❇️Comprehensive Guide to Multivariable Control: From Differential Equations to QFT Controllers
This tutorial offers an in-depth look at multivariable control systems, particularly within electric arc welding, covering from basic principles like differential equations and block diagrams to advanced topics such as system dynamics, controllability, and advanced control strategies like H-infinity and LQR controllers. It emphasizes the Quantum Field Theory (QFT) controller's role in effectively managing complex control challenges. Designed for students, educators, and engineers, the video bridges theoretical concepts with practical applications, making it a key educational tool in control engineering.
🔻YouTube: https://youtu.be/uB9cJTalCuA
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#MultivariableControlSystems #ElectricArcWeldingControl #DifferentialEquations #StateSpaceModeling #HinfinityController #PIDTuning #LQRController #QFTController

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#clustering
#Ieee_Transactions
#Fuzzy
#Neural_Networks
#GA
#Coming_Soon

% Doc This Code
%% Part 1
% Differential Equations Line 46
% Block diagram Line 76
% State space equations Line 110
% transformation function matrix Line 125
% Denoscription of matrix fraction Line 130

%% Part 2
% System_Pole Line 156
% SmithForm_G Line 162
% MacMilan_pole Line 168
% Zero_Element Line 172
% Zero_transfer Line 181
% Zero_decoupling Line 185
% Controllability and Observability Line 199
% Norm_2 , Norm_infinitely , Norm_Henkel Line 210
% Realization of system balance Line 231
% Reduction of Order Line 238
% Igenvalues of Frobenius Line 262
% Grishorian bands Line 288
% Nyquist Plot Line 303
% Gain-Space Diagram Line 321

%% Part 3
% H-infinity controller Line 357
% PI with pidtune Line 425
% PI 2 (sigma) Line 477
% LQR Controler Line 553
% Optimal LQR with H inf Line 620
% QFT Controler Line 684
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#Code #MIMO

نکاتی در مورد تحلیل آماری و بهینه سازی کد

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❇️Fast Self-Supervised Clustering With Anchor Graph
This tutorial showcases the Fast Self-Supervised Clustering method for large-scale, high-dimensional data analysis without labeled samples, using MATLAB. It introduces the Fast Self-Supervised Framework (FSSF) and Balanced K-Means-based Hierarchical K-Means (BKHK) with bipartite graph theory. The method involves four key steps: acquiring an anchor set with BKHK, constructing a bipartite graph, solving the problem using FSSF, and selecting representative points for label propagation. Demonstrated to surpass other methods in performance and efficiency, it offers key insights for those in machine learning and data science.
🔻YouTube: https://youtu.be/_HgnVNGY5gQ
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#MachineLearning #MATLABSimulation #SelfSupervisedClustering #AnchorGraph #IEEE #DataScience #ClusteringAlgorithms #UnsupervisedLearning #BigData #AIResearch

❇️General Fuzzy C-Means Clustering with Objective Function Control
In this MATLAB tutorial, we explore the General Fuzzy C-Means (GFCM) clustering strategy, a novel approach from the IEEE Transactions on Fuzzy Systems that enhances the traditional fuzzy C-means clustering by using an objective function to control fuzziness. This method improves clustering precision by providing a clear definition of fuzzy degree, enabling exact control over results. We demonstrate the GFCM algorithm's adaptability across various distance metrics and fuzzy degrees, emphasizing the importance of choosing the right fuzzy degree. The tutorial covers theoretical basics, practical applications, and the algorithm’s convergence and stability, offering valuable insights for students, researchers, and professionals in data science and machine learning.
🔻YouTube: https://youtu.be/o9DxlIYMNM0
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#FuzzyClustering #DataScience #MachineLearning #IEEE #FuzzySystems #Clustering #ObjectiveFunction #GFCM

Reinforcement Learning Toolbox™
User's Guide 2023a

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#Reinforcement_Learning #Toolbox #RL #2023a #Environments #Designer #Simulink #Agents #Q_Learning #SARSA #Deep_Q_Network #Policy #Actor #Deep_Deterministic_Policy_Gradient #TD3 #PPO #TRPO #MBPO #NN

نکاتی در مورد متلب 2023a
دارک مود ، نحوه دانلود و نصب ، برخی مزایا و معایب
نحوه دانلود و نصب کتابخانه و مثال های اماده از داخل متلب
نحوه استفاده از راهنما و مثال های آماده
اجرای مثال اماده یادگیری تقویتی پارک اتوماتیک
اجرای مثالی از طراحی اپ
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❇️Track Multiple Vehicles Using a Camera❇️
This example shows how to detect and track multiple vehicles with a monocular camera mounted in a vehicle.

✅Overview
Automated Driving Toolbox™ provides pretrained vehicle detectors and a multi-object tracker to facilitate tracking vehicles around the ego vehicle. The vehicle detectors are based on ACF features and Faster R-CNN, a deep-learning-based object detection technique. The detectors can be easily interchanged to see their effect on vehicle tracking.

The tracking workflow consists of the following steps:

Define camera intrinsics and camera mounting position.

Load and configure a pretrained vehicle detector.

Set up a multi-object tracker.

Run the detector for each video frame.

Update the tracker with detection results.

Display the tracking results in a video.
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#Track #Vehicles #Camera #detector #intrinsics #Driving_Toolbox #R_CNN #deep_learning #ACF

Automated Driving Toolbox™
User’s Guide 2023a

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#Sensor #Vehicle #Pattern #Camera #Estimation #Labelling #App #Moving #Drawing #Automated #Simulation #Kalman_Filters #Map #Control #Velocity #NCAP #3D

✅Guidance and Navigation of Aircraft using Model Predictive Control

❇️FlightGear V3
❇️Simulink
❇️just run

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#AircraftNavigation #FlightControl #ModelPredictiveControl #FlightGearV3 #Simulink #AviationTechnology #AircraftGuidance #FlightSimulation #AerospaceEngineering #AircraftAutomation #AviationSimulation #FlightTraining #AutonomousFlight #FlightSimulationSoftware #AircraftSystems #AircraftDevelopment #AircraftTechnology #FlightGearCommunity #SimulinkModeling #AerospaceInnovation

تحلیل و طراحی کنترل موقعیت ربات یک درجه آزادی با استفاده از منطق فازی (تولباکس) در متلب

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❇️"Designing Fuzzy Systems with Recursive Least Squares | MATLAB Simulink Tutorial for Cruise Control and DC Motor Speed"❇️

more in coment

To watch in
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#FuzzyLogic #MATLABSimulink #ControlSystems #RecursiveLeastSquares #GaussianFunctions #SystemTesting #OptimalControl #AdaptiveControl #TechTutorial #Engineering #MATLABCoding #RealTimeControl #AlgorithmExplanation #VersatileSystems #OnlineLearning

❇️Transfer Function Coefficient Identification with MATLAB: Markov Series and Hankel Matrix Method

Denoscription:

"Dive into MATLAB as we explore the Markov series and Hankel matrix method for identifying transfer function coefficients. This tutorial guides you through the step-by-step process of extracting numerator and denominator coefficients, shedding light on the intricacies of system identification. Perfect for engineers and enthusiasts looking to enhance their understanding of transfer function modeling.
To watch in
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#MATLAB #TransferFunction #SystemIdentification #ControlSystems #Engineering #MarkovSeries #HankelMatrix #MATLABCoding #CoefficientIdentification

❇️پروژه درس شناسایی سیستم

—شناسایی سیستم به صورت فضای حالت
—شناسایی سیستم با مدل های ARX , OE , BJ
—شناسایی غیر خطی NLARX ,...
—شناسایی جعبه خاکستری
—بهبود مدل با استفاده از همرشناین وینر
—شناسایی حوزه زمان سیستم با تولباکس شناسایی سیسیتم
—طراحی کنترل کننده پیش بین برای مدل شناسایی شده
—مقایسه کنترل کننده پیش بین با کنترل کننده PID همراه با نویز در خروجی و ورودی سیستم و ردیابی ورودی مرجع
—راهکاری ساده برای فیلتر نویز و...
دانلود:
https://npd.servr.ir/ident/pro/


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#MATLABCoding #ControlSystems #SystemIdentification #Modeling #NonlinearIdentification #PredictiveControl #PIDController #NoiseFiltering #Simulation #MATLABTutorial

❇️Revolutionizing Multi-Robot Path Planning: Adaptive Differential Sine-Cosine Algorithm

In this video, we explore the innovative Multi-Strategy and Self-Adaptive Differential Sine–Cosine Algorithm in MATLAB, enhancing multi-robot path planning. Surpassing the traditional SCA, this approach introduces diverse strategies for better adaptability and performance, achieving a 42% improvement in navigating complex environments. Discover its application, comparisons with leading algorithms, and its potential to transform robotics.
🔻YouTube: https://youtu.be/4ZSgFP-G-jY
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#Robotics #PathPlanning #MATLABSimulation #AlgorithmImprovement #MultiRobotSystems #AdaptiveAlgorithms #SineCosineAlgorithm #MetaheuristicAlgorithms #EngineeringInnovation #TechExploration

❇️Mastering Optimization with Slime Mould Algorithm: A MATLAB Tutorial
Dive into our MATLAB tutorial on the Slime Mould Algorithm (SMA) for stochastic optimization. Learn how SMA, inspired by nature, addresses complex optimization problems. This video covers SMA's basics, its MATLAB implementation, and showcases its effectiveness with visualizations and examples, catering to both beginners and experts. Ideal for researchers, students, and enthusiasts in computational intelligence, this tutorial is designed to enrich your optimization knowledge and spark innovation.
🔻YouTube: https://youtu.be/FqDkJSRGBiU
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#SlimeMouldAlgorithm #OptimizationTutorial #MATLABCoding #StochasticOptimization #AlgorithmVisualisation #ComputationalIntelligence #MATLABTutorial #EngineeringEducation #ScienceAndTechnology #ResearchInnovation

❇️Bacterial Foraging Optimization (BSO) ❇️

The text describes a 2D optimization problem aiming to minimize the distance between a position (x1, x2) and the target point (1, 2), with the optimal solution being (1, 2) where the fitness value is zero. It introduces the Bacterial Swarm Optimization (BSO) algorithm, a heuristic method inspired by bacterial foraging behavior. The algorithm operates through a population of individuals that navigate the search space to find the optimal solution based on fitness values and probabilistic rules. It adapts step size and swim length for a balance between exploration and exploitation, and uses elimination-dispersal events to avoid local optima. The algorithm's effectiveness depends on parameter selection and the problem's nature.
🔻YouTube: https://youtu.be/XvQw0RALeTo
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#BSO #algorithm #heuristic #optimization #search_space #bacteria #population #exploration #exploitation
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🟢R2024a Release Highlights🟢
#MATLAB

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❇️Major Updates:
- Computer Vision Toolbox: Deploy YOLOX object detection; conduct team-based labeling; perform real-time visual SLAM.
- Deep Learning Toolbox: Support architectures such as transformers; import and co-simulate PyTorch and TensorFlow models.
- GPU Coder: Generate generic CUDA for deep learning; use single memory manager and profile code for MEX code generation.
- Instrument Control Toolbox: Use the Instrument Explorer app to manage devices with IVI and VXIplug&play drivers without writing code.
- Satellite Communications Toolbox: Model multiplatform scenarios and perform visibility and communications link analyses on them.
- UAV Toolbox: Design and deploy flight controller for a vertical take-off and landing (VTOL) UAV with PX4 hardware-in-the-loop simulation; interface with PX4 Cube Orange Plus and Pixhawk 6c autopilots.

❇️Transitions:
- Simulink 3D Animation: Simulate and visualize dynamic systems in Unreal Engine 5.1 with new prebuilt scenes, actors, and sensors.
- SoC Blockset: Prototype and test on SDR and vision hardware with SoC Blockset Support Package for Xilinx Devices.

❇️MATLAB and Simulink Updates:
- Editor Spell Checker: Check spelling in text and comments in MATLAB code files.
- Simulink Editor: Preserve signal line shape when moving and resizing blocks.

❇️MATLAB:
- Local Functions: Define functions anywhere in noscripts and live noscripts.
- Python Interface: Convert between MATLAB tables and Python Pandas DataFrames.
- Python Interface: Interactively run Python code with Run Python Live Editor task.
- REST Function Service: Call MATLAB functions from any local or remote client program using REST.
- Secrets in MATLAB Vault: Remove sensitive information from code.
- ode Object: Solve ODEs and perform sensitivity analysis using SUNDIALS solvers.

❇️Simulink:
- Simulink Solver: Use local solvers for components with faster dynamics.
- Simulation Object: Control the execution and tune parameter values of noscripted simulations.
- MATLAB Apps: Create a custom app that interfaces with a Simulink model using MATLAB App Designer.

❇️Support Packages
- 6G Exploration Library for 5G Toolbox
- Audio Toolbox Interface for SpeechBrain Library
- Computer Vision Toolbox Model for Pose Mask R-CNN 6-DOF Object Pose Estimation
- Databricks ODBC Driver
- Embedded Coder Support Package for Infineon AURIX TC3x Processors
- Lidar Toolbox Model for RandLA-Net Semantic Segmentation
- Lidar Toolbox Support Package for Hokuyo Lidar Sensors
- MariaDB ODBC Driver
- PostgreSQL ODBC Driver

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#matlab_2024