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Fuzzy Control - Science topic
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Questions related to Fuzzy Control
I am using the Fuzzy Controller in MatLab R2017a. But when I execute the controller, it gives an error:
Error in 'model3_FUZZY/Fuzzy_PID_Controller/FuzzyController': Initialization commands cannot be evaluated.
Caused by:
Struct contents reference from a non-struct array object.
The files are also given. Kindly guide me, How do I proceed, and remove this error.
Is synergetic control a model-free or model-based approach? Please tell me the reasons.
How about PID control?
I want to make weak grid in Simulink using PV-Battery Systems and then to apply fuzzy control logic system to stabilize the voltages and frequency of weak grid efficiently.
Suggest me any efficient model and technique to get some results.
How can I obtain the membership functions associated to sign function in order to design a Takagi-Sugeno fuzzy model? Thank you.
>> a_p=0.24;
a_d=0.12;
a_i=0.22;
s=0.1*e12;
kp=0.5; ki=0.25; kd=0.47;
ks_p=0.1*(1/(a_p+abs(s)-a_p*abs(s))+(a_p-1)/(a_p*abs(s)-1));
ks_d=0.1*(1/(a_d+abs(s)-a_d*abs(s))+(a_d-1)/(a_d*abs(s)-1));
ks_i=0.1*(1/(a_i+abs(s)-a_i*abs(s))+(a_i-1)/(a_i*abs(s)-1));
phi_p=s*ks_p;
phi_d=s*ks_d;
phi_i=s*ks_i;
u=kp*phi_p+ki*phi_i+kd*phi_d
Error using *
Out of memory. Type HELP MEMORY for your options.
Error in main (line 32)
phi_i=s*ks_i;
how to fix this memory error
what should we do that fuzzypid track the reference trajectory .
a_p=0.24;
a_d=0.12;
a_i=0.22;
s=0.8*e12;
kp=80000000; ki=5000; kd=2000;
ks_p=0.5*(1/(a_p+abs(s)-a_p*abs(s))+(a_p-1)/(a_p*abs(s)-1));
ks_d=0.5*(1/(a_d+abs(s)-a_d*abs(s))+(a_d-1)/(a_d*abs(s)-1));
ks_i=0.5*(1/(a_i+abs(s)-a_i*abs(s))+(a_i-1)/(a_i*abs(s)-1));
phi_p=s*ks_p;
phi_d=s*ks_d;
phi_i=s*ks_i;
u=kp*phi_p+ki*phi_i+kd*phi_d;
find an attached graph
Researchers are encouraged to the promote academic research and the exchange of results and opinions in this forum. However, this can be regarded as a low-effort post. The title of your post "What should we do?" cannot be meaningfully interpreted without taking a wild guess.
On the code, I have absolutely no idea what they are without the standard math notations or technical comments. Furthermore, running the cluttered code shows errors of some undefined variables. In fact, no where in the code shows any intelligible direct relation to the standard inference system of a typical Fuzzy PID [controller].
Please consider editing the post and fix the code (to plot the attached figure) if you are looking for meaningful advices from other researchers.
In his name is the judge
Hi
There is a fuzzy logic control system in python. The system contain 2 inouts and 18 outputs.
inference of system is mamdani and shape function used to be guassian.
Then in term of refine performance of the controller I need to optimize specifications belong to shape functions of both input and output. In order to that I need to use multi objective optimization.
We have 2 input and 1 output in case of this problem. I have developed 3 shape functions for each entrance and 3 for output and the shape function is gaussian so we have 18 parameters totally.
I defined my problem as a function in python. But notice this I there is not any clear relationship between input and output of function. It’s just a function which is so complicated with 2 inputs and 18 outputs.
I made my decision to use NSGAII algorithm and I really don't want to change the algorithm.
So I try every way to optimize my function but I didn’t find any success. By searching about python library which can do multiobjective optimization I find Pymoo as the best solution but I really failed to optimize my function which is complicated custom function, with it.
So It’s really my pleasure if you can introduce me a library in python or suggest me a way that I can use Pymoo in order to this aim.
wish you best
Take refuge in the right.
I wanted an authentic reference for fuzzy control (a book in English) to reference in my paper.
In his name is the judge
Hi
I'm trying to make a fuzzy controller in order to optimize my absorber performance in opensees(in python).
I use adaptive neuro-fuzzy inference system (ANFIS) toolbox in matlab to make fuzzy system as controller.
input data for fuzzy logic system are acceleration and velocity of absorber and the output data is force wich controller send it to absorber for performance improvement.
in fact i want controller learn ,based on velocity and acceleration of a point of structure, how much force need for turn structure into it's balance position.
note that quantity of force determine by fuzzy controller system and applying force part of absorbr job.
logically we have to assign come load to the point of structure wich absorber locate there and then get acceleration and velocity of absorber as input training data of fuzzy logic system.
but i realy don't know how can i do this.
note that i want to give force to structure in balance position, in the otherwise i think make train data is possible when using dynamic loading so i entirely confused here
if you have any suggestion i realy eager to hear it.
wish you best
Take refuge in the right.
In his name is the judge
Hi
I want design cotroller in to control active force of damper.
On this purpose, i analyzed structure with damper, wich is tlcdg, and get data whic use for generate controller. data are accleration and velocity of damper as input and dampers force as output ( i mean force wich made by own damper under earthquake excitation, not active force).
here is anfis properties :
number of inputs are 2
number of outputs is 1
generate fis method is grid partition
number of membership function are 4 for each input
input membership function type is guass2mf
output membership function type is linear
opt method is "hybrid".
(have to say i tried different epochs membership function , .....)
Unfortunately anfis toolbox in matlab refuse to train and build Suitable fuzzy controller wich means error is too much in training, so this answer is not acceptable.
i have some idea for make it true but i'm not sure.
here is my ideas :
* first i think train controller with more or less data ( training data are about 2000 wich is under 100s earthquake excitation but when i reduce earthquake excitation to 10 or 15 seconds the error is acceptable however i think this solution is not good.)
* second maybe i must try one damper for training.
* the last idea is to assign force on the damper location and get acceleration and velacity for generate trianing data.
here is my data and shot frome my try.
Any help is greatly appreciated.
Take refuge in the right.
Pls, I need a bibliography for
advantage of PID controller for mobile robot
Backstepping controller
MPC controller
Fuzzy controller
I must emphasize the advantages and disadvantages.
i need to design a model with some specifications
To Design a Fuzzy Control FLC that uses the indirect field-oriented control IFOC approach to regulate the velocity of an IM motor please i need an urgent help and solution
Please suggest the most useful of software on Linear Matrix Inequalities to solve Fuzzy control systems?
In his name is the judge
Hi
can you say wich set of data is best to train and test ANFIS in matlab.
i mean how generate data to get less error, like nuber of data or Dissipation of them
Wish you best.
Take refuge in the right.
In his name is the judge
Hi
In order to design controler for my damper ( wich is tlcgd), i want to use fuzzy system.
So i have to optimize rules for fuzzy controler. i want to know for optimizition rules of fuzzy systems wich one is the best genetiz algorithm or Artificial neural network?
Wish you best
Take refuge in the right.
I have just designed a fuzzy system, and now I want to redesign it based on the fractional order. However, I have no idea where to start from and what to do. Could you please tell me what I should do and mention any related sources to study?
Can anyone provide me with MATLAB code for fuzzy neural networks?
How do we create 49 rules according to the behaviour of the boost converter so as to implement in the matlab simulink and use in place of Pi controller which has much complex mathematical models?
I want to implement Fuzzy Control in an actual production process based on S7-1200 PLC , I have simulated the fuzzy control system in Matlab .But ,When i want to programe the PLC code using Siemens TIA Portral software, I found the fuzzy inference and defuzzication is difficult to programme. I have read many articles and papers disscusing fuzzy control based on PLC, they all omited the key step ,that is how to programme the fuzzy inference and defuzzication, so i still confused about that. I am very long for someone to be able to give me some help or share me some fragments of the PLC code related to this question.
My E-mail: 22060897@zju.edu.cn
Thanks for all reading my question!
PLZ, I need a PSO Matlab code to optimize the fuzzy controller.
I understand the fundamentals of particle swarm optimization and how to optimize fuzzy logic controller by PSO, but I don't know how to implement it in Matlab so can anyone help me by sending any ready code or sheet that explain how to optimize FLC using PSO.
Dear researchers,
How to implement fuzzy in dSPACE1104. By replacing the speed PI control with fuzzy controller getting over run error. How to resolve it.
does any seperate settings has to be selected while implementing fuzzy in dSPACE1104.
Please suggest the solution to resolve this issue.
Thank you.
Dear researchers,
while replacing fuzzy with speed PI control for PMSM drive, speed is tracking fine but whenever the load is applied speed is drastically reducing.
If torque limits have of output membership function chosen as >1500 or above and (placed a limiter at output (+-20 because rated torque 20N-m)found no decrement in speed but torque ripple is there. What could be the possible reason.
How to select the range, we must select the range of output membership function between +-20 right?
7*7 base rule base only considered for error and change in error that usually all the people mention in paper.
does speed settles back in fuzzy even under load or it maintains steady state error?
Please share your experience.
Thank you.
If you build a parameter learning algorithm based on the Lyapunov stability theorem for updating the parameters of an adaptive fuzzy controller, how to determine the cost function and Lyapunov function? Is there a physical connection between them?
Previously i have worked on Backstepping control for Two Stage Grid connected PV Inverter in which i used PI controller for DC-Link Voltage as can be seen in attached picture. Now I want to implement Fuzzy Control instead of PI control but the dc-link voltage constantly increases no matter what i do. Can any one share experience who has worked on PV inverters or atleast fuzzy logic control.
Besides the Schneider Electric PLCs that are programmed with EcoStruxure Control Expert or Unity Pro, Do you know of any other PLC(s) that include fuzzy control functions?
It would be interesting to know which manufacturers have been following the IEC 61131-7 standard, to know the state of the art of the application of fuzzy systems in industrial processes.
What is the elaboration of various control laws in comparison to each other. It means, in the level of comparison, when to take Adaptive control? when to adopt Robust control? when for Neuro-fuzzy control? when for Optimal control? when for model predictive control?
Hi, I'm trying to simulate a grid connected Pv inverter model using fuzzy logic controller by generating pulse from Dq0 voltage component. For filter I used LCL filter. I need some help.
1. I'm putting 769V PV input but getting 885V output which is more than input. Output power is 25KW for 50KW input. I'm not using any MPPT method and calculated DC link capacitor for 700V and getting 885V as well on dc link. Is this a problem?
2. When I simulate I'm getting huge reactive power flowing. If I fix this using Three phase reactive load THD of current goes very high up and system efficiency decreases. How can this be solved?
3. D-axis voltage in pu is 0.85 and Q-axis voltage is -0.5 instead of 1 and zero. Need help in this area also.
I search for applications about recommender systems.
The Idea in general is to use algorithms which gives according to historical data of the classifications of soil. then make a mapping spell between the existing data of soil, weather and crops and an input entered, user to know the type of crop adapted to his soil in addition to that the amount of appropriate fertilizer.
If you have research/review articles comparative studies or support it will be very helpful .
I research about design of Neuro-fuzzy controllers. Can anyone show me how to simulate my ANFIS model in SIMULINK? I could not find any model for ANFIS in the SIMULINK library. I could just find fuzzy controller not neuro fuzzy controller. Any suggestions?
How to train ANFIS using a wide range of input/output data that comes from the conventional controller like PID?
And how to extract the data (the error and change of error) from pid controller and use them as input to the ANFIS controller?
PID controller can take one input at a time (like step input or ramp) and gives you output. I want to try different methods to give a range of inputs to the PID controller as a reference (like array input). so that the ANFIS would work well for the input, and can be trained like in step input (not for all step input of all values, just one input of value 1), for the step of different value like step input = 2, For the time being, I am learning how to use anfis for simple plant model that is (0.021/(0.000347s^2 +s)). For future use, I will use anfis as fuzzy controller to control the trajectory of the welding robot.
for instance, We have the position equation of the welding torch below with 100 data samples .
This equation indicates the position of the welding torch in x-direction
x=0.177 ΔL1-0.29 ΔL2+0.177 ΔL3+12.23
Dear All,
I am working on the fuzzy control of mobile robots. Many papers have provided different fuzzy rules to describe this process. Usually the inputs to the fuzzy logic are the displacement needed to the reference point and the orientation towards the reference point and the out puts are the right and left angular velocity. The rules have the general form of : If x1j is X1j, ... , xnj is Xnj Then uj is Uj,
In which j is the jth rule number, x is the input, u is the output, X is the input fuzzy set and U is the out put fuzzy set.
My question is that how can I check the stability of a defined set of fuzzy rules?
Thanks in advanced.
Elham.
The plan is to control an average power generator speed to achieve a MPPT strategy.
I tried to make an PID controller tuned by a Fuzzy Logic Controller for a web winder system, the thing is the radius change along the process, so I made a block where is calculated.
I made a Fuzzy PID with 2 inputs (error and derivate of error, each one has a range from -0.1 to 0.1) and 3 outputs (dKp, dKi and dKd, each one with a range form 0 to 1), which are properly scaled according to the next formula:
Kp=(Kpmax-Kpmin)*dKp+Kpmin
Ki=(Kimax-Kimin)*dKi+Kimin
Kd=(Kdmax-Kdmin)*dKd+Kdmin
And Kp, Ki and Kd are tuned gains used by the classical PID controller.
The problem is the Fuzzy controller is very unstable during large simulations and the response seems to have so much noise, that behavior is noticeable seeing the scaled outputs of the Fuzzy Controller (Gains). As we can see, they have noise forward from 10 seconds. Also I had to sample the error and error rate because the simulation just don't work without that. The following error appears with out sampling:
"Warning: At time 1.4954202659488365, simulation found (14) Masked zero-crossing events.
Masked zero crossings are caused by even roots problems. It indicates that some zero
crossings may not be detected. To solve this problem, you can increase the refine factor
from (1) to a larger value. The refine factor can be accessed from the save options under
Data Import/Export option in the parameter configuration panel.
--------------------------------------------------------------------------------
Zero-crossing signal index : 365
Zero-crossing signal name : MinmaxInput
Block type : MinMax
Block path : 'PlantaEBT/Fuzzy PIDT1/Fuzzy Logic Controller/FIS
Wizard/Rule2/impMethod'
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Zero-crossing signal index : 395
Zero-crossing signal name : MinmaxInput
Block type : MinMax
Block path : 'PlantaEBT/Fuzzy PIDT1/Fuzzy Logic Controller/FIS
Wizard/Rule2/impMethod'"
To be honest I don't know why I have to sample the signals, the control gets worse in higher sampling times. I'm not a expert in Fuzzy PID or Fuzzy Logic in general too.
Also the response, despite to have a lower undershoot, has a bigger overshoot than the classical PID, I don't know how to improve the Fuzzy PID performance.
I attached all the necessary files. If there's any additional information that you need, please let me know.
Also, any kind of help, I will appreciate it.
Thanks
PS: Sorry for my english, I'm still learning.
Dear All
when simulating a feedback control system with Fuzzy controller in MATLAB Simulink for a tank level get this warning in run mode :
''input 2 expects a value in range [-4 4], but has a value of 4.22701e+15.''
Note : everything is performed well in terms of memberships functions for input variables
I implement the fuzzy controller by using the Arduino controller, it runs successfully
can implement fuzzy type 2 in the Arduino controller?
If input and output are known in advance for a system then how to choose membership function in fuzzy logic control? Also, How to design a fuzzy rule base for fuzzy controller? Is there any specific way to choose fuzzy rule base?
I am doing an analysis on MPPT in PV by comparing the traditional method with an intelligent one. I am looking for a working circuit ( PV with Fuzzy control ) with FIS matrix files. which i can simulate on Simulink.
please , need help...i am open to suggestion
I am working on optimization of energy consumption in buildings, where I am using a fuzzy logic controller. From a few research papers I saw that for input i.e change in temperature which lies in range of(-28 to 28), the output membership function i.e power lies in range of(-20 to 20).
So, if I change the range of I/P to (-32 to 32), what will new o/p range of power? Or will it still be the same ?
Common applications using fuzzy models are warming, aerating and cooling system, ventilating, robot, warm exchange pilot plant and wheel inverted pendulum. In these areas, the correct selection of fuzzy tenets is important in T-S Fuzzy model to control the system, which gives strength and nature of the system. T-S model can accurately rough the non-linear system and the steadiness. Fuzzy logic hypothesis is one of the control techniques and it is the efficient approach to manage the uncertainty issue for complex nonlinear system. A fuzzy controller utilizes fuzzy standards such as though then proclamations including fuzzy logic, fuzzy sets and fuzzy interference. One of the fuzzy systems is Takagi-Sugeno (T-S) fuzzy model is an effective device in approximating most complex non-linear system. The T-S models the non-linear system by weighted whole of linear time invariant systems.
Papers:
Li, Hongyi, Lijie Wang, Haiping Du, and Abdesselem Boulkroune, “Adaptive fuzzy backstepping tracking control for strict-feedback systems with input delay,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 3, 2017, pp. 642-652
Ding Zhai, An-Yang Lu a, Jiuxiang Dong b, Qing-Ling Zhang a, “Stability analysis and state feedback control of continuous-time T–S fuzzy systems via anew switched fuzzy Lyapunov function approach,” Applied Mathematics and Computation, vol. 293, 2017, pp. 586–599.
Yan-Jun Liu, Shaocheng Tong, Dong-Juan Li and Ying Gao, “Fuzzy Adaptive Control with State Observer for a Class of Nonlinear Discrete-Time Systems with Input Constraint,” IEEE transaction on fuzzy systems, 2015.
Hongyi Li, Jiahui Wang and Peng Shi, “Output-Feedback Based Sliding Mode Control for Fuzzy Systems with Actuator Saturation,” IEEE Transaction on Fuzzy systems, 2015.
My Fuzzy Controller is responding very slow, what is the reason. Do scaling factor is must for fuzzy.
Is there any method to find scaling factor?
Really frustrating slow response of fuzzy.
I tried to use a fuzzy logic controller to replace the PID contoller for the breast compression system. The response of the fuzzy controller is just too slow compared with my original PID controller (within 5s settling time) and there is no significant difference in settling time between 5 or 7 membership functions. I managed to reduce the system settling time to less than 20s by increasing the range of the output membership function from -1 1 to -6 6 (if i go beyond 6 the overshoot increase and the system become unstable). To be honest, I have no idea why the settling can be reduce by increasing the range of output? Is the other way I can speed up the fuzzy controller response?
Hi,
I have developed a fuzzy control system that produces an overall accuracy of 90.5% and I want to be able to prove that the fuzzy rule base was not tuned specifically for the data set in order to fabricate the accuracy.The rule base was developed purely through intuition.
The data set is a publicly available annotated corpus that is used by machine learning researches. I used the annotators label and the output of my system in order to figure out the accuracy, that is to say if majority of the annotators agree on a certain label for an input and the output of my FCS matches this label then I consider it as a correct outcome.
Is there any way to prove that my fuzzy rule base is in no way fabricated to match this particular dataset? I'm asking since the rule base was developed using intuition. and is there a standard way to test my system given a dataset like above.
I have prepared one two DOF semi active fuzzy control system with Bouc-Wen model. But Fuzzy system is not working its very slow.
What mistake I am doing or what is wrong with my model.
The model, fis file, and two reference paper are attached with this. What's wrong with the model not clear.
i want to simulate a wireless sensor network improved using fuzzy logic or neural networks, i can not execute this using NS-2, is omnet can do this.
thanks
jannat
I built fuzzy controller in simulink Matlab and I want to achieve in digsilent? I tried rewrite fuzzy control to m file. but the problem is the evalfis function just accept one input ? anyone knows? please help me? thank you very much!
I need the code of or software if available
i am working on ANFIS based induction motor control using fractional order controller data.
Scaling factors are being so important to help a fuzzy controller to achieve good performance. Reading so many papers, I've realized that almost all of the authors had chosen them according to several traditional trial and-error methods . Which is time consuming in my opinion and do not guarantee good results.
Apart from optimization methods, how can I choose the scaling factors for a fuzzy controller? Or is there a rule to choose them accroding to the obtained results ?
Type-2 Fuzzy logic controller is considered as the improved version of the conventional type-1 fuzzy logic control.
Recently many publications uses the type-2 fuzzy controllers in micro grid controls.Is their any tool box in Matlab which can model the type-2 fuzzy logic controller? or the present fuzzy logic tool box is sufficient to model a type-2 fuzzy controller?
I am using the Matlab-2014b version.
An integration of fuzzy logic for the development of a fuzzy control law of the access control system. Indeed, the goal of ensuring a constant flow of road traffic where we present a approach based on fuzzy logic allowing to have a reduced representation of the space of dimension 4 in a space of dimension 1 ensuring the regulation of traffic on the one hand, and the analysis system on the other.
Hi . Im trying to design a LQR controller for the system below. but I don't know where to put the LQR Gain Block in the system . I know I have to remove the Fuzzy Controller Block and Replace it with LQR Gain , but I don't know how to do that , I mean I guess I need an Observable Matrix for that? can anyone help me pls ?
Basically I am working on Lorenz Attractor and I want to design an intelligent controller based on Fuzzy Logic.
Kindly help.
Thanyou in advance.
Hi!
I have had this question, ever since I started my research in adaptive fuzzy controllers and that is--- How shall I construct the fuzzy rule to approximate the unknown dynamics- f(x) and g(x) in the dynamical system. If I start off with lets say 3 MFs to approximate the dynamics and if lets say I have some 9 states that completely describe the system dynamics, then, do I need to take all the possible combinations i.e 3^9 as number of fuzzy rules? Or just 3? Or any number between 3 an 3^9? What should be the optimum number of fuzzy rules to approximate the unknown dynamics?
I am using fuzzy logic to help decision-makers to get "quickly" and reliable decisions. I have collected many projects and I am interested on 12 parameters. So Finally my system has 12 inputs, each one has 3 membership functions(MFs), and one output with 4 MFs.
But I have a problem regarding the Inference system, i.e. If-Then-Rules. My parameters have a solid Relationship between them, so I think that I Don't have to consider all possible combination ( 3^12) but I have to make sure that my system got a good training and then I can trust him.
Before talking about the testing the system with a sall dataset, let's talk about the way I can consider all possible If then rules. should I come bach to each project and according to each one I see on which membership it belongs, or I use Simply the parameters ranges that I already realised and build my If-then-rules according to that?
One more question relating to unsuccessful projects. how could I consider these information (example: when a was low and b was Med and ….. Then the project X was insuccessful, Can I use it like this:
If a is Low and b is Med and …. Then output is not X ?)
I will more than happy f you share with me your experience so I can adapt it to my problem and act accordingly.
Thank you, and I am wainting for your interactions.
King Regards,
Hi! I have a system that loosely looks like this-
$\dot{x}_{1}=f_{1}(x_{1},x_{2},x_{3},x_{4})$,
$\dot{x}_{2}=f_{2}(x_{1},x_{2},x_{3},x_{4})$,
$\dot{x}_{3}=x_{4}$,
$\dot{x}_{4}=f_{4}(x_{1},x_{2},x_{3},x_{4})+bu_{1}$,
$y=x_{3}$,
I am designing a direct fuzzy adaptive controller to control the state x3. I wish to know what should be inputs to the fuzzy system that will approximate the ideal controller? Is it going to be all the states i.e $x_{1},x_{2},x_{3},x_{4}$ or $e,\dot{e}$? And finally what kind of adaptive law will ensure that the error is driven to zero. In my real system, I have got 9 states, and the state that I am interested in controlling has a relative degree of two. So, Shall I take all the states as input to my adaptive fuzzy controller? If I choose that, then will it not be exhorbitantly computationally expensive- considering the fact that there are 3 MFs per input-- resulting in $3^{9}$ rules? Kindly provide your inputs. Thanks in advance.
I am working on a problem with many conditions (say 40). If I write the membership function for two inputs; how can I create own fuzzy output function. Is it possible with the fuzzy toolbox?
I am working on Simulink Model of 8DOF semi active suspension quarter car model with passenger. The MR Bouc Wen Model is used in the model which is controlled by Fuzzy controller with 2 input (sprung mass displacement, sprung mass velocity) and 1 output (Voltage).
I want to optimize Fuzzy rules and Fuzzy Membership function by Particle Swarm Optimization techniques. The Objective Function is Minimization of Sprung Mass Displacement.
I am looking for Matlab Codes for the same. How can I call Simulink output as Objective function in Coding?
I'm working on a implementation of a Fuzzy controller in a FPGA board, I know that a part of the controller is the inference and I need a matriz for that, so the question is, How can I implement that part in VHDL.
I would like to create an Adaptive Fuzzy Controller which its output membership functions will change every time accoırding to algorithm that I will code. Can you please tell me if it is possible to do in simulink environment where I can add algorithms to my fuzzy controller and make it adaptive.
Fozzy Logic Toolbox in Matlab/ Simulink does not have the option of doing so .
Best
I want fuzzy logic controller design theoretically and in MATLAB simulink and programming any one have that material?especially for designing uavs and quadrotors
What will be a successful mixture? for what end? could you please steer me towards some good references that have applications, and solution by approximate analytical methods?
Many thanks and best regards
Sarmad.
Presents a systematic treatment of fuzzy fractional differential equations as well as newly developed computational methods to model uncertain physical problems
Complete with comprehensive results and solutions, Fuzzy Arbitrary Order System: Fuzzy Fractional Differential Equations and Applications details newly developed methods of fuzzy computational techniquesneeded to model solve uncertainty. Fuzzy differential equations are solved via various analytical andnumerical methodologies, and this book presents their importance for problem solving, prototypeengineering design, and systems testing in uncertain environments.
In recent years, modeling of differential equations for arbitrary and fractional order systems has been increasing in its applicability, and as such, the authors feature examples from a variety of disciplines to illustrate the practicality and importance of the methods within physics, applied mathematics, engineering, and chemistry, to name a few. The fundamentals of fractional differential equations and the basic preliminaries of fuzzy fractional differential equations are first introduced, followed by numerical solutions, comparisons of various methods, and simulated results. In addition, fuzzy ordinary, partial, linear, and nonlinear fractional differential equations are addressed to solve uncertainty in physical systems. In addition, this book features:
Basic preliminaries of fuzzy set theory, an introduction of fuzzy arbitrary order differential equations, and various analytical and numerical procedures for solving associated problems
Coverage on a variety of fuzzy fractional differential equations including structural, diffusion, and chemical problems as well as heat equations and biomathematical applications
Discussions on how to model physical problems in terms of nonprobabilistic methods and provides systematic coverage of fuzzy fractional differential equations and its applications
Uncertainties in systems and processes with a fuzzy concept
Fuzzy Arbitrary Order System: Fuzzy Fractional Differential Equations and Applications is an ideal resource for practitioners, researchers, and academicians in applied mathematics, physics, biology, engineering, computer science, and chemistry who need to model uncertain physical phenomena and problems. The book is appropriate for graduate-level courses on fractional differential equations for students majoring in applied mathematics, engineering, physics, and computer science.
We want do develop a device which can intimate us if blood pressure increases or decreases beyond the prescribed range. This device should we wearable like a bangle.
I want analyse the stability of chaotic systems and design a controller for such systems.
consider a boost converter with fuzzy controller.
what is the adv of taking 3,6,9 membership functions?
will that cause complexity in hardware implementation (say in dsPIC30f)
Considering the practical and theoretical approaches, how many membership function should be taken?
I want to know about the present state of the art in the field of fuzzy logic controllers. I have with me only some surveys which spoke about the researches that were conducted till 2007. I am in need of some survey papers that can help me get an idea of the research work that has been carried out after 2007 and till the present date. Please help.
This type of algorithm (Takagi-Sugeno fuzzy) is talent to deal with MIMO plants that possess lag time, big inertia and uncertainty and can be deemed as one of the most important components of intelligent buildings
Is there any other ideas than "probability theory" to derive the intervals from the responses in intervall-type Type-2 Takagi-Sugeno Systems?
what methods are available to identify intervall-type Type-2 Takagi-Sugeno Systems and which one is better and why?
I must write Sugeno type fuzzy controller with .m script. How can I write without using fuzzy toolbox? Where can I find an example about it?
Thanks...
We have to control the amount of water flow as well as any advice how to make it Intelligent?
Actually i'm working on a wind system, now i want to replace the PI controller with Fuzzy Logic Controller for MPPT strategy, but i have a problem defining the right gains to obtain some good results.
Hello, I'm looking for some practical examples of MPC algorithm i.e. procedures step by step with the practical background. Theory is well documented, but examples I found are full of gaps, which made them not so usefull.
I want to create a membership function for the probability distribution (generated from statistical data) of the remaining fatigue life. Can anybody guide me on how to do so? I have found few papers on internet but most of these seem to confuse me rather than helping me. It would be great if somebody could guide me in the aforementioned matter. Thanks.
someone wants to implement fuzzy logic in simulink, what steps should he follow? Thanks in advance!
I m working on use of fuzzy logic in water quality indezing...
Hi,
I am working on to design closed loop blood pressure control system, for this firstly I developed fuzzy rule set, but for increasing the efficiency of controller I plan to develop adaptive fuzzy control in Matlab.
So, for this I choose ANFIS edditor module in Matlab. For this module training data set and testing data set are required to develop a rule.
But I don't know what is the structure of data set or data set should contain which type of information for training purpose.
I am looking for any example data set to training or testing in ANFIS.
Dear Naira, hello. I'd like to explore the possibility of doing joint research in fuzzy optimization. I have worked with fuzzy control for mechanical systems but I'd lke to improve this.
i am working on a simulink model and need to normalise the output values to -1 to 1 so that i can imply fuzzy controller easily. i am working in simulink environment.
Regards:
I need to analysis some data on stability model using Ebernert and Russell, Shukla and other relevant ones
Hello all,
My system is MIMO system I want to implement fuzzy controller for this system, but I have digital inputs and digital outputs . Any body have an idea how is the shape of the fuzzy memberships should be to manipulate the digital behavior of these digital input and outputs.
Regards
MAnsour
i am trying to use fuzzy cognitive map to model a system, so i just start trying the available tools to do so, one of these tool is FCM tool under matlab, where you fill the connection matrix and also number of concepts and the initial state vector to initials the map. the problem when i use sigmoid function as threshold it gives some result where the system should converge and at stable state, whenever i change the state vector for the initial value the result always stay the same which is not make sense? if any one has use this tool and can help to explain what went wrong that will be highly appreciated.
I would like to ask you to share with your experiences related to fuzzy supervisory control. I think about such a algorithm which is able to change parameters of a classical PID controller, based on a human experience due to plant's dynamics variations, disturbances etc.
I wonder if such a algorithm would be able to make a control process more robust.
What microprocessor should I choose if I want to implement PID or fuzzy control? Arduino? Raspberry Pi? Any suggestions?
i designed a FLC with 2 inputs and 1 output. the range of inputs are [0 1] and [0 20] and output's range is [0.008 0.01]. when i run it in my simulation the value of output is zero means flc gives a zero value for output while the range of output starts at 0.008. assuming i designed flc inappropriately. Is getting zero value logic? my inputs are in the own ranges.
why do i get this result?
regards
i am working on fault detection of a system and using fuzzy logic for decision and detection. but issue is i have a variable and fluctuating residual signal , now i have to take its limits to set a rule based on the magnitude of the residual, but its is varying and once it is + valued and other time negative like a sinusiodal sine wave, now i should do for smoothing it and find the accurate magnitude so i can assign values to my fuzzy controller?
Considering practical implementation which one should be selected.
Please provide reason for ur suggestion.
I'm working on a laboratory Helicopter model (from HUMUSOFT company) and I've designed a TS fuzzy observer-based controller for it and I want to implement it.At first a PID controller performs then fuzzy controller, but fuzzy controller produces a very large signal output that is out of range of machine. I want to switch between these two controllers in machine range.
I have an online parameter estimation based on recursive least-square method to approximate the model given I/O observation data. How can I link this model with the adaptive PID control law, considering that neither pole placement technique nor Model Reference Adaptive control can be implemented because the system is too complex to model ?
In Layer 2 of ANFIS sugeno type model, it serves to evoke firing-strength by multiplying each input signal. why the firing strength need to be normalised?
The system is nonlinear described by a set of 8 ODEs (8 states). Two discontinuous functions (jump discontinuity) are present in the first state equation. Can any one suggest the method to design a TS fuzzy model for such system?
dx1/dt = -x1.x3 + k12.x2 + EGP(1 - x5) - F - Fr + u1
dx2/dt = x1.x3 - k12.x2 - x2.x4
dx3/dt = -ka1.x3 + kb1.x6
dx4/dt = -ka1.x4 + kb1.x6
dx5/dt = -ka1.x5 + kb1.x6
dx6/dt = -ke.x6 + ki.x7
dx7/dt = -ki.x7 + ki.x8
dx8/dt = -ki.x8 + u2
states: [x1 x2 x3 x4 x5 x6 x7 x8]
parameters: [k12 EGP ka1 ka2 ka3 kb1 kb2 kb3 ki ke]
dis-continuous functions: [1] Fr = 0.003(x1-9), when x1>=9
Fr = 0, [otherwise]
[2] F = 0.97 , when x1>=4.5
F = 0.97.x1/4.5 [otherwise]
Q/ How to take the discontinuous functions as premise variables of fuzzy model?
If I have designed a fuzzy inference system (say controller) using Fuzzy Logic Toolbox in Simulink. Now Based on some performance criteria, I need to update/modify the existing structure of fuzzy controller, say, Rule base, scaling factors and membership functions.
How do I do so in Simulink? Lets say, I have implemented the algorithm in a S-function/Embeded Mat;ab Function block in Simulink. now, I want to access the rule base and MFs and update them online without stopping the simulation.
I would be grateful if you could send me the download link of it, because I could not find it via internet while I really need it. There is interval type2 fuzzy toolbox but I could not find Generalized fuzzy toolbox.
I have read a lot theory about Self Organizing Fuzzy control from Internet. But I am still not completely able to understand its theory and implementation.
I have attached one of the best document I could find on Self Organizing Fuzzy controller from internet.
Please can anybody share the implementation for self organizing fuzzy? if anybody has developed any control logic using this technique.
Thanks in advance :)
I am very new to fuzzy logic. And I read a lot of examples over the internet. But I could not understand one particular example where only non overlapping member fuctions were used (which I find very uncommon). So I would like to have a better understanding in this aspect.
and one more request.
I am trying the implemetation for fuzzy controller in C. So if there is any good material to read I would appreciate if you can suggest.
Thank You
Does connecting two fuzzy logic controllers one for speed and another for current bring about any improvement in the performance of the DC motor? I have used fuzzy logic controller for speed control and a PI controller for current control . When i connect another fuzzy logic controller for current control in series with the already existing fuzzy logic controller output is not correct. I am not able to understand why.
