A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.Comment: 14 page

Capturing hand tremors with a fuzzy logic wheelchair joystick controller

We have designed and built a fuzzy logic wheelchair controller which minimizes the effect of Multiple Sclerosis and tremors. The aim of our project has been to give people with Multiple Sclerosis better control of an electric wheelchair by removing tremors from the joystick signal. The system intercepts the signal from the joystick and then passes it through the fuzzy logic controller. The fuzzy logic identify and eliminate erratic or unusual movements, employing a history mechanism to determine what "unusual" is. The fuzzy logic than outputs a signal which closely represents the intent of the user. This paper reports on the experiments conducted with our prototype wheelchair, using test volunteers with MS, as well as on the design of a new fuzzy controller. Also, we give a brief overview of the variety of recorded tremors. We show that those who have the most severe MS tremors benefit from the system, and are able to control the wheelchair safely

Simulation of speed control brushless DC motor using gaussian fuzzy logic controller

This paper presents a control scheme of a Fuzzy Logic for the brushless direct current (BLDC) motor drives. The BLDC motor has some advantages compare to others type of motors. However, the nonlinearity of this motor drive characteristics cause it is difficult to handle using conventional proportional-integral-differential (PID) controller. In order to overcome this main problem, Fuzzy Logic controller with a Gaussian membership function is developed. The mathematical model of BLDC motor is derived. The controller is designed to tracks variations of speed references and stabilizes the output speed during load variations. The effectiveness of the proposed method is verified by develop simulation model in Matlab Simulink software. The simulation results show that the proposed Fuzzy Logic controller (FLC) produce significant improvement control performance compare to the PID controller for both condition controlling speed reference variations and load disturbance variations

Comparison of DC motor speed control performance using fuzzy logic and model predictive control method

The main target of this paper is to control the speed of DC motor by comparing the actual and the desired speed set point. The DC motor is designed using Fuzzy logic and MPC controllers. The comparison is made between the proposed controllers for the control target speed of the DC motor using square and white noise desired input signals with the help of Matlab/Simulink software. It has been realized that the design based on the fuzzy logic controller track the set pointwith the best steady state and transient system behavior than the design with MPC controller. Finally, the comparative simulation result prove the effectiveness of the DC motor with fuzzy logic controller

Design and implementation of fuzzy logic controllers

The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC

Sistem Kendali Hybrid Pid - Logika Fuzzy Pada Pengaturan Kecepatan Motor Dc

PID Fuzzy Logic Controller System for DC Motor Speed Control. A good controller system must have resilience todisturbance and must be able to response quickly and accurately. Problem usually appears when PID controller systemwas built sensitively hence the system's respon to the disturbance will yield big overshot/undershot then the possibilityof oscillation to be happened is excelsior. When the controller system was built insensitively, the overshot/undershotwill be small but the recovery time will be longer. Hybrid controller system could overcome those problems bycombining PID control system with fuzzy logic. The main control of this system is PID controller while the fuzzy logicacts to reduce an overshot/undershot and a recovery time. The fuzzy logic controller is designed with two input errorand delta error and one output of the motor speed. The output of fuzzy logic controller should be only half of the PIDcontroller for limiting entirely fuzzy output. This hybrid system design has a better respon time controller system thanPID controller without fuzzy logic

Fuzzy efficiency optimization of AC induction motors

This paper describes the early states of work to implement a fuzzy logic controller to optimize the efficiency of AC induction motor/adjustable speed drive (ASD) systems running at less than optimal speed and torque conditions. In this paper, the process by which the membership functions of the controller were tuned is discussed and a controller which operates on frequency as well as voltage is proposed. The membership functions for this dual-variable controller are sketched. Additional topics include an approach for fuzzy logic to motor current control which can be used with vector-controlled drives. Incorporation of a fuzzy controller as an application-specific integrated circuit (ASIC) microchip is planned