Implementing Fuzzy Logic in the Control of a Biologically Inspired Robotic Cat Leg

2008 ◽  
Author(s):  
Anthony L. Crawford ◽  
Dean B. Edwards
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Vertical Displacement ◽  
Skeletal Structure ◽  
Governing Equations ◽  
Biologically Inspired ◽  
Logic Control ◽  
Forcing Function ◽  
Force Range

This research discusses the implementation of a fuzzy logic control system to drive the movement of a simplified cat leg model. The system’s movement in this paper addresses a planar motion where the model experiences a fixed horizontal velocity and a harmonic vertical displacement. The fuzzy logic (FL) controller applies membership functions to fuzzify the position and velocity errors and applies height defuzzification to generate the time dependant forcing function for the system’s horizontal and vertical governing equations. A PID controller is also applied as a benchmark for this research. Both controllers are optimized using the simplex method for which the FL controller performed just as well as the PID controller with more promise of accounting for the nonlinear influences that were neglected in this simplified cat leg model and requiring actuators with a lower required force range. This research provides the skeletal structure for which an effective total controller can be built on.

scholarly journals MATHEMATICAL ALGORITHM OF FUZZY LOGIC CONTROLLER FOR MULTILEVEL INVERTER CREATING VERTICAL DIVIDED VOLTAGE

2019 ◽  
Vol 59 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Erol Can
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Output Voltage ◽  
Fuzzy Logic Controller ◽  
Boost Converter ◽  
Multilevel Inverter ◽  
Logic Control ◽  
Multi Level

A 9-level inverter with a boost converter has been controlled with a fuzzy logic controller and a PID controller for regulating output voltage applications on resistive (R) and inductive (L), capacitance (C). The mathematical model of this system is created according to the fuzzy logic controlling new high multilevel inverter with a boost converter. The DC-DC boost converter and the multi-level inverter are designed and explained, when creating a mathematical model after a linear pulse width modulation (LPWM), it is preferred to operate the boost multi-level inverter. The fuzzy logic control and the PID control are used to manage the LPWM that allows the switches to operate. The fuzzy logic algorithm is presented by giving necessary mathematical equations that have second-degree differential equations for the fuzzy logic controller. After that, the fuzzy logic controller is set up in the 9-level inverter. The proposed model runs on different membership positions of the triangles at the fuzzy logic controller after testing the PID controller. After the output voltage of the converter, the output voltage of the inverter and the output current of the inverter are observed at the MATLAB SIMULINK, the obtained results are analysed and compared. The results show the demanded performance of the inverter and approve the contribution of the fuzzy logic control on multi-level inverter circuits.

Interval Type-2 Fuzzy Logic Control of Maglev Test Stand

2015 ◽  
Vol 759 ◽  
pp. 71-76
Author(s):  
Ireneusz Dominik
Keyword(s):  
Magnetic Field ◽  
Fuzzy Logic ◽  
Computational Complexity ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Test Stand ◽  
Logic Control ◽  
Interval Type

The paper contains a description of a research into applying classic algorithm of PID controller as well as advanced Type-2 Fuzzy logic controller to ensure stability of the levitating object in magnetic field. The implemented algorithm can handle uncertainties without increasing drastically the computational complexity, which is crucial in case of PLCs. The issues concerning the construction of the unit, where the experiments were carried out, are presented, as well as the characteristics of the object for different controllers.

Fuzzy Logic Controller Sebagai Pengatur Kecepatan Motor Shunt

2017 ◽  
Vol 8 (1) ◽  
pp. 11-16
Author(s):  
Machrus Ali ◽  
Budiman ◽  
Yanuangga Gala Hartlambang ◽  
4 Dwi Ajiatmo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Settling Time ◽  
Logic Control

Telah banyak penelitian pada motor shunt, karena kumparan penguat medan diparalel terhadap kumparan armatur. Motor DC shunt tidak terlalu membutuhkan banyak ruangan karena diameter kawat kecil, tetapi daya keluaran yang dihasilkan kecil karena arus penguatnya kecil. Metode Fuzzy Logic Control (FLC) telah banyak digunakan untuk optimasi suatu system. Penelitian ini membandingkan antara desain tanpa controller, dengan PID controller, dan FLC controller. Dari ketiga desain, menunjukkan bahwa desain control Fuzzy Logic Controller terbaik dari ketiga desain dengan besar putaran 300.0 rpm dengan settling time 1.702 detik dan besar Arus Rotor Motor Shunt (A) sebesar 1.9598 A, dengan setling time 1.323 detik. Penelitian ini akan dikembangkan menggunakan metode kecerdasan buatan lainnya

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

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