On-Line Fuzzy Logic Temperature Control of a Concentric-Tubes Heat Exchanger Facility

2006 ◽  
Author(s):  
Claudia Ruiz-Mercado ◽  
Arturo Pacheco-Vega ◽  
Kevin Peters
Keyword(s):  
Heat Exchanger ◽  
Temperature Control ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Outlet Temperature ◽  
Single Input Single Output ◽  
Single Output ◽  
On Line ◽  
The Difference ◽  
Two Stages

We develop a fuzzy rule based controller to perform on-line temperature control of a concentric-tubes heat exchanger facility. The rules were derived from dynamical values of the mass flow rates and fluid temperatures in the heat exchanger. The controller was embedded in a closed-loop single-input single-output system to control the outlet temperature of the cold fluid. The controller was constructed in two stages, the difference between them being the amount of information provided to the controller. To validate the fuzzy controller two sets of tests were carried out for maintaining a constant value of the outlet temperature under different perturbations. Results from this analysis demonstrate that the fuzzy-based controller is able to achieve control of the system, and that the information about the system provided to it is important in terms of accuracy and efficiency.

Decentralized robust evolving cloud-based controller for two input two output systems

2021 ◽  
pp. 014233122110492
Author(s):  
J Vijay Anand ◽  
PS Manoharan
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Fuzzy Rule ◽  
Single Input Single Output ◽  
Point Tracking ◽  
Single Output ◽  
Tito Systems

The fuzzy logic controller (FLC) makes it possible to control a system using IF-THEN rules through human intellect. It tackles parameter uncertainty using imprecise reasoning. The fuzzy logic controller is usually tuned using offline methods. An online evolving adaptation of fuzzy controller design is a recent trend in fuzzy rule-based systems. The robust evolving cloud-based controller (RECCo) is one such controller implemented for single-input-single-output (SISO) systems. The membership functions and consequent rules are automatically updated in real time based on the input data. In this paper, a decentralized robust evolving cloud-based controller (DRECCo) is proposed for two-input-two-output (TITO) systems. It consists of two independent loops with RECCos having a nonparametric premise facet and an adaptive proportional-integral-derivative (PID) model consequent facet. The effectiveness of the proposed method is validated for the benchmark interacting two-tank process (ITTP) and quadruple-tank process (QTP) by simulation and in real time. The results indicate that with the information of loop pairing and the forward-acting/reverse-acting nature of the process, the proposed controller can adapt itself to ensure set-point tracking and disturbance rejection.

Multi-model Fuzzy Modal Control of SISO Nonlinear Plant

2016 ◽  
Vol 14 (4) ◽  
pp. 19-26 ◽  
Author(s):  
V. Lukov ◽  
M. Alexandrova ◽  
N. Nikolov
Keyword(s):  
Fuzzy Controller ◽  
Static Characteristic ◽  
Control Law ◽  
Modal Control ◽  
Single Input Single Output ◽  
Single Output ◽  
Single Input ◽  
Nonlinear Static ◽  
Takagi Sugeno ◽  
Nonlinear Plant

Abstract The article presents the synthesis of a multi-model modal control of single input – single output nonlinear plant, based on Takagi-Sugeno fuzzy controller. For that purpose, the nonlinear static characteristic of the plant is presented by two linear parts. These two linear structures are described in state space. The feedback vectors and the coefficients ki of the modal controllers are calculated. An integral component in the control law is added.

scholarly journals A Gain-Scheduling PI Control Based on Neural Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Stefania Tronci ◽  
Roberto Baratti
Keyword(s):  
Controller Design ◽  
Neural Model ◽  
Gain Scheduling ◽  
Generic Model ◽  
Continuous Stirred Tank Reactor ◽  
Single Input Single Output ◽  
Single Output ◽  
Model Control ◽  
On Line ◽  
Generic Model Control

This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC) formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR), considering both single-input single-output (SISO) and multi-input multi-output (MIMO) control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

scholarly journals Design of Hybrid Neural Fuzzy Controller for Human Robotic Leg System

2018 ◽  
Vol 14 (1) ◽  
pp. 145-155
Author(s):  
Ekhlas H. Karam ◽  
Ayam M Abbass ◽  
Noor S. Abdul-Jaleel
Keyword(s):  
Fuzzy Controller ◽  
Nonlinear Function ◽  
Matlab Simulation ◽  
Single Node ◽  
Differential Model ◽  
Single Input Single Output ◽  
Single Output ◽  
Single Input ◽  
Robotic Leg ◽  
Neural Fuzzy

 In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that the efficiency of this controller when compared with the results of the leg model that is controlled by PI+2D, PD+NID, and FPD-ID controllers.

scholarly journals Design of Robust Adaptive Fuzzy Controller for a Class of Single-Input Single-Output (SISO) Uncertain Nonlinear Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jieqiong Lin ◽  
Jiakang Zhou ◽  
Mingming Lu ◽  
Hao Wang ◽  
Allen Yi
Keyword(s):  
Fuzzy Controller ◽  
Elliptical Vibration Cutting ◽  
Single Input Single Output ◽  
Vibration Cutting ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Elliptical Vibration ◽  
Single Output ◽  
Single Input ◽  
Robust Adaptive

In order to solve the precision and stability control problems of nonlinear uncertain systems applied in machining systems, in this paper, a robust adaptive fuzzy control technique based on Dynamic Surface Control (DSC) method is proposed for the generalized single-input single-output (SISO) uncertain nonlinear system. A first-order low-pass filter is introduced in each step of the traditional robust control method to overcome the “calculation expansion” problem, and Takagi–Sugeno (T-S) fuzzy logic system is applied to approximate an uncertain nonlinear function of unknown structure in the system. The designed robust adaptive fuzzy controller is applied to the 3D elliptical vibration cutting (3D EVC) device system model, and the effectiveness of the controller design is verified by analysis of position tracking, speed tracking, and tracking error. The results of studies show that the robust adaptive fuzzy controller can effectively suppress the jitter problem of the three-dimensional elliptical vibration cutting device so that the control object can be stabilized quickly even if it has a little jitter at the beginning. It can be smoothed to move along the ideal displacement and velocity signals. It is verified that the designed controller has strong robust adaptability.

scholarly journals Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Muhammet Öztürk ◽  
İbrahim Özkol
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Strong Stability ◽  
Inference System ◽  
New Approach ◽  
Fuzzy Controllers ◽  
Advantages And Disadvantages ◽  
Neuro Fuzzy ◽  
On Line ◽  
The Difference

AbstractIn this paper, a new approach for Neuro-Fuzzy Controller (NFC) has been presented and compared to previously defined NFCs given in open literature. The proposed controller is based on an on-line Adaptive Neuro-Fuzzy Inference System (ANFIS) and meticulous analysis through simulations is performed to show its robustness. The performance of Neuro-Fuzzy Controllers (NFC) depends on controller inputs. To show the difference and superiority of the proposed controller, many studies in the open literature are examined and compared. Therefore, the advantages and disadvantages of the Neuro-Fuzzy controller are outlined and an optimum Neuro-Fuzzy controller is structured and presented. To test our developed controller for a nonlinear problem, having coupling effects, a 2 DOF helicopter model is chosen. Also to show the robustness, the controller performance which is applied to a 2 DOF helicopter is investigated and compared with other Neuro-Fuzzy controller structures. To better show NFC performance, NFC control results were compared with LQR+I. It is observed that besides being on-line adaptive for all systems, the controller developed has many priorities such as noiseless, strong stability, and better response time.

scholarly journals Design and Implementation of Adaptive PID and Adaptive Fuzzy Controllers for a Level Process Station

2021 ◽  
Author(s):  
Aparna Venkataraman
Keyword(s):  
Real Time ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Equilibrium Points ◽  
Process Variable ◽  
Open Loop ◽  
Single Input Single Output ◽  
Adaptive Fuzzy ◽  
Single Output ◽  
Adaptive Fuzzy Logic

This proposed work proposes the design and real-time implementation of an adaptive fuzzy logic controller (FLC) and a proportional-integral-derivative (PID) controller for adaptive gain scheduling that can be configured for any complex industrial nonlinear application. Initially, the open-loop test of the single-input single-output (SISO) system, with nonlinearities and disturbances, is conducted to represent the mathematical model of the process around a set of equilibrium points. The adaptive controllers are then developed and deployed by using the national instruments reconfigurable input/output data acquisition device (NI RIO), NI myRIO-1900, and the control parameters are adapted in real-time corresponding to the changes in the process variable. The resulting servo and regulatory performance of the controllers are compared in MATLAB® software. The adaptive fuzzy controller is deduced to be the better controller as it can generate the desired output with quicker settling times, fewer oscillations, and negligible overshoot.

Modeling and Simulation of Robotic Material Removal Process in Uncertainty Environment

2011 ◽  
Vol 675-677 ◽  
pp. 1003-1006
Author(s):  
Xian Lun Wang ◽  
Yu Xia Cui
Keyword(s):  
Material Removal ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Interaction Force ◽  
Machining Process ◽  
Force Model ◽  
Removal Process ◽  
Robotic Machining ◽  
On Line ◽  
Uncertainty Environment

The interaction force and the environments uncertainties are the most challenges for robotic material removal process. The conventional constant force control methods for the deburring process have the inherent characteristic of leaving the deburred surface as an imprint of the original. A process force model considering the burrs variation is presented to predict the contact force in robotic machining process. A self-tuning fuzzy strategy is adopted to implement the on-line compensation for the static error caused by the traditional impedance controller. The fuzzy controller is adjusted by an updating factor to select the most appropriate fuzzy rule set based on the measured performance results. Simulation results show efficacy of the proposed method in robotic machining process, and the control performance is better than that of a traditional impedance controller.

Design of Robust Mixed Fuzzy Controller for Gas Metal Arc Welding Process

2006 ◽  
Author(s):  
Ata Allah Eftekharian ◽  
Hasan Sayyaadi ◽  
Mohammad Amin Tadayon
Keyword(s):  
Arc Welding ◽  
Fuzzy Controller ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Single Input Single Output ◽  
Single Output ◽  
Metal Arc Welding ◽  
Highly Nonlinear ◽  
Arc Welding Process ◽  
Fifth Order

Welding is an important manufacturing process that can be automated and optimized. In this paper we discuss the Gas Metal Arc Welding (GMAW) control and modeling problem. For modeling the process recently developed highly nonlinear fifth order mathematical model is used, for controlling the GMAW process we use a new Mixed Fuzzy Control (MFC) structure. In this work first a Traditional Fuzzy Controller (TFC) is designed from the viewpoint of a Single-Input Single-Output (SISO) system for controlling each state of GMAW process. Then, an appropriate coupling fuzzy controller is also designed according to the characteristics of gas metal arc welding process and incorporated into a TFC. We then show by simulation that this control strategy can not only improve the tracking performance of the controller, but also can deal with model uncertainty and disturbances.

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