scholarly journals Adaptive Backstepping Fuzzy Control Based on Type-2 Fuzzy System

2012 ◽  
Vol 2012 ◽  
pp. 1-27 ◽  
Author(s):  
Ll Yi-Min ◽  
Yue Yang ◽  
Li Li
Keyword(s):  
Fuzzy Control ◽  
Fuzzy System ◽  
External Disturbance ◽  
Approximation Error ◽  
Adaptive Backstepping ◽  
Control Approach ◽  
Type Reduction ◽  
Adaptive Laws

A novel indirect adaptive backstepping control approach based on type-2 fuzzy system is developed for a class of nonlinear systems. This approach adopts type-2 fuzzy system instead of type-1 fuzzy system to approximate the unknown functions. With type-reduction, the type-2 fuzzy system is replaced by the average of two type-1 fuzzy systems. Ultimately, the adaptive laws, by means of backstepping design technique, will be developed to adjust the parameters to attenuate the approximation error and external disturbance. According to stability theorem, it is proved that the proposed Type-2 Adaptive Backstepping Fuzzy Control (T2ABFC) approach can guarantee global stability of closed-loop system and ensure all the signals bounded. Compared with existing Type-1 Adaptive Backstepping Fuzzy Control (T1ABFC), as the advantages of handling numerical and linguistic uncertainties, T2ABFC has the potential to produce better performances in many respects, such as stability and resistance to disturbances. Finally, a biological simulation example is provided to illustrate the feasibility of control scheme proposed in this paper.

scholarly journals Comparison of Type-1 and Type-2 Fuzzy Systems for Mineralization of Bioprinted Bone

2021 ◽  
Author(s):  
Ashkan Sedigh ◽  
Mohammad-R. Akbarzadeh-T ◽  
Ryan E. Tomlinson
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Approximation Error ◽  
Culture Period ◽  
Biological Parameters ◽  
High Uncertainty ◽  
Pressure Nozzle

ABSTRACTBioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinting parameters, such as pressure, nozzle size, and speed, have a large influence on the quality of the bioprinted construct. Moreover, cell suspension density, cell culture period, and other critical biological parameters directly impact the biological function of the final product. Therefore, an approximation model that can be used to find the values of bioprinting parameters that will result in optimal bioprinted constructs is highly desired. Here, we propose type-1 and type-2 fuzzy systems to handle the uncertainty and imprecision in optimizing the input values. Specifically, we focus on the biological parameters, such as culture period, that can be used to maximize the output value (mineralization volume). To achieve a more accurate approximation, we have compared a type-2 fuzzy system with a type-1 fuzzy system using two levels of uncertainty. We hypothesized that type-2 fuzzy systems may be preferred in biological systems, due to the inherent vagueness and imprecision of the input data. Here, our results demonstrate that the type-2 fuzzy system with a high uncertainty boundary (30%) is superior to type-1 and type-2 with low uncertainty boundary fuzzy systems in the overall output approximation error for bone bioprinting inputs.

scholarly journals Uncertainty Avoider Interval Type II Defuzzification Method

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Sadegh Aminifar
Keyword(s):  
Fuzzy System ◽  
Uncertainty Avoidance ◽  
System Response ◽  
Type Reduction ◽  
Meaningful Relationship ◽  
System Output ◽  
Computational Bottleneck ◽  
Interval Type

One of the IT2FS (interval type-2 fuzzy system) defuzzification methods uses the iterative KM algorithm. Because of the iterative nature of KM-type reduction, it may be a computational bottleneck for the real-time applications of IT2FSs. There are several other interval type-2 defuzzification methods suffering from lack of meaningful relationship between membership function uncertainties and changing of system output due to lack of clearly defined variables related to uncertainty in their methods. In this paper, a new approach for IT2FS defuzzification is presented by reconfiguring interval type-2 fuzzy sets and how uncertainties are present in them. This closed-formula method provides meaningful relation between the presence of uncertainty and its effect on system output. This study investigates uncertainty avoidance that the output of IT2FS obtained by centroid or bisection methods in comparison with type-1 fuzzy system (T1FLS) moves to points with less uncertainty. Uncertainty can enter into T1FSs and affect system response. Finally, for proving the affectivity of the proposed defuzzification method and uncertainty avoidance, several investigations are done and a prototype two-input one-output IT2FS MATLAB code is enclosed.

Adaptive H8 Fuzzy Control for a Class of Uncertain Discrete-Time Nonlinear Systems

2010 ◽  
Vol 1 (4) ◽  
pp. 48-67
Author(s):  
Tsung-Chih Lin ◽  
Shuo-Wen Chang
Keyword(s):  
Fuzzy Control ◽  
Discrete Time ◽  
Fuzzy Controller ◽  
Output Feedback Control ◽  
Training Data ◽  
Reference Trajectory ◽  
Control Approach ◽  
Adaptive Laws ◽  
Interval Type

In this paper, an adaptive interval type-2 fuzzy controller is proposed for a class of unknown nonlinear discrete-time systems with training data corrupted by noise or rule uncertainties involving external disturbances. Adaptive interval type-2 fuzzy control scheme and control approach are incorporated to implement the main objective of controlling the plant to track a reference trajectory. The Laypunov stability theorem has been used to testify the asymptotic stability of the whole system and the free parameters of the adaptive fuzzy controller can be tuned on-line by an output feedback control law and adaptive laws. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. The simulation example is given to confirm validity and tracking performance of the advocated design methodology.

scholarly journals Design Nonlinear Model Reference with Fuzzy Controller for Nonlinear SISO Second Order Systems

2019 ◽  
Vol 9 (4) ◽  
pp. 2491
Author(s):  
Ekhlas H. Karam ◽  
Nasir A. Al-Awad ◽  
Noor Safaa Abdul-Jaleel
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Controller ◽  
External Disturbance ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Model Reference ◽  
Single Input Single Output ◽  
Interval Type

<p>Model reference controller is considering as one of the most useful controller to specific performance of systems where the desired output is produced for a given input. This system used the difference between the outputs of the plant and the desired model by comparing them to produce the signals of the control. This paper focus on design a model reference controller (MRC) combined with (type-1 and interval type-2) fuzzy control scheme for single input-single output (SISO) systems under uncertainty and external disturbance. The model reference controller is designed firstly without fuzzy scheme based on an optimal desired model and Lyapunov stability theory. Then a (type-1 and Interval type-2) fuzzy controller Takagi-Sugeno type is combine with the suggested MRC in order to enhance the performer of it, the common parts between the two fuzzy systems such as: fuzzifier, inference engine, fuzzy rule-base and defuzzifier are illustrated. In this paper the proposed controller is applied to controla (SISO) inverted pendulum sustem and the Matlab R2015 software is used to carry out two simulation cases for the overall controlled scheme. The obtained results for the two cases show that the proposed MRC with both fuzzy control schemes have acceptable performance, but it have better performance with the interval type-2 fuzzy scheme.</p>

Design and application of an adaptive backstepping sliding mode controller for a six-DOF quadrotor aerial robot

Robotica ◽  
2018 ◽  
Vol 36 (11) ◽  
pp. 1701-1727 ◽  
Author(s):  
Mohd Ariffanan Mohd Basri
Keyword(s):  
Sliding Mode Control ◽  
Position Control ◽  
Sliding Mode ◽  
External Disturbance ◽  
Adaptive Backstepping ◽  
External Disturbances ◽  
Control Approach ◽  
Mode Control ◽  
Aerial Robot ◽  
Six Dof

SUMMARYThe quadrotor aerial robot is a complex system and its dynamics involve nonlinearity, uncertainty, and coupling. In this paper, an adaptive backstepping sliding mode control (ABSMC) is presented for stabilizing, tracking, and position control of a quadrotor aerial robot subjected to external disturbances. The developed control structure integrates a backstepping and a sliding mode control approach. A sliding surface is introduced in a Lyapunov function of backstepping design in order to further improve robustness of the system. To attenuate a chattering problem, a saturation function is used to replace a discontinuous sign function. Moreover, to avoid a necessity for knowledge of a bound of external disturbance, an online adaptation law is derived. Particle swarm optimization (PSO) algorithm has been adopted to find parameters of the controller. Simulations using a dynamic model of a six degrees of freedom (DOF) quadrotor aerial robot show the effectiveness of the approach in performing stabilization and position control even in the presence of external disturbances.

scholarly journals Comparative Study of Type-1 and Type-2 Fuzzy System in Decision Support System

2018 ◽  
Vol 6 (3) ◽  
Author(s):  
Humaira Humaira ◽  
Yance Sonatha ◽  
Cipto Prabowo ◽  
Hidra Amnur ◽  
Rita Afyenni
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Comparative Study ◽  
Support System ◽  
Fuzzy System

scholarly journals Modeling of Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) for Power Prediction

2021 ◽  
Author(s):  
Jafar Tavoosi ◽  
Amir Abolfazl Suratgar ◽  
Mohammad Bagher Menhaj ◽  
Amir Mosavi ◽  
Ardashir Mohammadzadeh ◽  
...  
Keyword(s):  
Solar Cell ◽  
Wind Turbine ◽  
Fuzzy System ◽  
Back Propagation ◽  
Optimal Number ◽  
Membership Functions ◽  
Back Propagation Algorithm ◽  
Power Prediction ◽  
Type Reduction

Not only does this paper present a novel type-2 fuzzy system for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also it brings forward an exquisite technique to acquire an optimal number of membership functions and the corresponding rules. It proposes a seven-layered NCPRT2FS. For fuzzification in the first two layers, Gaussian type-2 fuzzy membership functions with uncertainty in the mean, are exploited. The third layer comprises rule definition and the forth one embeds fulfillment of type reduction. The three last remained layers are the ones in which resultant left–right firing points, two end-points and output all get assessed correspondingly. It should not be neglected off the nutshell that recurrent feedback at the fifth layer exerts delayed outputs ameliorating efficiency of the suggested NCPRT2FS. Later in the paper, a modern structural learning, established on type-2 fuzzy clustering, is held forth. An adaptively rated learning back-propagation algorithm is extended to adjust the parameters ensuring the convergence as well. Eventually, solar cell photo-voltaic and wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge so persuasive.

Interpolation functions of interval Type-2 fuzzy systems

2021 ◽  
pp. 1-17
Author(s):  
Shan Zhao ◽  
Zhao Li
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Classical Type ◽  
Data Sets ◽  
Single Output ◽  
Type Reduction ◽  
Single Input ◽  
Interval Type ◽  
Interpolation Functions

The interpolation functions of interval type-2 fuzzy systems and their universal approximation are investigated in this paper. Two types of fuzzification methods are designed to construct the antecedents and consequents of the type-2 inference rules. Then the properties of the fuzzy operator and the type-reduction algorithm are used to integrate all parts of the fuzzy system. Interpolation functions of interval type-2 fuzzy systems, which are proved to be universal approximators, are obtained based on three models, namely single input and single output, double inputs and single output, and multiple inputs and single output. The proposed approach is applied to approximate experiments of dynamic systems so as to evaluate the system performance. The system parameters are optimized by the QPSO algorithm. Experimental results for several data sets are given to show the approximation performances of the proposed interpolation functions are better than those of the interpolation function of the classical type-1 fuzzy system.

Sign in / Sign up

Export Citation Format

Share Document