Fuzzy Logic Real-Time Digital Control of a Hardware in the Loop Maglev Device Using MATLAB and xPC Target

2003 ◽  
Author(s):  
O. Taghavi ◽  
P. S. Shiakolas ◽  
O. Kuljaca
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Digital Control ◽  
Magnetic Levitation ◽  
Tracking Error ◽  
Open Loop ◽  
Reference Trajectory ◽  
Hardware In The Loop ◽  
Time Data ◽  
Control Effort

This work will discuss the use of a single environment for real-time digital control with a hardware-in-the-loop (HIL) magnetic levitation (maglev) device for modeling and controls education, with emphasis on fuzzy logic (FL) feedforward control. This environment utilizes two computers (host and target), an off-the-shelf data acquisition card, and the HIL device (a nonlinear, open-loop, unstable, and time varying, custom-built maglev). The software includes tools from MathWorks Inc., and a C++ compiler. The values of any parameter (control law, reference trajectory) in the Smulink model can be changed dynamically on the host computer and their effects observed in real-time on the HIL system. Real-time data was collected from the HIL device and used in designing, tuning and implementing a feedforward FL controller all using MathWorks tools that controlled the HIL device in real-time. It was observed that the tracking error was substantially improved when the FL augmented the control effort of a classical lead compensator. The procedure for the FL development, tuning and hardware implementation along with examples will be presented. This system has been recently completed and was successfully used in an educational setting for one graduate and undergraduate Mechanical Engineering course.

scholarly journals Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Svenja Ipsen ◽  
Sven Böttger ◽  
Holger Schwegmann ◽  
Floris Ernst
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Image Data ◽  
Distinct Advantage ◽  
Reference Trajectory ◽  
Data Streaming ◽  
Tracking Accuracy ◽  
Time Data ◽  
4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

scholarly journals Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot

2021 ◽  
pp. 107754632110191
Author(s):  
Farzam Tajdari ◽  
Naeim Ebrahimi Toulkani
Keyword(s):  
Real Time ◽  
Tracking Error ◽  
Parallel Robot ◽  
Torque Control ◽  
Test Bed ◽  
Linear Quadratic ◽  
Control Effort ◽  
The Real ◽  
Unknown Disturbances ◽  
Quadratic Integral

Aiming at operating optimally minimizing error of tracking and designing control effort, this study presents a novel generalizable methodology of an optimal torque control for a 6-degree-of-freedom Stewart platform with rotary actuators. In the proposed approach, a linear quadratic integral regulator with the least sensitivity to controller parameter choices is designed, associated with an online artificial neural network gain tuning. The nonlinear system is implemented in ADAMS, and the controller is formulated in MATLAB to minimize the real-time tracking error robustly. To validate the controller performance, MATLAB and ADAMS are linked together and the performance of the controller on the simulated system is validated as real time. Practically, the Stewart robot is fabricated and the proposed controller is implemented. The method is assessed by simulation experiments, exhibiting the viability of the developed methodology and highlighting an improvement of 45% averagely, from the optimum and zero-error convergence points of view. Consequently, the experiment results allow demonstrating the robustness of the controller method, in the presence of the motor torque saturation, the uncertainties, and unknown disturbances such as intrinsic properties of the real test bed.

scholarly journals A model reference adaptive variable impedance control method for robot

2021 ◽  
Vol 336 ◽  
pp. 03005
Author(s):  
Xinchao Sun ◽  
Lianyu Zhao ◽  
Zhenzhong Liu
Keyword(s):  
Real Time ◽  
Tracking Control ◽  
Control Method ◽  
Impedance Control ◽  
Tracking Error ◽  
Reference Trajectory ◽  
Control Parameters ◽  
Model Reference ◽  
Force Tracking ◽  
Model Reference Adaptive

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.

scholarly journals Experimental Platform for Evaluation of On-Board Real-Time Motion Controllers for Electric Vehicles

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6448
Author(s):  
Thanh Vo-Duy ◽  
Minh C. Ta ◽  
Bảo-Huy Nguyễn ◽  
João Pedro F. Trovão
Keyword(s):  
Real Time ◽  
Electric Vehicles ◽  
Cost Effective ◽  
Open Loop ◽  
Control Force ◽  
Hardware In The Loop ◽  
Electric Motors ◽  
Electronic Stability Program ◽  
Time Motion ◽  
Braking Control

Electric vehicles are considered to be a greener and safer means of transport thanks to the distinguished advantages of electric motors. Studies on this object require experimental platforms for control validation purpose. Under the pressure of research, the development of these platforms must be reliable, safe, fast, and cost effective. To practically validate the control system, the controllers should be implemented in an on-board micro-controller platform; whereas, the vehicle model should be realized in a real-time emulator that behaves like the real vehicle. In this paper, we propose a signal hardware-in-the-loop simulation system for electric vehicles that are driven by four independent electric motors installed in wheels (in-wheel motor). The system is elaborately built on the basis of longitudinal, lateral, and yaw dynamics, as well as kinematic and position models, of which the characteristics are complete and comprehensive. The performance of the signal hardware-in-the-loop system is evaluated by various open-loop testing scenarios and by validation of a representative closed-loop optimal force distribution control. The proposed system can be applied for researches on active safety system of electric vehicles, including traction, braking control, force/torque distribution strategy, and electronic stability program.

DIRECT ADAPTIVE TYPE-2 FUZZY CONTROL FOR NONLINEAR SYSTEMS

2006 ◽  
Vol 06 (03) ◽  
pp. 389-411 ◽  
Author(s):  
KHEIREDDINE CHAFAA ◽  
LAMIR SAIDI ◽  
MOUNA GHANAI ◽  
KHIER BENMAHAMMED
Keyword(s):  
Fuzzy Controller ◽  
Tracking Error ◽  
Nonlinear Dynamical System ◽  
Reference Trajectory ◽  
Oscillation System ◽  
Control Effort ◽  
Nonlinear Dynamical ◽  
Supervisory Controller ◽  
And Control

A new direct adaptive type-2 fuzzy controller for a nonlinear dynamical system is developed in this paper. The parameters of the membership functions characterizing the linguistic terms in the type-2 fuzzy IF–THEN rules change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. A supervisory controller is appended to the type-2 fuzzy controller to force the state to be within the constraint set. Stability of this adaptive scheme is established using Lyapunov stability tools, where we guarantee the global stability of the resulting closed-loop system, in the sense that all signals involved are uniformly bounded. The simulation results for a Duffing forced-oscillation system show better performances, i.e. tracking error and control effort can be made smaller.

scholarly journals Neuro-Fuzzy Logic Based Robust Detection and Isolation of Economizer and Air Pre-Heater Faults in Steam Boiler

2019 ◽  
Vol 8 (4) ◽  
pp. 10727-10733
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Real Time ◽  
Logic Model ◽  
Steam Boiler ◽  
Time Data ◽  
Air Preheater ◽  
Neuro Fuzzy ◽  
Real Time Data ◽  
Intelligent Methods

The technique of Fault Detection and Isolation (FDI) of Economizer and Air-preheater of Boiler using Neuro-fuzzy system is presented in this paper. FDI using model based approach and intelligent methods are the current trend applied in space industries, process industries and power plants. Intelligent methods like Fuzzy, Neural network and Neuro-fuzzy methods are simpler for modeling and faster for detection and isolation of faults. Here the water wall type steam boiler which is used for producing steam in fertilizer industry is studied. The proposed scheme is detecting and isolating the faults and failures happens in the economizer and air preheater of boiler. The common faults are corrosion, erosion, cracking of boiler tubes at welding points, tube rupturing, scale formation in the tubes, external ash deposits etc. The inherent non-linearity of boiler makes Neuro-fuzzy logic method suitable for FDI for all possible faults. The detection of faults is carried out by computing residuals, which are the differences between real process output and estimated output by neuro-fuzzy logic model. These estimated outputs were obtained from the neuro-fuzzy logic model which is trained using real time data by Adaptive Neuro-fuzzy Inference Systems (ANFIS). The real time data of economizer and air-preheater of boiler is collected and used for residual generation. The residuals will be formed for two outputs which are playing important role. If the residual exceeds threshold value indicates various faults in the boiler components and makes the proposed FDI scheme robust against process and measurement noises, process modeling error, disturbances and all uncertainties etc. The threshold band is calculated using model error model method. To isolate the faults, the residuals are normalized and its magnitudes are compared with present fault severity limits. More the range of severity more will be the magnitude of faults in the boiler. FDI by neuro-fuzzy method is more advantages as it combines the advantage of artificial neural network and fuzzy logic methods. The neural networks are more adaptable and have more learning ability. Fuzzy systems are dealing with human reasoning and decision making. As a result the designed FDI scheme is more sensitive to faults and less sensitive to uncertainties and disturbances etc. makes the scheme robust. The required data and fault knowledge for the research work is collected from BHEL make 55 tons per hour capacity, water tube type boiler available in Madras fertilizer Limited (MFL), Chennai.

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