Lyapunov approach based design of a gain adaptive interval type-2 fuzzy controller for servo systems

2020 ◽  
pp. 1-19
Author(s):  
Ritu Rani De (Maity) ◽  
Rajani K. Mudi ◽  
Chanchal Dey
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Controller ◽  
Rule Base ◽  
Performance Study ◽  
Servo Systems ◽  
Lyapunov Approach ◽  
The Stability ◽  
Interval Type

This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a Quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.

scholarly journals Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Wen-Jer Chang ◽  
Yu-Wei Lin ◽  
Yann-Horng Lin ◽  
Chin-Lin Pen ◽  
Ming-Hsuan Tsai
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Actuator Saturation ◽  
Design Method ◽  
Fuzzy Model ◽  
The Stability ◽  
Interval Type ◽  
Takagi Sugeno

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

Interval Type-2 Fuzzy Logic Control of NM70 Shape Memory Actuator

2014 ◽  
Author(s):  
Ireneusz Dominik
Keyword(s):  
Fuzzy Logic ◽  
Shape Memory ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Logic Control ◽  
Interval Type ◽  
Nonlinear Plant

The main aim of this article is to present the usage of type-2 fuzzy logic controller to control a shape memory actuator. To enhance real-time performance simplified interval fuzzy sets were used. The algorithm was implemented in the ATmega32 microcontroller. The dedicated PC application was also built. The fuzzy logic controller type-2 was tested experimentally by controlling position of the shape memory alloy actuator NM70 which despite its small size distinguishes itself by its strength. The obtained results confirmed that type-2 fuzzy controller performed efficiently with a difficult to control nonlinear plant. The research also proved that interval type-2 controllers, which are a simplified version of the general type-2 controllers, are very efficient. They can handle uncertainties without increasing drastically the computational complexity. Experimental data comparison of the fuzzy logic controller type-2 with type-1 clearly indicates the superiority of the former, especially in reducing overshooting.

scholarly journals Three-Dimensional Fuzzy Control of Ultrasonic Cleaning

2021 ◽  
Vol 15 (3) ◽  
pp. 169-176
Author(s):  
Volodymyr Morkun ◽  
Olha Kravchenko
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Three Dimensional ◽  
Rule Base ◽  
Ultrasonic Cleaning ◽  
Distributed Parameters ◽  
Input And Output ◽  
Proposed Model ◽  
Interval Type

Abstract Consideration of ultrasonic cleaning as a process with distributed parameters enables reduction of power consumption. This approach is based on establishment of control over the process depending on fixed values of ultrasonic responses in set points. The initial intensity of radiators is determined using a three-dimensional (3D) interval type-2 fuzzy logic controller essentially created for processes with distributed parameters, as well as complex expert evaluation of the input data. The interval membership functions for the input and output data consider the space heterogeneity of ultrasonic cleaning. A rule base is formed, which is 2D and not dependent upon the number of input and output parameters. A model illustrating ultrasonic cleaning with a 3D interval type-2 fuzzy logic controller is designed. Comparative analysis of the output parameters of the proposed model and the traditional method indicates an increase in the energy efficiency by 41.17% due to application of only those ultrasonic radiators that are located next to the contamination.

An Interval Type 2 Fuzzy Logic Framework for Faster Evolutionary Design

2019 ◽  
Vol 16 (12) ◽  
pp. 5140-5148
Author(s):  
Sarabjeet Singh ◽  
Satvir Singh ◽  
Vijay Kumar Banga
Keyword(s):  
Fuzzy Logic ◽  
Graphics Processing Unit ◽  
Noisy Data ◽  
Parallel Execution ◽  
Rule Base ◽  
Processing Unit ◽  
Data Set ◽  
Interval Type ◽  
Graphics Processing

In this paper, a fast processing and efficient framework has been proposed to get an optimum output from a noisy data set of a system by using interval type-2 fuzzy logic system. Further, the concept of GPGPU (General Purpose Computing on Graphics Processing Unit) is used for fast execution of the fuzzy rule base on Graphics Processing Unit (GPU). Application of Whale Optimization Algorithm (WOA) is used to ascertain optimum output from noisy data set. Which is further integrated with Interval Type-2 (IT2) fuzzy logic system and executed on Graphics Processing Unit for faster execution. The proposed framework is also designed for parallel execution using GPU and the results are compared with the serial program execution. Further, it is clearly observed that the parallel execution rule base evolved provide better accuracy in less time. The proposed framework (IT2FLS) has been validated with classical bench mark problem of Mackey Glass Time Series. For non-stationary time-series data with additive gaussian noise has been implemented with proposed framework and with T1 FLS. Further, it is observed that IT2 FLS provides better rule base for noisy data set.

ON THE CONTROL OF TUMOR GROWTH VIA TYPE-1 AND INTERVAL TYPE-2 FUZZY LOGIC

2015 ◽  
Vol 15 (05) ◽  
pp. 1550083 ◽  
Author(s):  
SHAHRZAD GHOLAMI ◽  
ARIA ALASTY ◽  
HASSAN SALARIEH ◽  
MEHDI HOSSEINIAN-SARAJEHLOU
Keyword(s):  
Fuzzy Logic ◽  
Tumor Growth ◽  
Cancer Cells ◽  
Fuzzy Controller ◽  
Growth Control ◽  
Uncertain Information ◽  
Fuzzy Logic Systems ◽  
Interval Type

This paper deals with growth control of cancer cells population using type-1 and interval type-2 fuzzy logic. A type-1 fuzzy controller is designed in order to reduce the population of cancer cells, adjust the drug dosage in a manner that allows normal cells re-grow in treatment period and maintain the maximum drug delivery rate and plasma concentration of drug in an appropriate range. Two different approaches are studied. One deals with reducing the number of cancer cells without any concern about the rate of decreasing, and the other takes the rate of malignant cells damage into consideration. Due to the fact that uncertainty is an inherent part of real systems and affects controller efficacy, employing new methods of design such as interval type-2 fuzzy logic systems for handling uncertainties may be efficacious. Influence of noise on the system is investigated and the effect of altering free parameters of design is studied. Using an interval type-2 controller can diminish the effects of incomplete and uncertain information about the system, environmental noises, instrumentation errors, etc. Simulation results confirm the effectiveness of the proposed methods on tumor growth control.

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