71,658 research outputs found

    Sistem Kendali Fuzzy Bertipe-2 Interval dengan Struktur Adaptif Beracuan Model

    Get PDF
    Adaptive fuzzy controller is a fuzzy controller that has ability to change its parameters when the plant's operating conditions vary. In this paper, design and implementation of model reference adaptive fuzzy control are presented. Interval type-2 fuzzy logic controller with PD-like action is employed and its performance is studied. The fuzzy controller structure is applied to control an inverted pendulum. Simulation and experimental study shows that by using similar membership function, fuzzy rules and scaling, interval type-2 adaptive fuzzy logic controller provides better control system performance compared to type-1 fuzzy controller

    Model Reference Adaptive Fuzzy Control

    Get PDF
    Fuzzy control is a model-free linguistic control (if-then rules), which is easy to understand and provides nonlinear controllers for nonlinear systems. In recent years, some fuzzy controllers with an adaptive mechanism for unknown systems have been studied. In these studies, the parameters of a fuzzy controller are adjusted by some experience of human opereators or adaptive lows with some if-then rules. But the stability of the control system which is constructed by the plant and the fuzzy controller has not been analysed in most of these studies In this paper, we propose a class of Model Reference Adaptive Fuzzy Controllers for nonlinear systems. This class of controllers are the fuzzy controllers with the structure of the direct adaptive control system which can directly stabilize tracking error e. Finally, we derive the stability conditions (the adaptive laws) for the fuzzy controller for nonlinear systems by taking quadratic parameter error φ_i as the Lyapunov function V

    ADAPTIVE FUZZY SETTING REFERENCE MODEL FOR HOIST CRANE MOVEMENT

    Get PDF
    This journal describes control system designing of Hoist Crane movement using Adaptive Fuzzy Reference Model (Adaptive Fuzzy Control Reference Model) for non-linear movement controlling. Non linear movement controlling in hoist crane selected to test adaptive fuzzy control rule effectiveness through simulation is non linear hoist crane movement model which basically is unstable. Adaptive Fuzzy Control rule is derived using Lyapunov theory based on linearization model from hoist crane movement. Reference model selected was stabilized linear model. Then simulation was performed to observe MRAFC performance on non linear model. Full state feedback control through simulation has been shown not able to stabilize hoist crane movement. MRAFC is able to perform better, even for cases where controlling parametric model was uncertain or changing over time (time-varying). Point to note in the designing was how to select reference model as wise as possible because it affect control system stability leve

    Adaptive Type-2 Fuzzy Logic Control of a Bioreactor

    Get PDF
    Two adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI controller, where the output scaling factor is adjusted online by fuzzy rules according to the current trend of the controlled process. The performance of a type-2 fuzzy logic controller with 49 rules is used as reference

    Modeling And Control Of 2-DOF Underwater Planar Manipulator.

    Get PDF
    This paper investigates the performance of the fuzzy model reference adaptive control applied on 2-dof underwater planar manipulator (MIMO system). Takagi-Sugeno fuzzification is chosen for the fuzzy system. Proportional-integral update law is used in the adjustment mechanism to obtain a fast parameters adaption

    Neizrazito adaptivno upravljanje silom dodira slijednih mehanizama s jednim stupnjem slobode gibanja

    Get PDF
    The paper presents position/force control with a completely fuzzified adaptive force control system for the single degree of freedom servo mechanisms. The proposed force control scheme contains an adaptive fuzzy force controller and a subordinated fuzzy velocity controller. By using a second-order reference model, a model reference-based fuzzy adaptation mechanism is able to keep the error between the model and system output responses within desired limits. The results obtained by computer simulations indicate a stable performance of the force control system for a wide range of environment stiffness variations. The proposed adaptive force control method has also been effective in case of a contact with a rough surface or a complex form workpiece.Članak prikazuje upravljanje položajem/silom dodira slijednog mehanizma s jednim stupnjem slobode gibanja korištenjem neizrazitog adaptivnog sustava upravljanja silom. Predložena shema upravljanja silom dodira sadrži adaptivni neizraziti regulator sile i podređeni neizraziti regulator brzine vrtnje. Koristeći referentni model drugog reda, neizraziti na modelu zasnovani adaptacijski mehanizam u stanju je držati razliku između odziva modela i odziva sustava u zadanim granicama. Rezultati dobiveni numeričkim simulacijama pokazuju stabilno vladanje sustava upravljanja silom dodira za široki raspon varijacija krutosti okoline. Predložena metoda adaptivnog upravljanja silom se pokazala uspješnom i u slučaju dodira s neravnom površinom ili s radnim predmetom složena oblika

    FUZZY MODEL REFERENCE ADAPTIVE CONTROL OF VELOCITY SERVO SYSTEM

    Get PDF
    The Implementation of fuzzy model reference adaptive control of a velocity servo system is analysed in this paper. Designing the model reference adaptive control (MRAC) and the problem of choosing adaptation gain is considered. Tuning the adaptation gain by fuzzy logic subsystem and a simple synthesis procedure of fuzzy MRAC are proposed. Several simulation runs show the advantages of fuzzy MRAC approach. Experimental validation on laboratory speed servo is realized by the acquisition system. Results confirm benefits of proposed controller comparing to standard MRAC

    Design of stable adaptive fuzzy control.

    Get PDF
    by John Tak Kuen Koo.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 217-[220]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- "Robust, Adaptive and Fuzzy Control" --- p.2Chapter 1.3 --- Adaptive Fuzzy Control --- p.4Chapter 1.4 --- Object of Study --- p.10Chapter 1.5 --- Scope of the Thesis --- p.13Chapter 2 --- Background on Adaptive Control and Fuzzy Logic Control --- p.17Chapter 2.1 --- Adaptive control --- p.17Chapter 2.1.1 --- Model reference adaptive systems --- p.20Chapter 2.1.2 --- MIT Rule --- p.23Chapter 2.1.3 --- Model Reference Adaptive Control (MRAC) --- p.24Chapter 2.2 --- Fuzzy Logic Control --- p.33Chapter 2.2.1 --- Fuzzy sets and logic --- p.33Chapter 2.2.2 --- Fuzzy Relation --- p.40Chapter 2.2.3 --- Inference Mechanisms --- p.43Chapter 2.2.4 --- Defuzzification --- p.49Chapter 3 --- Explicit Form of a Class of Fuzzy Logic Controllers --- p.51Chapter 3.1 --- Introduction --- p.51Chapter 3.2 --- Construction of a class of fuzzy controller --- p.53Chapter 3.3 --- Explicit form of the fuzzy controller --- p.57Chapter 3.4 --- Design criteria on the fuzzy controller --- p.65Chapter 3.5 --- B-Spline fuzzy controller --- p.68Chapter 4 --- Model Reference Adaptive Fuzzy Control (MRAFC) --- p.73Chapter 4.1 --- Introduction --- p.73Chapter 4.2 --- "Fuzzy Controller, Plant and Reference Model" --- p.75Chapter 4.3 --- Derivation of the MRAFC adaptive laws --- p.79Chapter 4.4 --- "Extension to the Multi-Input, Multi-Output Case" --- p.84Chapter 4.5 --- Simulation --- p.90Chapter 5 --- MRAFC on a Class of Nonlinear Systems: Type I --- p.97Chapter 5.1 --- Introduction --- p.98Chapter 5.2 --- Choice of Controller --- p.99Chapter 5.3 --- Derivation of the MRAFC adaptive laws --- p.102Chapter 5.4 --- Example: Stabilization of a pendulum --- p.109Chapter 6 --- MRAFC on a Class of Nonlinear Systems: Type II --- p.112Chapter 6.1 --- Introduction --- p.113Chapter 6.2 --- Fuzzy System as Function Approximator --- p.114Chapter 6.3 --- Construction of MRAFC for the nonlinear systems --- p.118Chapter 6.4 --- Input-Output Linearization --- p.130Chapter 6.5 --- MRAFC with Input-Output Linearization --- p.132Chapter 6.6 --- Example --- p.136Chapter 7 --- Analysis of MRAFC System --- p.140Chapter 7.1 --- Averaging technique --- p.140Chapter 7.2 --- Parameter convergence --- p.143Chapter 7.3 --- Robustness --- p.152Chapter 7.4 --- Simulation --- p.157Chapter 8 --- Application of MRAFC scheme on Manipulator Control --- p.166Chapter 8.1 --- Introduction --- p.166Chapter 8.2 --- Robot Manipulator Control --- p.170Chapter 8.3 --- MRAFC on Robot Manipulator Control --- p.173Chapter 8.3.1 --- Part A: Nonlinear-function feedback fuzzy controller --- p.174Chapter 8.3.2 --- Part B: State-feedback fuzzy controller --- p.182Chapter 8.4 --- Simulation --- p.186Chapter 9 --- Conclusion --- p.199Chapter A --- Implementation of MRAFC Scheme with Practical Issues --- p.203Chapter A.1 --- Rule Generation by MRAFC scheme --- p.203Chapter A.2 --- Implementation Considerations --- p.211Chapter A.3 --- MRAFC System Design Procedure --- p.215Bibliography --- p.21

    Multi-Input Multi-Output Adaptive Control of 9-DOF Hyper-Redundant Robotic Arm

    Get PDF
    In this paper, multi-input multi-output (MIMO) direct adaptive torque controller is presented that uses conventional fuzzy system to provide asymptotic end-effector tracking of a reference path for a 9-DOF hyper redundant manipulator dynamic model. As a result, MIMO adaptive controller, which inputs torque of each joint to control end-effector dynamic variables, can highly improve the robotic performance considering both its kinetics and dynamics while executing motion control or tracking a reference in work space. Also, it increases the robustness with respect to disturbance, sensor noise and poorly understood dynamic model. The efficacy of our control algorithm affects the accuracy , stability and robustness of both motion control and path tracking.https://ecommons.udayton.edu/stander_posters/1788/thumbnail.jp
    corecore