. When testing it becomes clear that the decomposed fuzzy PID controller gives better performance over a typical operational range than a traditional linear PID controller.

Decomposition of a fuzzy controller based on the inference break-up method

A concept called the decomposition of multivariable control rules is presented. Fuzzy control is the application of the compositional rule of inference and it is shown how the inference of the rule base with complex rules can be reduced to the inference of a number of rule bases with simple rules. A fuzzy logic based controller is applied to a simple magnetic suspension system. The controller has proportional, integral and derivative separate parts which are tuned independently. This means that all parts have their own rule bases. By testing it was found that the fuzzy PID controller gives better performance over a typical operational range then a traditional linear PID controller. The magnetic suspension system and the contact-less optical position measurement system have been developed and applied for the comparative analysis of the real-time conventional PID control and the fuzzy control.

Artificial neural networking model for the prediction of high efficiency boiler steam generation and distribution

Abstract Development of artificial neural network (ANN) models using real plant data for the prediction of fresh steam properties from a brown coal-fired boiler of a Slovenian power plant. The power plant generates electrical and thermal energy used for the city-wide district heating. The energy is produced in three blocks. Each block consists of a coal-fired boiler and an extraction condensing steam turbine. The electricity production is planned, while the generation of heat for heating purposes depends on the ambient temperature. A model will be presented which, using an ANN, predicts the power production of the power plant and distributes the production between the boilers so that the latter operate at their highest efficiency. The real data on the amount of the generated steam in the existing system boilers will be compared to the results of the model and the findings will be indicated regarding the coal consumption savings and their impact on the environment. However, the final set of input parameters was optimised with a compromise between smaller number of parameters and higher level of accuracy through sensitivity analysis. Data for training were carefully selected from the available real plant data.

Input-output modelling with decomposed neuro-fuzzy ARX model

This paper presents a new neuro-fuzzy system based model, which is useful for the modelling of nonlinear dynamic systems. The new proposed model constitutes a soft computing method, namely, reasoning with a fuzzy inference system (FIS) and an optimisation by the neural-network learning algorithm. A structure, named the decomposed neuro-fuzzy ARX model is proposed. This structure is based on decomposition of the FIS. An evolution of a learning algorithm for the decomposed fuzzy model is suggested. A comparative study of dynamic system identification using conventional FIS models and the proposed neuro-fuzzy ARX model is presented for Box-Jenkins data set.

Identification of non-linear dynamic systems with decomposed fuzzy models

This paper presents an approach which is useful for the identification of discrete non-linear dynamic systems based on fuzzy relational models. Fuzzy systems are characterized by a rule-base specification. If the complexity of a rule-base increases, knowledge acquisition may become tedious because the number of rules increases with an increasing number of fuzzy variables. Decomposed fuzzy models are proposed and applied to dynamic systems modeling. The evolution of the identification algorithms for the decomposed fuzzy model is suggested. A comparative study of the dynamic system identification with the conventional relational model and the decomposed relational model is presented for a well-known identification problem, namely the Box-Jenkins gas furnace data.

MODELLING, SIMULATION AND FUZZY CONTROL OF THE GMAW WELDING PROCESS

Abstract Welding is an important manufacturing process that can be automated and optimised. The dynamic characteristic of a self-regulated, consumable electrode welding arc has been studied when the torch-to-work-piece distance varies with time. The self-regulation process have been modelled analytically and various dynamic models have been developed. Computer simulations have been used to obtain a better understanding of the mechanisms which change arc voltage and current in response to changes in arc length. A welding current fuzzy controller has been proposed.

Web-Based Monitoring and Control of Industrial Processes Used for Control Education

Abstract Web-based technologies enable the implementation of remote monitoring and control of industrial plants. In this paper, some solutions and advantages of using distributed process control systems for control education are presented. In the area of process control teaching, practical experience plays an important role and web-based technologies enable the implementation of remote experiments onto real laboratory or industrial systems for effective engineering education. We present our experiences with two remote experiments based on distributed control systems realized on laboratory systems. The user interface of the first experiment is implemented by professional supervision tools and is using web-based SCADA technology. In the second experiment we discuss the process of developing fault detection and isolation applications for online and remote education by using industrial equipment from the field of process technology (batch, continuous) supported by web technologies. Through-out the course, the student can remotely develop and test model-based and data-driven FDI schemes in Matlab/Simulink by using OPC technology. Finally, the improved platform for web-based remote control system experiments suitable for control education is proposed.

Fuzzy logic based quality monitoring in short-circuit gas metal arc welding

Conventional methods, e.g. destructive and non-destructive testing methods, are expensive and time-consuming; therefore, possibilities of online and automated quality control of a welding process during welding as such are investigated. The paper deals with the possibilities of application of fuzzy logic to the analysis of weld quality, particularly assessment of the weld surface condition by means of measurable electric signals emitted during welding. A simple fuzzy inference system was realised which could relatively efficiently assess the weld quality on the basis of time variations of the welding voltage and short-circuit time in a certain time window.

Comparison of the self-tuning on-off controller with the conventional switching controllers

A self-tuning on-off control algorithm concept is presented. The controller contains a predictor, which is used to estimate the future system output sequences taking into account the whole set of future on-off input sequences over a specified number of time intervals. For every time interval the optimal input is selected by minimizing a suitable cost function. The prediction is based on a parametric system model, whose parameters are estimated. some simulation studies are included to assess the relative performance characteristics of the proposed algorithms. The performance of the self-tuning on-off controller is compared with that of conventional switching controllers.<<ETX>>