scholarly journals Modeling, Simulation, and Stabilization of Two Wheels Inverted Pendulum Robot Using Hybrid Fuzzy Control

2021 ◽  
Vol 3 (3) ◽  
pp. 93-98
Author(s):  
Made Rahmawaty
Keyword(s):  
Fuzzy Control ◽  
Inverted Pendulum ◽  
Position Control ◽  
Fuzzy Model ◽  
Pole Placement ◽  
Nonlinear Controller ◽  
Modeling Simulation ◽  
Logic Control ◽  
Movement Characteristics ◽  
Takagi Sugeno

Two wheels inverted pendulum robot has the same characteristics as inverted pendulum, which are unstable and nonlinear. Nonlinear systems can often be linearized by approximating them by a linear system obtained by expanding the nonlinear solution in a series, and then linear techniques can be used. Fuzzy logic control is the famous nonlinear controller that has been used by researchers to analyze the performance of a system due to the easiness to understand the nature of the controller. This research discusses about two wheels inverted pendulum robot design using hybrid fuzzy control. There are two types of fuzzy control, namely Fuzzy Balanced Standing Control (FBSC) to maintain stability and Fuzzy Traveling and Position Control (FTPC) to maintain position. Based on Takagi-Sugeno (T-S) fuzzy model on two wheels inverted pendulum robot, FBSC control used Parallel Distributed Compensation (PDC) with pole placement technic. Based on two wheels inverted pendulum robot movement characteristics, FTPC was designed using Mamdani Fuzzy architecture. FTPC control is used to help FBSC to maintain robot stability and to adjust to the desired position. Simulation result shows that controller for two wheels inverted pendulum robot can stabilize pendulum angle in 0 radian and close to the desired position

Finite time takagi-sugeno fuzzy control for hydro-turbine governing system

2016 ◽  
Vol 24 (5) ◽  
pp. 1001-1010 ◽  
Author(s):  
Bin Wang ◽  
Jianyi Xue ◽  
Fengjiao Wu ◽  
Delan Zhu
Keyword(s):  
Fuzzy Control ◽  
Finite Time ◽  
Control Method ◽  
Fuzzy Model ◽  
Schur Complement ◽  
Stability Control ◽  
Linear Matrix ◽  
Hydro Turbine ◽  
Governing System ◽  
Takagi Sugeno

In this study, a robust finite time Takagi-Sugeno fuzzy control method for hydro-turbine governing system (HTGS) is investigated. Firstly, the mathematical model of HTGS is introduced, and on the basis of Takagi-Sugeno (T-S) fuzzy rules, the T-S fuzzy model of HTGS is presented. Secondly, based on finite time stability theory, a novel finite time Takagi-Sugeno fuzzy control method is designed for the stability control of HTGS. Thirdly, the relatively loose sufficient stability condition is acquired, which could be transformed into a group of linear matrix inequalities (LMIs) via Schur complement as well as the strict mathematical derivation is given. Furthermore, the control method could resist random disturbances, which shows the good robustness. Simulation results indicate the designed finite time T-S fuzzy control scheme works well compared with the conventional method. The approach proposed in this paper is easy to implement and also provides reference for relevant hydropower systems.

scholarly journals An interval observer design for uncertain nonlinear systems based on the T-S fuzzy model

2017 ◽  
Vol 27 (3) ◽  
pp. 397-407 ◽  
Author(s):  
Yamina Menasria ◽  
Hichem Bouras ◽  
Nasreddine Debbache
Keyword(s):  
Nonlinear Systems ◽  
Estimation Error ◽  
Fuzzy Model ◽  
Observer Design ◽  
Pole Placement ◽  
Dynamic State ◽  
New Approach ◽  
Dynamic State Estimation ◽  
Interval Observer ◽  
Takagi Sugeno

AbstractA new approach to build an interval observer for nonlinear uncertain systems is presented in this paper. Nonlinear systems modeled in the Takagi-Sugeno (T-S) form are studied. A T-S proportional observer is first issued by pole-placement and LMI tools. Secondly, time-varying change of coordinates for each dynamic state estimation error is used to design an interval observer. The system state bounds are then directly deduced.

Stable nonlinear controller design for a Takagi-Sugeno fuzzy model

2001 ◽  
Vol 5 (1) ◽  
pp. 20-25
Author(s):  
Choon-Young Lee ◽  
Tae-Dok Eom ◽  
Ju-Jang Lee
Keyword(s):  
Controller Design ◽  
Fuzzy Model ◽  
Nonlinear Controller ◽  
Nonlinear Controller Design ◽  
Takagi Sugeno

scholarly journals Analysis, Filtering, and Control for Takagi-Sugeno Fuzzy Models in Networked Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Sunjie Zhang ◽  
Zidong Wang ◽  
Jun Hu ◽  
Jinling Liang ◽  
Fuad E. Alsaadi
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Systems ◽  
Nonlinear Function ◽  
Fuzzy Model ◽  
Dynamic Structure ◽  
Future Research ◽  
Compact Set ◽  
Great Success ◽  
Randomly Occurring Uncertainties ◽  
Takagi Sugeno

The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.

Nonlinear control design using Takagi-Sugeno fuzzy applied to under-actuated visual servo system

2020 ◽  
Vol 42 (15) ◽  
pp. 2969-2983
Author(s):  
Vimala Kumari Jonnalagadda ◽  
Vinodh Kumar Elumalai ◽  
Harvir Singh ◽  
Amit Prasad
Keyword(s):  
Fuzzy Control ◽  
Moving Objects ◽  
Direct Method ◽  
Fuzzy Model ◽  
Control Design ◽  
Linear Matrix ◽  
Nonlinear Stabilization ◽  
Ts Fuzzy Model ◽  
Takagi Sugeno ◽  
Plate System

This paper presents the Takagi-Sugeno (TS) fuzzy control design for nonlinear stabilization and tracking control of a ball on plate system. To deal with the plant nonlinearity and the fuzzy convergence issue, we formulate the parallel distributed compensator (PDC) TS fuzzy model to characterize the global behaviour of the nonlinear system and synthesize a feasible control framework using a velocity compensation scheme. The nonlinear dynamics of the ball on plate system is obtained using the Euler-Lagrangian energy based approach. To identify the moving objects in the video stream, a background subtraction algorithm using thresholding technique is formulated. Moreover, the stability analysis of the TS fuzzy control is reduced to linear matrix inequality (LMI) problem and solved using the Lyapunov direct method. The potential benefits of the proposed control structure for real time test cases are experimentally assessed using hardware in loop (HIL) testing on a ball on plate system. Experimental results substantiate that the TS fuzzy scheme can significantly improve not only the tracking performance but also the robustness of the closed loop system.

Fuzzy control design for nonlinear impulsive switched systems using a nonlinear Takagi-Sugeno fuzzy model

2020 ◽  
Vol 42 (9) ◽  
pp. 1700-1711
Author(s):  
Mohsen Ghalehnoie ◽  
Mohammad-Reza Akbarzadeh-Tootoonchi ◽  
Naser Pariz
Keyword(s):  
Nonlinear System ◽  
Fuzzy Control ◽  
Switched Systems ◽  
Fuzzy Model ◽  
Control Design ◽  
Small Region ◽  
Local Sector ◽  
Impulsive Switched Systems ◽  
Takagi Sugeno ◽  
Ultimate Bound

This paper deals with the fuzzy control design for nonlinear impulsive switched systems modeled by a novel nonlinear Takagi-Sugeno (T-S) fuzzy structure. To model, this structure only uses some of the nonlinearities as premise variables. So, the derived model has fewer rules compared with the traditional T-S fuzzy models that utilize standard local sector nonlinearity; and accordingly, the number of stability/stabilization conditions is sharply reduced. In our structure, considering only a part of nonlinearities as premise variables causes that some of the local models in the consequent part of the fuzzy rules be nonlinear but with less complexity than the original nonlinear system. As a result, the feasibility of our model’s stability criteria is more than those that one can directly establish for the original nonlinear system. Besides, unlike the existing methods in the literature of impulsive switched systems that aim to permanently reduce the value of Lyapunov function candidates, this paper takes into account this process until trajectories reach a sufficient small region containing the origin. Then, within this region, the size of Lyapunov functions is just controlled at impulse instants. This strategy is useful when there are non-vanishing impulses and/or uncertainties, and it is more relaxed when the goal is ultimate bound stability. Finally, to achieve the stabilizing control signal that ensures practical control issues along with smallest ultimate bound and widest region of attraction, this paper also proposes an optimization problem with linear and bilinear constraints. The simulation results represent the performance of the proposed method.

scholarly journals T-S Fuzzy Control of Uncertain Chaotic Vibration

2012 ◽  
Vol 19 (3) ◽  
pp. 379-389 ◽  
Author(s):  
Abdelkrim Boukabou ◽  
Noura Mansouri
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Chaos Control ◽  
Chaotic Systems ◽  
Fuzzy Model ◽  
Control Approach ◽  
Chaotic Vibration ◽  
Uncertain Chaotic Systems ◽  
Simulation Results ◽  
Takagi Sugeno

We present in this paper a novel and unified control approach that combines intelligent fuzzy logic methodology with predictive method for controlling chaotic vibration of a class of uncertain chaotic systems. We first introduce prediction into each subsystem of Takagi Sugeno (T-S) fuzzy IF-THEN rules and then present a unified T-S predictive fuzzy model for chaos control. The proposed controller can successfully stabilize the chaos and track the desired targets. The simulation results illustrate its effectiveness.

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