A Fuzzy Logic Based Automatic Control of Rotary Crane (A Simulation Approach)

Abstract

In this paper, our aim was to the control the Rotary crane by using Fuzzy logic. For this one must obtain the mathematical model of the crane. After obtaining the model, the dynamics of the crane is identified. The Rotary crane is to be controlled in such a manner to increase the system speed without compromising the stability and safety of the whole system. Also the load is transferred to desired location accurately. The main advantage of Fuzzy based control is that no parameter identification is required as compare to other control strategies available. There are some applications where the conventional control strategies cannot provide the desired response up to certain level. But the fuzzy logic based control is capable of enhance the response, since it has learning capability which helps to create the accurate rules for each separate conditions. Also this fuzzy based control will reduce oscillations of payload to prevent hazards for people and equipments in the work place, industries.

Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach

“…Fuzzy logic proved to be a valuable tool for translating the dive log data into quantitative form [5]. Temperature, salinity, photoperiod, pH, dissolved oxygen, water flow and water level were monitored and controlled in a closed, recirculating seawater raceway [6]. A fuzzy logic-based expert system replaced the classical process control system for operation of the bioreactor, continuing to optimize de-nitrification rates and eliminate discharge of toxic by-products [7].…”

Mathematical Modelling, Stability Analysis and Control of Flexible Double Link Robotic Manipulator: A Simulation Approach

“…The nonlinear differential equations are used to describe the dynamic characteristics of nonlinear systems [1][2][3][4]. Practically, nonlinear systems are solved by numerical and graphical methods [5][6].…”

Fuzzy logic based automation and control simulation of Sulfuric acid manufacturing process: A case study

“…Rule-base of fuzzy logic breaks the control problem down into a series of IF X AND Y THEN Z rules that define the desired system output response for given system input conditions . The number and complexity of rules depends on the number of input parameters that are to be processed and the number of fuzzy variables associated with each parameter [7][8]. Create fuzzy logic membership functions that define the meaning (values) of input/output terms used in the rules.…”

“…So instead of conventional or advanced controller which totally depends on the mathematical model, fuzzy logic control strategy has been selected as an option. This scheme of control will evaluate certainty within uncertainty and handling the parameters within range to control the entire process continuously though fluctuations occurs [7][8]. Fuzzy control provides effective solutions for nonlinear and partially unknown processes, mainly because of its ability to combine information from different sources [9].…”