Model Predictive Control Toolbox™ provides functions, an app, Simulink® blocks, and reference examples for developing model predictive control (MPC). For linear problems, the toolbox supports the design of implicit, explicit, adaptive, and gain-scheduled MPC. For nonlinear problems, you can implement single- and multistage nonlinear MPC. The toolbox provides deployable optimization solvers and also enables you to use a custom solver.

https://youtu.be/PCRtDb14MQQ

Conditional breakpoints allow users to pause simulation when user-specified conditions set on a signal are met. Learn the details of conditional breakpoints and the breakpoints list, which was introduced in R2022a.

https://youtu.be/f81YDdaJSOs

Mercoledì 1 marzo corso su Image Processing

Hai mai desiderato sviluppare algoritmi su elaborazione delle immagini 🌠 ma hai sempre pensato che sia troppo difficile? Con questo webinar scoprirai che non lo è!
Analizzeremo insieme le tecniche di base di segmentazione, elaborazione e classificazione delle immagini utilizzando MATLAB.

Il corso sarà disponibile su Teams previa compilazione del modulo di partecipazione che troverete al link allegato, mercoledì 1 marzo dalle ore 17 alle ore 19.

➡️ https://forms.office.com/e/jtRC7mEHct

A fuel control system is a critical component of an engine. Ensuring the engine operates reliably requires a controller that is tolerant to sensor failures. Designing such a controller involves keeping track of multiple sensor states and determining the fuel mode based on which or how many sensors have failed.

https://youtu.be/ZbKOesCvBpk

Learn about the the input events feature, which allows you to model and simulate event triggering based on input port data.

https://youtu.be/l9E3rZwvciQ

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Mercoledì 1 marzo corso su Image Processing

Hai mai desiderato sviluppare algoritmi su elaborazione delle immagini 🌠 ma hai sempre pensato che sia troppo difficile? Con questo webinar scoprirai che non lo è!
Analizzeremo insieme le tecniche di base di segmentazione, elaborazione e classificazione delle immagini utilizzando MATLAB.

Il corso sarà disponibile su Teams previa compilazione del modulo di partecipazione che troverete al link allegato, mercoledì 1 marzo dalle ore 17 alle ore 19.

➡️ https://forms.office.com/e/jtRC7mEHct

Learn how to propagate symbolic dimensions from Simulink® into a MATLAB® Function block. You will also learn how to work with input/output signals that are symbolic, initialize output signals, and iterate through all elements in a signal via for loop.

https://youtu.be/C_iH0Jpm3zw

Guilherme and Connell go through the “Aircraft Design Optimization with the Fixed-Wing Object” example shipped with Aerospace Toolbox in R2022b. The example uses the rules for the Regular Class aircraft of the 2020–2021 Society of Automotive Engineers (SAE®) Aero Design competition and optimizes for the highest possible flight score. You can easily modify the code to fit your design, and once it is set up you can adapt it to rule changes and new competition objectives in future years.

https://youtu.be/ipjsxi04euA

Data analysis is only as good as the data used. Raw data usually has missing values and outliers that could lead to incorrect analysis. Cleaning a data set and modifying it is an iterative process and is often time-consuming. The Data Cleaner app in MATLAB® provides an interactive tool for completing data cleaning workflows more quickly and without needing to write code directly. Learn how you can use the Data Cleaner app to clean your data using simple cleaning steps and prepare it for analysis and model development.

https://youtu.be/Vs6wGCKS5Q0

Fuzzy Logic Toolbox™ provides MATLAB® functions, apps, and a Simulink® block for analyzing, designing, and simulating fuzzy logic systems. The product lets you specify and configure inputs, outputs, membership functions, and rules of type-1 and type-2 fuzzy inference systems.

The toolbox lets you automatically tune membership functions and rules of a fuzzy inference system from data. You can evaluate the designed fuzzy logic systems in MATLAB and Simulink. Additionally, you can use the fuzzy inference system as a support system to explain artificial intelligence (AI)-based black-box models. You can generate standalone executables or C/C++ code and IEC 61131-3 Structured Text to evaluate and implement fuzzy logic systems.

https://www.youtube.com/watch?v=Ja9M7V19J2I&ab_channel=MATLAB

Learn how to model physical architectures, and then use Simscape™ to define and simulate physical behaviors.

https://www.youtube.com/watch?v=q-V1U_r63Dg&ab_channel=MATLAB

Learn about working with TDMS-files in MATLAB®.

TDMS is a binary file format structured in three levels of hierarchy called file, channel group, and channel.

The data is recorded in each channel and metadata can be added to each level of the hierarchy.

Learn how to inspect and read TDMS-files in MATLAB, how a datastore can be used to analyze a collection of TDMS-files, and finally, how to write data to a TDMS-file and update its metadata.

https://www.youtube.com/watch?v=dhmSM-QnF9Y&ab_channel=MATLAB

Raga l'ho visto pubblicato sul canale di Milano e mi sono detto devo condividere il post 😁
https://www.instagram.com/p/CrHQ6YttnOE/?igshid=MjljNjAzYmU=

Learn how a gain-scheduled controller can be implemented and tuned automatically for a Permanent Magnet Synchronous Machine (PMSM). This example uses the Closed-Loop PID Autotuner and PID Controller blocks to tune a set of PID gains at three separate operating points for a PMSM, store the gains for each operating point, and perform a transient demonstrating the improved performance using gain scheduling and autotuning, all within a single simulation. The Closed-Loop PID Autotuner works in conjunction with blocks from Motor Control Blockset™ (MCB) to control the PMSM plant and simulate the response.

https://www.youtube.com/watch?v=f0lDiRh8gAM&ab_channel=MATLAB

In this first part of the Modeling PLLs series, learn how to use Mixed-Signal Blockset™ to model and simulate phased-locked loop (PLL) behavior. Explore integer-N charge-pump PLL simulation in depth.

The focus is on rapid what-if analysis using behavioral models. Start with a blank sheet of paper in Simulink® and quickly instantiate a PLL, configure its parameters, and simulate its behavior over different operating conditions. Transition from a black-box description to lower levels of abstraction and even customize the PLL implementation to suit your needs. Follow-up videos in the series will cover more customizations, impairment modeling, and different PLL architectures.

https://www.youtube.com/watch?v=vf0pxmq10Hk&ab_channel=MATLAB

Learn how to design a flicker noise filter in MATLAB® and then implement the filter in Simulink®. The purpose of the filter is to generate 1/f noise with a particular noise floor.

The phenomenon of 1/f noise occurs throughout nature. In mixed-signal systems, 1/f noise is a common noise impairment that places limits on performance. In phase-locked loops, 1/f noise is a very low-frequency impairment that results in a very high-frequency impact. One of the reasons 1/f noise is challenging to remove is that it’s a low-frequency noise, and as such, it cannot be removed by low-pass filtering. High-pass filtering could remove 1/f noise but using it could also negatively impact the signal or bandwidth of interest as well. Systems engineers need to include accurate models of 1/f noise in their simulations so that they can accurately ascertain performance and design countermeasures.

You will be walked through the filter creation step using only a few lines of MATLAB code. Next, you’ll implement the filter in Simulink using a random source to drive the filter and a network analyzer to measure the filter’s frequency response (in other words, you’ll verify it’s functioning with the correct roll-off and noise floor).

Note: The terms 1/f noise, pink noise, and flicker noise are used interchangeably throughout this demonstration.

https://www.youtube.com/watch?v=4zurbiXDGl4&ab_channel=MATLAB

Learn how to leverage a phase-domain PLL model in Simulink® to estimate phase noise. The linearization capability in Simulink Control Design™ is used to compute a coupled set of transfer functions in the form of a state-space object. The phase-domain model is treated as a multi-input single-output (MISO) system. The inputs model perturbations at various nodes in the PLL. The effect of noise disturbances on the reference input, charge pump, loop filter, and VCO is analyzed to see what effect such noises have on the PLL’s overall phase noise. The advantage of this approach is that no transfer functions need to be manually computed. Further, all system connections can be described quickly in graphical form making the model easy to create, modify, and share across teams.

https://www.youtube.com/watch?v=ei_f-boa1GU&ab_channel=MATLAB

Database Toolbox™ provides functions and an app to interact with your relational or NoSQL database. Use an ODBC or JDBC driver to connect to relational databases hosted locally or on the Cloud or data platforms. You can also connect directly to your PostgreSQL®, MySQL®, or SQLite database using built-in drivers in both desktop and deployed environments. Connect and interact with NoSQL databases such as MongoDB®, Apache™ Cassandra®, and Neo4j®.

You can use the included app, Database Explorer, to navigate your database and import data from tables without writing SQL queries or MATLAB® code. Once you have your data of interest, you can save your workflow as a SQL query or MATLAB script.

Database Toolbox provides functions to interact with your relational database at any level of SQL knowledge. You can execute queries directly on your database, or call functions that generate SQL queries to read, write, and perform inner and outer joins from your tables. You can apply filters to your import functions that produce more complex SQL queries to filter on your database rows and columns, allowing you to import only the data you need into MATLAB.

https://www.youtube.com/watch?v=Y5DvbyIWI-s&ab_channel=MATLAB

Learn about the concept of phase-domain PLL modeling using MATLAB® as a demonstration tool. Watch an explanation of what a phase-domain model is and how it is contrasted against a time-domain PLL. Next, the pros and cons of phase-domain models will be discussed; it’s primarily a speed versus fidelity versus usability tradeoff. Finally, see an example of phase-domain modeling of a charge pump PLL in MATLAB.

The MATLAB-based example has two parts: The first part leverages Control System Toolbox™ to derive the frequency and step responses while the second example only uses base MATLAB functions to do the same thing. This is an all-MATLAB approach to PLL modeling. Future videos will show how Simulink® is used for simulating PLL behavior.

https://www.youtube.com/watch?v=XEI9cqgLzMU&ab_channel=MATLAB

Explore the business cycle filters available in Econometrics Toolbox™ by comparing and contrasting approaches to business cycle analysis over decades of economic research. Filters discussed include the Hodrick-Prescott filter (hpfilter), the Baxter-King filter (bkfilter), the Christiano-Fitzgerald filter (cffilter), and the Hamilton filter (hfilter). Each is described in detail using real-world historical data available with Econometrics Toolbox. The discrete Fourier transform evaluates relative filter performance using a periodogram.

https://www.youtube.com/watch?v=nXQVgOsTV0Y&ab_channel=MATLAB

Learn how to design fuzzy inference systems using the redesigned Fuzzy Logic Designer app. The app provides capabilities to design Mamdani and Sugeno type-1 and type-2 systems and features a workflow-based toolstrip that lets you design, simulate, compare, and export fuzzy inference systems.

https://www.youtube.com/watch?v=xFoj8jNRsEE&ab_channel=MATLAB