scholarly journals Convergencia de la trayectoria lingüística en el espacio de estados de un Controlador Difuso aplicado a un Sistema No Lineal

2019 ◽  
pp. 23-31
Author(s):  
Pedro Téllez-Cuevas ◽  
Aldo Hernández-Luna ◽  
Claudia Yadira Luna-Carrasco
Keyword(s):  
Fuzzy Logic ◽  
Pid Control ◽  
Phase Plane ◽  
Fuzzy Controller ◽  
Equivalent Model ◽  
Pendulum System ◽  
Small Oscillations ◽  
Unit Impulse ◽  
Efficiency And Effectiveness ◽  
Input Variables

This article presents the design of a fuzzy controller to stability analysis base on the convergence of the linguistic trajectory in the state of space, for an inverted car-pendulum system, the fuzzy controller is of the Mamdani type and, it consists of 25 rules, 3 input variables and each one is composed of five memberships functions. The inverted car-pendulum system is represented by a non-linear model, which is obtained from a linearized equivalent model under the consideration of small oscillations. Results are validated against a PID control base on the trajectory on the phase plane to evaluate the efficiency and effectiveness of the fuzzy controller. The dynamic behavior of the system of both controllers is obtained with a unit impulse input, the simulation of the control system is developed on the MATLAB / SIMULINK software using the FUZZY LOGIC TOOLBOX, which allows to perform test and simulations, and also it shows results of graphic form.

Self Tuning Based Adaptive Fuzzy Logic Controller in Lab view for Sterilizing Equipments

2014 ◽  
Vol 3 (2) ◽  
pp. 84
Author(s):  
P. J. Ragu
Keyword(s):  
Fuzzy Logic ◽  
Temperature Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Lab View ◽  
Adaptive Fuzzy Logic ◽  
Self Tuning ◽  
Input Variables ◽  
Temperature Errors

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning  Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.

scholarly journals Using Fuzzy Logic to Adjust Plasma Concentration Based on a Two-Compartment Pharmacokinetic Model

2013 ◽  
Vol 341-342 ◽  
pp. 1171-1174
Author(s):  
Lei Zhao ◽  
Xin Ling Shi ◽  
Yu Feng Zhang ◽  
Ya Jie Liu
Keyword(s):  
Fuzzy Logic ◽  
Pharmacokinetic Model ◽  
Fuzzy Controller ◽  
Reference Value ◽  
Rule Base ◽  
Loop Control ◽  
Closed Loop Control System ◽  
Loop Control System ◽  
Input Variables ◽  
Adaptation Scheme

In this paper, a closed-loop control system was developed using fuzzy logic to adapt the parameters of a pharmacokinetic (PK) model. The system is based on a two-compartment PK model with first-order rate process of oral administration. The fuzzy logic adaptation scheme uses the error and the change in error as the input variables. The output variable of the fuzzy controller is the scaling factor to adjust the PK parameter. The fuzzy controller adjusted the real situation concentration to the reference value derived from the parameters of population mean data according to the established rule-base. The simulation results show that the controller provides good performance to adjust the concentration with the reference values.

Application of MR Damper in Structural Control Using Genetic Algorithm and Fuzzy Logic PID Tuning Methods

2007 ◽  
Author(s):  
Zhiqiang Gu ◽  
S. Olutunde Oyadiji
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Pid Control ◽  
Fitness Function ◽  
Control Variable ◽  
Mr Damper ◽  
Fuzzy Pid Control ◽  
Pid Tuning ◽  
Input Variables

In this paper, the application of MR damper in structure control using PID tuning based on genetic algorithms and fuzzy logic is developed and used to control the vibrations of based-excited linear and nonlinear single degree-of freedom-systems, which represent building structures subjected to earthquake loading and are excited by means of the El Centro earthquake. For GA method, the fitness function, which is based on these three constraint conditions: the control variable, the error and the rise-time, is chosen in order to apply genetic algorithms to optimize the coefficients of a PID controller. For the fuzzy control, the PID parameters are found using fuzzy self-tuning for the fuzzy logic relationship between the parameters of PID control and the controller input variables e and ec . The adaptive fuzzy PID control, which uses the error e and the change of the error ec as the input variables, modifies the three PID parameters on line based on the fuzzy control principle. The Bouc-Wen model of MR damper is used to simulate the MR damper characteristics. The simulation results demonstrate that the MR damper, used in conjunction with PID tuning control is effective for controlling the vibration of the structure.

scholarly journals Anti-Swing Fuzzy Controller Applied in a 3D Crane System

2012 ◽  
Vol 2 (2) ◽  
pp. 196-200 ◽  
Author(s):  
D. Antic ◽  
Z. Jovanovic ◽  
S. Peric ◽  
S. Nikolic ◽  
M. Milojkovic ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Complex Systems ◽  
Mathematical Models ◽  
Fuzzy Controller ◽  
Main Difficulty ◽  
Trial And Error ◽  
Nonlinear Mathematical Models ◽  
Highly Nonlinear ◽  
Experimental Laboratory ◽  
Input Variables

It is well known that fuzzy logic can be used in the control of complex systems described by highly nonlinear mathematical models. However, the main difficulty in the design of a fuzzy controller comes with the adjustment of the controller’s parameters that are usually determined by human experts’ knowledge or trial and error methods. In this paper, we describe an implementation of fuzzy logic in order to reduce oscillations during the positioning of a 3D crane system. The fuzzy controller’s structure is quite simple, requiring only two input variables. The proposed fuzzy controller has been applied to an experimental laboratory framework and results show that oscillations are significantly reduced.

Development of Energy Management System for a Parallel Hybrid Electric Vehicle Using Fuzzy Logic

2006 ◽  
Author(s):  
Amir Mohammad Fazeli ◽  
Ali Nabi ◽  
Farzad Rajaei Salmasi ◽  
Meisam Amiri
Keyword(s):  
Fuzzy Logic ◽  
Fuel Consumption ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Optimal Region ◽  
Vehicle Simulator ◽  
Input Variables

In this paper a fuzzy power controller for a parallel hybrid passenger car is developed in order to minimize its fuel consumption and optimize its components efficiencies. This is done by controlling the engine operating points in its optimal region and maintaining the SoC of batteries and electric motor operating point at the highest possible efficiency. Of course the designed control strategy must be able to obey the driver’s commands and achieve the PNGV regulations. The controller implemented in this paper is a Mamdani fuzzy logic controller which takes the batteries SoC, electric motor’s speed, ICE speed and the total demanded torque as its input variables and gives the ICE torque as its output. This controller has been simulated using the ADvanced VehIcle SimulatOR (ADVISOR) and its results have been compared to the default fuel mode fuzzy controller of ADVISOR. Simulation results show considerable improvement in the efficiency of the ICE and consequently, fuel consumption and acceleration performances.

Path Tracking Controller Design of Automatic Guided Vehicle Based on Four-Wheeled Omnidirectional Motion Model

2020 ◽  
Vol 17 (2) ◽  
pp. 7996-8010
Author(s):  
X. Wu ◽  
Y. Yang
Keyword(s):  
Control System ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Threshold Value ◽  
Position Angle ◽  
Path Tracking ◽  
Global Coordinate System ◽  
Automatic Guided Vehicle ◽  
Input Variables ◽  
Industrial Pc

This paper presents a new design of omnidirectional automatic guided vehicle based on a hub motor, and proposes a joint controller for path tracking. The proposed controller includes two parts: a fuzzy controller and a multi-step predictive optimal controller. Firstly, based on various steering conditions, the kinematics model of the whole vehicle and the pose (position, angle) model in the global coordinate system are introduced. Secondly, based on the modeling, the joint controller is designed. Lateral deviation and course deviation are used as the input variables of the control system, and the threshold value is switched according to the value of the input variable to realise the correction of the large range of posture deviation. Finally, the joint controller is implemented by using the industrial PC and the self-developed control system based on the Freescale minimum system. Path tracking experiments were made under the straight and circular paths to test the ability of the joint controller for reducing the pose deviation. The experimental results show that the designed guided vehicle has excellent ability to path tracking, which meets the design goals.

Probabilistic and Fuzzy based Efficient Routing Protocol for Mobile Ad Hoc Networks

2020 ◽  
Vol 13 (3) ◽  
pp. 422-432
Author(s):  
Madan Mohan Agarwal ◽  
Hemraj Saini ◽  
Mahesh Chandra Govil
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Routing Protocol ◽  
Queue Length ◽  
Ad Hoc ◽  
Fuzzy Controller ◽  
Second Phase ◽  
Path Distance ◽  
Fixed Threshold ◽  
Multiple Paths

Background: The performance of the network protocol depends on number of parameters like re-broadcast probability, mobility, the distance between source and destination, hop count, queue length and residual energy, etc. Objective: In this paper, a new energy efficient routing protocol IAOMDV-PF is developed based on the fixed threshold re-broadcast probability determination and best route selection using fuzzy logic from multiple routes. Methods: In the first phase, the proposed protocol determines fixed threshold rebroadcast probability. It is used for discovering multiple paths between the source and the destination. The threshold probability at each node decides the rebroadcasting of received control packets to its neighbors thereby reducing routing overheads and energy consumption. The multiple paths list received from the first phase and supply to the second phase that is the fuzzy controller selects the best path. This fuzzy controller has been named as Fuzzy Best Route Selector (FBRS). FBRS determines the best path based on function of queue length, the distance between nodes and mobility of nodes. Results: Comparative analysis of the proposed protocol named as "Improved Ad-Hoc On-demand Multiple Path Distance Vector based on Probabilistic and Fuzzy logic" (IAOMDV-PF) shows that it is more efficient in terms of overheads and energy consumption. Conclusion: The proposed protocol reduced energy consumption by about 61%, 58% and 30% with respect to FF-AOMDV, IAOMDV-F and FPAOMDV routing protocols, respectively. The proposed protocol has been simulated and analyzed by using NS-2.

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

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