scholarly journals Control of Rotary Cranes Using Fuzzy Logic

2003 ◽  
Vol 10 (2) ◽  
pp. 81-95 ◽  
Author(s):  
Amjed A. Al-mousa ◽  
Ali H. Nayfeh ◽  
Pushkin Kachroo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Operating Conditions ◽  
Building Construction ◽  
Loop Control ◽  
Industrial Structures ◽  
Inference Engines ◽  
First Case

Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them has become difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control attractive.In this work a fuzzy logic controller is introduced with the idea of “split-horizon”; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent sub-controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FIE). Computer simulations are used to verify the performance of the controller. Three simulation cases are presented. In the first case, the crane is operated in the gantry (radial) mode in which the trolley moves along the jib while the jib is fixed. In the second case (rotary mode), the trolley moves along the jib and the jib rotates. In the third case, the trolley and jib are fixed while the load is given an initial disturbance. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing the maneuvers in relatively reasonable times.

scholarly journals Control of Rotary Cranes Using Fuzzy Logic

2001 ◽  
Author(s):  
Amjed A. Al-mousa ◽  
Ali H. Nayfeh ◽  
Pushkin Kachroo
Keyword(s):  
Fuzzy Logic ◽  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Operating Conditions ◽  
Building Construction ◽  
Loop Control ◽  
Industrial Structures ◽  
Inference Engines

Abstract Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them became difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control preferable. In this work a fuzzy logic controller is introduced with the idea of split-horizon; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FTEs). Computer simulations are used to verify the performance of the controller. Three simulation cases are introduced: radial, compound, and damping. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing them in a relatively reasonable time.

Hybrid Anti-Windup Fuzzy PI Controller Based Direct Torque Control of Three Phase Induction Motor

2014 ◽  
Vol 573 ◽  
pp. 155-160
Author(s):  
A. Pandian ◽  
R. Dhanasekaran
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Fuzzy Controller ◽  
Direct Torque Control ◽  
Operating Conditions ◽  
Torque Control ◽  
Constant Speed ◽  
Three Phase

This paper presents improved Fuzzy Logic Controller (FLC) of the Direct Torque Control (DTC) of Three-Phase Induction Motor (IM) for high performance and torque control industrial drive applications. The performance of the IM using PI Controllers and general fuzzy controllers are meager level under load disturbances and transient conditions. The FLC is extended to have a less computational burden which makes it suitable for real time implementation particularly at constant speed and torque disturbance operating conditions. Hybrid control has advantage of integrating a superiority of two or more control techniques for better control performances. A fuzzy controller offers better speed responses for startup and large speed errors. If the nature of the load torque is varied, the steady state speed error of DTC based IM drive with fuzzy logic controller becomes significant. To improve the performance of the system, a new control method, Hybrid fuzzy PI control is proposed. The effectiveness of proposed method is verified by simulation based on MATLAB. The proposed Hybrid fuzzy controller has adaptive control over load toque variation and can maintain constant speed.

Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle Control of Inverted Pendulum (IP) Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 308-320
Author(s):  
Ashwani Kharola
Keyword(s):  
Fuzzy Logic ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Data Sets ◽  
Inference System ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

scholarly journals Frequency stabilization using fuzzy logic based controller for multi-area power system

2007 ◽  
Vol 25 (1) ◽  
pp. 22 ◽  
Author(s):  
H.D. Mathur ◽  
H.V. Manjunath
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Frequency Control ◽  
Electrical Power ◽  
Load Frequency Control ◽  
System Response ◽  
Electrical Power System

In this paper, a fuzzy logic controller is proposed for load frequency control problem of electrical power system. The fuzzy controller is constructed as a set of control rules and the control signal is directly deduced from the knowledge base and the fuzzy inference. The study has been designed for a two area interconnected power system. A comparison among a conventional proportional integral (PI) controller, some other fuzzy gain scheduling controllers and the proposed fuzzy controller is presented and it has been shown that proposed controller can generate the best dynamic response following a step load change. Robustness of proposed controller is achieved by analyzing the system response with varying system parameters.

scholarly journals ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Manuel Braz César ◽  
Rui Carneiro Barros
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Magnetorheological Dampers ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Degrees Of Freedom

Abstract In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.

scholarly journals Development of Line Follower Robot with Camera Surveillance System

2019 ◽  
Vol 8 (4) ◽  
pp. 2192-2197
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Feedback Mechanism ◽  
Control Technique ◽  
Loop Control ◽  
Fuzzy Logic Algorithm ◽  
Surveillance Camera ◽  
The Right ◽  
Switching Method

This study describes the development of a line follower robot for a surveillance camera monitoring system. An effective closed loop control fuzzy logic algorithm is used to constantly correct wrong movements of the mobile robot using a feedback mechanism. The robot senses a black line on a white surface and endeavors itself accordingly to follow the track. A manual navigation system has also been designed to overrule the automatic navigation control of the robot to reposition itself back on the track whenever it strays from the path unintentionally. The fuzzy controller algorithm is an advanced method to ensure the line follower robot moves accurately on the track. It is a replacement control technique of traditional switching method. To fuzzifying the digital input data of four infrared sensor that detecting the line, the data is converted into error and delta error that represent the current and previous position of the robot relative to the line that it follows. There are nine base rules that have been created with two inputs which are error and delta error to the robot direction whether to go to the right, move forward or to the left. Then, for defuzzification, center of sum and centroid of area method have been used to calculate the defuzzied value using trapezium area formulae. Based on the comparison between both control techniques, it is found that the line following surveillance robot with fuzzy logic controller works faster than conventional switching method to complete the same task with the average oscillation length using the fuzzy logic controller is reduced to half.

Fuzzy Logic Control of Diesel Combustion Phasing Using Ion Current Signal

2012 ◽  
Author(s):  
Tamer Badawy ◽  
Nassim Khaled ◽  
Naeim Henein
Keyword(s):  
Fuzzy Logic ◽  
Diesel Engines ◽  
Fuzzy Logic Controller ◽  
Combustion Process ◽  
Open Loop ◽  
Operating Conditions ◽  
Ion Current ◽  
Injection System ◽  
Loop Control ◽  
Engine Life

Diesel engines have to meet stringent emissions standards without penalties in performance and fuel economy. This necessitated the use of elaborate after treatment devices to reduce the tail pipe emissions. In order to decrease the demand on the after treatment devices, there is a need to reduce the emissions in the formation stage during combustion. This requires a precise control of the phasing of the combustion process. Currently, diesel engines are controlled by pre-set open loop schedules that require extensive, time consuming and costly laboratory tests and calibration tasks to meet the production target goals which are stricter than the emission standards. Such goals are set as a safe guard against the deterioration during engine life cycle. This paper presents an incremental fuzzy logic controller that adjusts the combustion phasing as per desired targets to meet production goals over the engine life period. An ion current/ glow plug sensor and its circuit are used to produce a signal indicative of different combustion parameters. Signal conditioning and filtering are applied to improve the quality of ion current. The algorithm developed in this paper optimizes the ion current feed back to increase its reliability for stable engine control while maintaining fast controller response, and high accuracy. Experiments are carried out on a four cylinder, turbo-charged, 4.5L heavy duty diesel engine equipped with a common rail injection system and an open ECU. The response of the controller is evaluated from experimental data obtained by running the engine under different steady, and transient operating conditions. The results demonstrate the ability of the closed-loop control system in achieving the desired combustion phasing.

scholarly journals Robot navigation with obstacle avoidance in unknown environment

2018 ◽  
Vol 7 (4) ◽  
pp. 2410 ◽  
Author(s):  
Neerendra Kumar ◽  
Zoltán Vámossy
Keyword(s):  
Fuzzy Logic ◽  
Obstacle Avoidance ◽  
Fuzzy Inference System ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Training Data ◽  
Inference System ◽  
Pure Pursuit

In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.

scholarly journals Optimization of a Fuzzy Logic Controller for MR Dampers Using an Adaptive Neuro-Fuzzy Procedure

2016 ◽  
Vol 17 (05) ◽  
pp. 1740007 ◽  
Author(s):  
Manuel Braz-César ◽  
Rui Barros
Keyword(s):  
Fuzzy Logic ◽  
Structural Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Inference System ◽  
Mr Dampers ◽  
Neuro Fuzzy ◽  
Adaptive Control Systems ◽  
Robust Systems

Intelligent and adaptive control systems are naturally suitable to deal with dynamic uncertain systems with non-smooth nonlinearities; they constitute an important advantage over conventional control approaches. This control technology can be used to design powerful and robust controllers for complex vibration engineering problems such as vibration control of civil structures. Fuzzy logic based controllers are simple and robust systems that are rapidly becoming a viable alternative for classical controllers. Furthermore, new control devices such as magnetorheological (MR) dampers have been widely studied for structural control applications. In this paper, we design a semi-active fuzzy controller for MR dampers using an adaptive neuro-fuzzy inference system (ANFIS). The objective is to verify the effectiveness of a neuro-fuzzy controller in reducing the response of a building structure equipped with a MR damper operating in passive and semi-active control modes. The uncontrolled and controlled responses are compared to assess the performance of the fuzzy logic based controller.

Modeling Study of Engineering Ceramics WEDM Based on Fuzzy Logic

2013 ◽  
Vol 395-396 ◽  
pp. 1194-1198
Author(s):  
Xue Li ◽  
He Wang ◽  
Qiu Ping Shao
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Prediction Model ◽  
Technological Parameter ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Laboratory Finding ◽  
Engineering Ceramics ◽  
Processing Effects

relevance between technological parameter and processing effects in WEDM is extremely complex. In order to solve the difficulty of set up mathematical model for technological parameter and processing effects in engineering ceramics WEDM, an prediction model based on fuzzy logic is proposed through establish fuzzy controller by input/output fuzzification, fuzzy inference and settle fuzzy, whereas, build prediction model of engineering ceramics WEDM surface roughness change according to technological parameter utilizing established fuzzy logic controller in matlab. Laboratory finding shows, utilization of this model can precisely predict surface roughness under preset condition with relatively minus error, which further validate the reliability of this model. Keywords: Engineering ceramics, fuzzy logic, WEDM, processing optimization

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