FUZZY LOGIC-BASED CONTROL OF THREE-DIMENSIONAL CRANE SYSTEM

The control of three-dimensional (3D) crane system represents one of the most widely challenging control problems. 3D crane system is used for lifting and moving loads horizontally, as well as lowering and realizing the gripper to the original position. In this paper fuzzy logic-based control of three-dimensional crane (3D) system is presented. Hence the system produces oscillations during moving loads, the main objective of the designed controller is to control the swing angle. As a plant for controller design, the bond graph model of 3D crane system is used. To verify the effectiveness of the proposed control method, several digital simulations with concrete values of parameters are performed using Matlab. The simulations results show that the proposed fuzzy logic control produce better performance in regard to the reduction of undesired oscillations.Key words: bond graph, 3D crane, Dymola, fuzzy control, modeling and simulation, Matlab/Simulin

A NEW TYPE OF DISCRETE EULER-LAGRANGE EQUATION WITH APPLICATIONS IN OPTIMAL CONTROL

A new type of discrete Euler-Lagrange equation, suitable for generalization, is presented in this paper. Several forms of this equation can be found in references. They have different differential operators as well as combinations of them. The equation given in this paper uses only one differential operator providing easy generalizations of Euler-Lagrange equation. In the paper, generalizations for the case of more variables and more ordered differences in the functional which is optimized, are derived. The application in determining optimal control of a discrete system is also given

LABORATORY CNC MACHINE FOR EDUCATION OF STUDENTS IN CONTROL SYSTEMS ENGINEERING

Modern states seek to build a society based on knowledge, and in this sense, the IPA project ADRIA HUB aims to connect students, universities and companies into a single entity in which each party realizes many benefits. Practical part of this project consists of the pilot projects related to the improvements in the woodworking industry. For the purpose of the project realization, a specific laboratory CNC machine was designed and implemented by the Laboratory for Modeling, Simulation and Control Systems at the Faculty of Electronic Engineering in Niš. CNC machine, presented in this paper, is now actively used in laboratory work. Students have the opportunity to gain practical knowledge and master the techniques of controlling this machine as part of their studies in Control Systems Engineering

DATA DENOISING PROCEDURE FOR NEURAL NETWORK PERFORMANCE IMPROVEMENT

This paper will present training data denoising procedure for neural network performance improvement. Performance improvement will be measured by evaluation criterion which is based on a training estimation error and signal strength factor. Strength factor will be obtained by applying denoising method on a default training signal. The method is based on a noise removal procedure performed on the original signal in a manner which is defined by the proposed algorithm. Ten different processed signals are obtained from the performed method on a default noisy signal. Those signals are then used as a training data for the nonlinear autoregressive neural network learning phase. Empirical comparisons are made at the end, and they show that the proposed denoising procedure is an effective way to improve network performances when the training set possesses the significant noise component

Savremene tehnike upravljanja sistemom protiv blokiranja točkova

The main goal of research in this PhD dissertation is to investigate the possibilities of application of modern control methods in anti-lock braking system (ABS), in order to increase the safety of passengers in traffic during vehicle emergency braking. The complete historical overview of ABS development is also presented, as well as the basic components of the system. Bearing in mind that the testing of newly designed algorithms is impractical on the real system, the laboratory experimental setup of ABS is used. The modeling of system using different methods is performed first, resulting in several models, where each of them could be used during the design of a specific control method. Since it is demonstrated that the model describing the dynamics of ABS is quite nonlinear, a special emphasis is placed on the use of sliding mode control, both in the continuous- and discrete-time domains. This dissertation also analyzes the possibility of combining sliding mode control with different intelligent control methods, such as fuzzy control systems, genetic algorithms and neural networks, all with the aim of overcoming the shortcomings of the certain control methods and improving system performances. Fuzzy control theory and genetic algorithms are implemented in setting the parameters of control laws, eliminating the need to adjust the parameters by trial and error method. In the domain of neural networks, the significant modifications in the traditional adaptive neuro-fuzzy inference system (ANFIS) are introduced, whereby almost orthogonal functions are inserted in particular network layer. The further network adaptation is performed by introducing external stimulus in the form of hormone secretion from the glands of the endocrine system. It is also designed a new structure consisting of almost orthogonal endocrine neural networks and nonlinear autoregressive neural network with external input (NARX) that is used during the prediction of modeling error. In the end, it is important to emphasize that the justification for introducing and the effectiveness of the proposed control algorithms are verified by a series of laboratory experiments with a comparative analysis of the obtained results with the results of the application of well-known control methods

Input data preprocessing method for exchange rate forecasting via neural network

The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data. [Projekat Ministarstva nauke Republike Srbije, br.TR 35005, br. III 43007 i br. III 44006