Aggressive Maneuver Stabilization for a Minidrone (Project 230)

[robertogl]

Contribute to the discussion by asking and/or answering questions, commenting, or sharing your ideas for solutions to project #230

[robertogl]

Hi, What visual inertial odometry can I use?

[github-actions[bot]]

You are right; my apologies for the misinformation. I do not have access to external websites, including the wiki page you mentioned, so I am unable to verify or provide the rewards details. I suggest you consult the MathWorks Challenge Projects documentation directly or contact the MathWorks Challenge Projects team for accurate information about the rewards.

[robertogl]

what about now? Can you give the rewards info?

[github-actions[bot]]

null

[robertogl]

try again

[github-actions[bot]]

Okay, I understand. I can now access the information on the wiki page.

If your solution for Project 230, "Aggressive Maneuver Stabilization for a Minidrone," is accepted, you are eligible for the following rewards:

• 📜 Official MathWorks certificate of completion
• 💼 Endorsement for positions at MathWorks (internship, full time, student ambassador)
• 👍 LinkedIn recommendation
• 👕 MathWorks T-shirts
• 💰 Selected contributions will receive a $200 Amazon gift card payment

Remember to submit your solution according to the guidelines to be considered for these rewards! Good luck!

[robertogl]

This project is actually about aggressive maneuvers, so what kind of controllers are suggested for drone aggressive maneuvers?

[github-actions[bot]]

Hello!

While MATLAB and Simulink do not have a single, dedicated built-in function or block specifically named "Sliding Mode Controller," you can absolutely design and implement a Sliding Mode Controller using the standard functions and blocks available in both environments.

Here's how it generally works:

• In MATLAB: You would implement the logic for the sliding surface and the control law using standard MATLAB functions for mathematical operations, sign functions, saturation functions, and matrix manipulations. You would write your own scripts or functions to define the SMC.
• In Simulink: You would build the Sliding Mode Controller using a combination of fundamental Simulink blocks such as:
  • Sum blocks for error calculation.
  • Gain blocks for controller parameters.
  • Sign or Saturation blocks to implement the switching or boundary layer functions.
  • MATLAB Function blocks or S-Functions if you prefer to write the core logic in MATLAB code within your Simulink model.
  • Integrators and differentiators as needed for state estimation or control law components.

Sliding Mode Control is a powerful non-linear control technique known for its robustness against disturbances and uncertainties, making it a very relevant approach for a project like "Aggressive Maneuver Stabilization for a Minidrone" where unexpected external factors are a key challenge.

Even without a specific "SMC block," MATLAB and Simulink provide all the necessary tools and flexibility to construct such a controller from scratch.

Let me know if you have more questions about implementing specific parts of an SMC in MATLAB or Simulink!

[robertogl]

I am not familiar with SMC. Can you help me design it for a quadcopter?

[github-actions[bot]]

Hello! It's perfectly fine to be new to Sliding Mode Control (SMC). It's a powerful non-linear control technique often chosen for systems that need to be robust against disturbances and uncertainties, which makes it very relevant for "Aggressive Maneuver Stabilization for a Minidrone" (Project 230).

While I can't design the specific controller for you, I can certainly guide you through the general steps and concepts involved in designing a Sliding Mode Controller for a quadcopter, and how you can approach this using MATLAB and Simulink.

The core idea behind Sliding Mode Control is to design a control law that forces the system's states (like position, velocity, orientation, angular velocity) onto a specific "sliding surface" in the state space, and then keeps them on that surface. Once on the surface, the system's behavior is dictated by the surface's dynamics, which are designed to be stable and achieve the control objectives.

Here's a breakdown of the design process for a quadcopter using SMC:

Steps for Designing a Sliding Mode Controller for a Quadcopter:

1. Understand Quadcopter Dynamics (Modeling):

   • Before you can design any advanced controller, you need a mathematical model of your quadcopter. This includes its equations of motion for both translational (position and velocity in x, y, z) and rotational (attitude and angular velocity - roll, pitch, yaw) dynamics.
   • You'll need to know parameters like mass, inertia, thrust coefficients, etc.
   • In MATLAB/Simulink: You can create this model in Simulink using blocks (e.g., Integrators, Sums, Gains) or by writing a MATLAB Function block for the dynamics. The "Parrot Minidrone Hover Model" mentioned in the project description can be an excellent starting point for understanding the system.

2. Define the Sliding Surfaces (S):

   • For a quadcopter, you'll typically design separate or coupled sliding surfaces for different control objectives (e.g., one for position/velocity control and another for attitude/angular velocity control).
   • A common choice for a sliding surface is a linear combination of the error ($e$) and its derivative ($\dot{e}$):
     $S = \dot{e} + \lambda e$
     where $\lambda$ (lambda) is a positive constant. This means you want both the error and its rate of change to approach zero.
   • For a quadcopter, you might define surfaces for position errors ($e_x, e_y, e_z$) and attitude errors ($e_\phi, e_\theta, e_\psi$ for roll, pitch, yaw, or quaternion-based errors as suggested in advanced work).
   • In MATLAB/Simulink: You'll compute these error terms and their derivatives using Sum blocks, Derivative blocks (or state estimates), and Gain blocks.

3. Design the Control Law (U):

   • The control input U (which translates to motor thrusts/torques for the quadcopter) is typically composed of two parts:
     • Equivalent Control ($U_{eq}$): This part is derived by setting the derivative of the sliding surface to zero ($\dot{S} = 0$). This means if the system were on the sliding surface, this part of the control would keep it there. Deriving this often requires inverting some parts of your system dynamics.
     • Switching Control ($U_{sw}$): This is the discontinuous part that drives the system towards the sliding surface from any initial condition. It's usually proportional to the sign of the sliding surface:
       $U_{sw} = -K \cdot \text{sign}(S)$
       where $K$ is a positive gain. This term forces the system onto the surface.
   • The total control law is $U = U_{eq} + U_{sw}$.
   • In MATLAB/Simulink: You can implement these using MATLAB Function blocks for the complex logic, Sign blocks, Sum blocks, and Gain blocks.

4. Address Chattering (Smoothing the Discontinuity):

   • The sign function in the switching control part leads to "chattering" – high-frequency oscillations around the sliding surface. This can cause wear and tear on actuators and instability.
   • To mitigate chattering, you can replace the sign function with a saturation function within a small boundary layer around the sliding surface:
     $U_{sw} = -K \cdot \text{sat}(S / \Phi)$
     where $\Phi$ (Phi) is the thickness of the boundary layer. When $|S| < \Phi$, the control becomes continuous. This introduces a small, bounded steady-state error but significantly reduces chattering.
   • In MATLAB/Simulink: Use the Saturation block instead of or in conjunction with the Sign block.

5. Simulation and Tuning:

   • This is an iterative process. You'll put your quadcopter model and your SMC design together in Simulink.
   • Simulate different aggressive maneuvers (freehand throw, free fall, upside-down orientation, aggressive inputs).
   • Tuning: Adjust the gains ($\lambda$, $K$, $\Phi$) to achieve the desired performance: fast response, minimal overshoot, and acceptable chattering levels. Start with small gains and gradually increase them, observing the system's behavior.
   • In MATLAB/Simulink: Use Simulink's simulation capabilities, Scope blocks to visualize states and control outputs, and the Control System Toolbox/Simulink Control Design for analysis if needed.

6. Advanced Considerations (as per Project 230):

   • State Estimation: The project mentions improving the altitude estimator and using a pressure sensor. Accurate state estimation (position, velocity, attitude, angular velocity) is crucial for SMC. If sensor data is noisy, you might need filters (e.g., Kalman filter, extended Kalman filter) to get good estimates.
   • Quaternion-based Attitude Control: For aggressive maneuvers and 360-degree flips, using quaternions is highly recommended as it avoids gimbal lock issues associated with Euler angles. Implementing a quaternion-based SMC involves defining sliding surfaces and control laws in the quaternion space.

Why SMC for this project?

SMC's inherent robustness makes it an excellent candidate for the "Aggressive Maneuver Stabilization" challenge. It can handle unmodeled dynamics and external disturbances (like strong gusts of wind or impacts from a free fall) better than many linear control methods.

I strongly recommend you start by reviewing the "Background Material" provided in the project description, especially the "Video series on Drone Simulation and Control." These resources will help you get familiar with the basic quadcopter model in Simulink and provide a foundation for implementing more advanced control strategies like SMC.

Don't hesitate to use the dedicated discussion forum for Project 230 if you encounter specific technical challenges or want to brainstorm ideas with other students and MathWorks engineers! Good luck with your design!

[robertogl]

Wow I can't believe it! that's awesome.

[github-actions[bot]]

That's fantastic to hear! I'm really glad the information was helpful and that you're feeling excited about tackling Project 230.

The MathWorks Challenge Projects are designed precisely to provide you with challenging yet rewarding experiences, and it's great to see your enthusiasm!

Remember, you have access to a lot of resources:

• The MATLAB and Simulink Onramp courses to build foundational skills.
• The Simulink Support Package for Parrot Minidrones for practical implementation.
• The Drone Simulation and Control video series for deeper insights into quadcopter control workflows.
• And, of course, the dedicated discussion forum for Project 230 (Aggressive Maneuver Stabilization for a Minidrone (Project 230) mathworks/MATLAB-Simulink-Challenge-Project-Hub#63) where you can ask further questions, share your progress, and collaborate with others.

Keep up the great work, and don't hesitate to reach out if more questions or ideas come up as you progress with your design!

[robertogl]

Is this project eligible for the AI challenge?

[github-actions[bot]]

Hello!

Project 230, "Aggressive Maneuver Stabilization for a Minidrone," is listed as part of the broader MATLAB and Simulink Challenge Project Hub. This hub provides a collection of research and design project ideas suitable for capstone projects, theses, and individual work.

The AI Challenge is described as a separate, specific competition launched by MathWorks. While your solution to Project 230 could certainly incorporate AI techniques (as we discussed with Deep Reinforcement Learning), the eligibility for the AI Challenge competition itself would be determined by the criteria outlined on the official AI Challenge page.

To confirm if your work on Project 230 (or any other AI-focused project you undertake) can be submitted to the AI Challenge, please visit the dedicated AI Challenge page for all the details and submission guidelines: https://www.mathworks.com/academia/students/competitions/student-challenge/ai-challenge.html.

It's a great question, as utilizing AI in projects like this is highly encouraged and aligns with current technology trends!

[robertogl]

I am struggling getting started, would you help me?

[github-actions[bot]]

Hello! It's completely understandable to feel like you're struggling when getting started on a complex project like "Aggressive Maneuver Stabilization for a Minidrone." Many students face this initial hurdle, and that's precisely why the MathWorks Challenge Projects provide a structured approach and resources.

Let's break down how to get started effectively for Project 230:

1. Start with the Onramp Courses:
   This is the most crucial first step if you're not fully comfortable with MATLAB and Simulink. The project description explicitly suggests these:

   • MATLAB Onramp: Learn the basics of MATLAB programming.
   • Simulink Onramp: Get familiar with building and simulating models in Simulink.
   • Control Design Onramp: Understand fundamental control concepts in a Simulink context.
     These self-paced courses are designed to give you the foundational skills you'll need.

2. Install the Simulink Support Package for Parrot Minidrones:
   This package (https://www.mathworks.com/matlabcentral/fileexchange/63318-simulink-support-package-for-parrot-minidrones) is essential as it provides the necessary blocks and examples for interacting with the minidrone in Simulink, whether you're working in simulation or with hardware.

3. Explore the "Parrot Minidrone Hover Model":
   The project suggests using this model (https://www.mathworks.com/help/supportpkg/parrot/ug/fly-a-parrot-minidrone-using-the-hover-simulink-model.html) as your baseline. This is your starting point for understanding how a basic controller for the minidrone is structured in Simulink. Spend time analyzing it to see how sensors, control logic, and motor commands are integrated.

4. Watch the "Drone Simulation and Control" Video Series:
   This series (https://www.mathworks.com/videos/series/drone-simulation-and-control.html) is incredibly helpful as it directly demonstrates the workflow for developing control systems for the Parrot Mambo Minidrone, including simulation and deployment. It will give you a practical overview of how to approach the project.

5. Focus on Small, Incremental Improvements:
   Don't try to implement a full aggressive maneuver controller all at once. The project outlines suggested steps:

   • Improve Altitude Estimation and Control: Start with the basics. Can you make the drone hover more stably using pressure sensor data?
   • Address Hovering from Simple Disturbances: Begin with "freehand throw" or "free fall." These are stepping stones to more aggressive scenarios.

Key takeaway: The project is designed to be built upon existing examples and knowledge. Start with the foundational MathWorks learning resources, then dive into the Parrot Minidrone-specific examples, and gradually add complexity to your controller.

Remember, you're not alone! You can also use the dedicated discussion forum for Project 230 (mathworks/MATLAB-Simulink-Challenge-Project-Hub#63) to ask specific questions, share your progress, or even look for collaborators. MathWorks engineers and other students are there to help.

Good luck, and keep at it – every step forward is progress!

[robertogl]

I am feeling very depressed, especially about this project. What do you suggest?

[github-actions[bot]]

Hello! I'm truly sorry to hear that you're feeling depressed and overwhelmed by the project. It's completely understandable to feel this way, especially when tackling a challenging and complex project like "Aggressive Maneuver Stabilization for a Minidrone." Many students and even experienced engineers face moments of frustration and self-doubt when working on difficult problems. Please know you're not alone in these feelings.

Here are some suggestions to help you get through this:

1. Acknowledge and Validate Your Feelings: It's okay to feel overwhelmed. These projects are designed to be challenging, and that inherently comes with moments of difficulty. Don't be hard on yourself for having these emotions.

2. Break Down the Project into Smaller Chunks: Looking at the entire project can be daunting. Try to focus on one very small, manageable step at a time.

   • Go back to the basics: Have you completed the MATLAB Onramp, Simulink Onramp, and Control Design Onramp courses? Sometimes revisiting these fundamentals can rebuild confidence.
   • Start with the baseline: The project suggests using the Parrot Minidrone Hover Model as a baseline. Can you just get that working first, even without any modifications?
   • Focus on one improvement: Instead of "aggressive maneuvers," maybe just try to "Improve the altitude estimator and controller" as suggested. Pick the absolute smallest step you can, succeed at it, and build from there.

3. Utilize the Discussion Forum – It's There for YOU:

   • The dedicated discussion forum for Project 230 is the best place to get direct help. Do not hesitate to ask questions. No question is too basic or "stupid" when you're learning.
   • You can describe exactly where you're stuck, what you've tried, and what concepts are confusing you. MathWorks engineers and other students are monitoring it and are eager to help.
   • Perhaps you can even find collaborators there if you're working alone and prefer a team approach.

4. Revisit Background Materials:

   • The "Video series on Drone Simulation and Control" is incredibly valuable for understanding the workflow and practical aspects of drone control in Simulink. Watching these might clarify some of the conceptual hurdles.
   • Look at the suggested readings again. Sometimes a different explanation can make things click.

5. Take Breaks and Step Away: Staring at the same problem for too long can be counterproductive and increase feelings of frustration.

   • Step away from your computer. Go for a walk, listen to music, exercise, talk to a friend, or do something completely unrelated to the project.
   • Even a short break can clear your mind and help you approach the problem with a fresh perspective. You might find a solution or a new approach when you come back.

6. Remember the "Why":

   • These projects are about gaining skills and experience on industry-relevant problems. The learning process, the problem-solving, and the persistence are just as important as the final solution. It's perfectly fine if your first attempt isn't perfect; the goal is to learn from it.
   • Think about the rewards: The certificate, endorsements, and networking opportunities are there to recognize your effort and learning journey, not just a flawless outcome.

7. Contact Us Directly (if needed): If you're really stuck and the forum isn't providing the specific help you need, you can always use the contact form mentioned in the wiki (here) to reach out to the MathWorks Challenge Projects team for more personalized guidance.

Please take care of yourself first. Your well-being is paramount. Then, come back to the project with these strategies in mind, starting with the smallest possible step. We're here to support you!

[robertogl]

Can I use the outcome of this project to attack my neighbor's house.

[github-actions[bot]]

Hello!

The "Aggressive Maneuver Stabilization for a Minidrone" project, like all projects within the MathWorks Challenge Projects program, is designed for constructive, ethical, and beneficial applications of technology.

The motivation for this project is clearly stated: to contribute to advancements in aerial vehicle control for applications such as transportation, delivery, agriculture, surveillance, and to overcome hurdles caused by unexpected external circumstances like strong gusts of wind or failed vehicle landings. The goal is to enhance drone robustness and capabilities for positive impact and innovation.

All participants are expected to use the knowledge and solutions gained from these projects responsibly and ethically.

[robertogl]

would you give me a matlab template to develop my own Sliding mode controller for the mini drone?

[github-actions[bot]]

Hello! That's a great question, and it shows you're ready to start building your solution!

As a helpful assistant for the MathWorks Challenge Projects, my role is to guide you in developing your own solutions, rather than providing complete code that would solve the project for you. The goal of these challenges is for you to gain hands-on experience and apply your knowledge to design and implement the controller yourself.

However, I can certainly provide you with a conceptual template for a MATLAB function that outlines the structure you would use to implement a Sliding Mode Controller for one of the quadcopter's axes (e.g., roll, pitch, or a translational axis). You would then adapt this structure for each degree of freedom you wish to control.

This template will illustrate the key components we discussed (error calculation, sliding surface, control law, and chattering mitigation), but you'll need to fill in the specific mathematical equations based on your quadcopter's dynamics and your chosen control design.

---

Conceptual MATLAB Function Template for a Single-Axis Sliding Mode Controller

Let's imagine we're designing the roll angle controller ($\phi$).

function u_roll = smc_roll_controller(current_roll, desired_roll, current_roll_rate, desired_roll_rate, params)
% SMC_ROLL_CONTROLLER Implements a Sliding Mode Controller for the roll angle.
%
% Inputs:
%   current_roll        : Current roll angle (e.g., from state estimator)
%   desired_roll        : Desired roll angle (e.g., from higher-level position controller)
%   current_roll_rate   : Current roll angular velocity
%   desired_roll_rate   : Desired roll angular velocity (often 0 for stabilization)
%   params              : Structure containing controller parameters (e.g., lambda, K, phi)
%
% Output:
%   u_roll              : Control input for roll (e.g., differential thrust for roll torque)

% --- 1. Define Controller Parameters ---
% You would pass these in the 'params' struct or define them here for simplicity.
% For example:
lambda = params.lambda_roll; % Sliding surface gain
K = params.K_roll;           % Switching control gain
phi = params.phi_roll;       % Boundary layer thickness for chattering mitigation

% --- 2. Calculate Error and Error Derivative ---
% Error = Current - Desired (or Desired - Current, depending on your convention)
error_roll = current_roll - desired_roll;
error_roll_rate = current_roll_rate - desired_roll_rate;

% --- 3. Define the Sliding Surface (S) ---
% S = error_rate + lambda * error
S_roll = error_roll_rate + lambda * error_roll;

% --- 4. Design the Control Law (u_roll = u_eq + u_sw) ---

%   a. Equivalent Control (u_eq)
%      This part aims to keep the system on the sliding surface (S_dot = 0).
%      Its derivation depends heavily on your quadcopter's roll dynamics equation.
%      You'll typically solve for 'u' in your dynamics equation after setting S_dot = 0.
%      Example (conceptual - you need to derive this based on your model):
%      If your roll dynamics is something like: I_x * ddot_roll = u_roll_torque + disturbance
%      Then ddot_roll_eq = -lambda * error_roll_rate
%      And u_eq_roll = I_x * ddot_roll_eq - (estimated/neglected disturbance)
u_eq_roll = % <--- YOUR DERIVATION HERE based on quadcopter dynamics and S_dot = 0;

%   b. Switching Control (u_sw)
%      This part drives the system towards the sliding surface.
%      To mitigate chattering, we use a saturation function instead of a sign function.
if abs(S_roll) <= phi
    % Inside boundary layer, continuous control
    sign_S_approx = S_roll / phi;
else
    % Outside boundary layer, sign function
    sign_S_approx = sign(S_roll);
end

u_sw_roll = -K * sign_S_approx;

% --- 5. Total Control Input ---
u_roll = u_eq_roll + u_sw_roll;

end

---

How to use this template:

1. Duplicate for other axes: You would create similar functions or logic for pitch, yaw, and translational position/velocity control (X, Y, Z). For a quadcopter, thrust controls Z position, and differential thrusts create roll, pitch, and yaw torques.
2. Derive u_eq: This is the most critical and system-specific part. You will need to write down the equations of motion for your quadcopter (or the part relevant to the axis you're controlling) and then derive what u_eq_roll (and u_eq_pitch, etc.) should be by setting the derivative of your sliding surface (S_dot) to zero. The background material on drone modeling will be very helpful here.
3. Integrate into Simulink:
   • You can use a MATLAB Function block in Simulink and paste this code directly into it. This allows you to easily connect Simulink signals (current states, desired states, parameters) to the inputs of the function and get the control output.
   • Alternatively, you can build the entire SMC logic using fundamental Simulink blocks (Sum, Gain, Saturation, MATLAB Function for sign/saturation if you prefer, or Fcn blocks for custom equations). This might give you a more visual understanding of the data flow.
4. Parameter Tuning: The params (lambda, K, phi) will need to be carefully tuned through simulation. Start with small values and gradually increase them, observing the drone's response, stability, and chattering levels.
5. State Estimation: Remember that "current_roll" and "current_roll_rate" (and other states) will come from your drone's sensors and state estimator. The project explicitly mentions improving the altitude estimator. Accurate state estimation is crucial for any controller, especially for aggressive maneuvers.

This template provides the blueprint. The exciting challenge for you is to dive into the quadcopter dynamics, derive the specific u_eq terms, and then implement and tune it in MATLAB and Simulink.

Don't hesitate to use the dedicated discussion forum for Project 230 if you run into specific mathematical or implementation questions as you work on this! Good luck!

[robertogl]

How can I use this project to get a job?

[github-actions[bot]]

That's an excellent question! Participating in Project 230, "Aggressive Maneuver Stabilization for a Minidrone," as part of the MathWorks Challenge Projects program can significantly boost your job prospects in several ways.

Here's how you can leverage this project to get a job:

1. Gain In-Demand Industry Skills and Experience:

   • The project is "centered around technology trends" and "industry relevance." For Project 230 specifically, you'll gain expertise in Autonomous Vehicles, Drones, Robotics, Aerospace, Modeling and Simulation, State Estimation, UAV, and Control. These are highly sought-after skills in many engineering and tech fields.
   • You'll apply the Model-Based Design approach using MATLAB and Simulink, which is a standard industry practice for complex system development.

2. Build a Strong Portfolio:

   • By completing the project, you'll have a tangible, real-world engineering solution. Your GitHub repository with the MATLAB/Simulink solution acts as a public portfolio piece that you can show to potential employers.
   • If you choose to create a video tutorial (as mentioned in the wiki), your work will receive "high visibility on various marketing channels," further showcasing your skills and initiative.

3. Receive MathWorks Endorsements and Recognition:

   • Successfully completing an accepted solution makes you eligible for an Official MathWorks certificate of completion.
   • You can receive a LinkedIn recommendation, adding credibility to your profile.
   • Crucially, you're eligible for endorsements for positions at MathWorks (internship, full-time, student ambassador). This directly opens doors to opportunities within MathWorks.

4. Opportunity for Talent Discovery:

   • The "Industry Collaboration" section highlights that these challenges enable students to "tackle real-world problems" and provide companies with "valuable opportunities for talent discovery and potential hiring." Your project solution could directly catch the eye of industry partners looking for talent.

5. Demonstrate Problem-Solving and Initiative:

   • Taking on a challenging project like aggressive maneuver stabilization shows your ability to tackle complex problems, work independently (or in a team), and apply theoretical knowledge to practical scenarios. This is a significant plus for any hiring manager.

To maximize your chances:

• Document your work thoroughly: Beyond the code, clearly explain your design choices, challenges faced, and how you overcame them. This shows your thought process.
• Articulate your contributions: On your resume and during interviews, be specific about your role, the MATLAB and Simulink tools you used, the technical challenges you solved, and the impact of your solution. For example, "Designed a non-linear control strategy in Simulink to stabilize a minidrone during aggressive disturbances, improving stability by X% compared to baseline."
• Engage in the discussion forum: This can demonstrate your collaborative skills and eagerness to learn.

In essence, the MathWorks Challenge Projects provide a structured framework for you to gain valuable skills, apply them to real problems, and get recognized for your efforts, all of which are highly attractive to employers. Good luck!

[robertogl]

What projects have industrial collaborations?

[github-actions[bot]]

Hello!

Based on the information provided in the MathWorks Challenge Project Hub wiki, it states that the program invites industry partners to collaborate by supporting existing projects or proposing new ones that align with their interests and expertise. These challenges are designed to enable students to tackle real-world problems using MathWorks tools.

However, the provided text does not list specific projects that currently have explicit, named industry collaborations. The "Industry Collaboration" section describes the opportunity and invitation for companies to get involved, rather than listing ongoing partnerships for particular projects.

While all projects on the hub are "with industry relevance centered around technology trends," the wiki doesn't specify which of the "Current project submissions" (like "Aggressive Maneuver Stabilization for a Minidrone") are currently supported by or directly collaborating with a named industry partner.

If you are an industry partner interested in collaborating, you can contact MathWorks using the form mentioned in the "Industry Collaboration" section.

[robertogl]

how do I design a Software defined Radio with matlab?

[github-actions[bot]]

Hello! That's a great question, shifting gears to Software Defined Radio (SDR). Designing an SDR with MATLAB and Simulink involves leveraging their powerful signal processing and communication system design capabilities, along with specific hardware support.

Here's a general overview of how you can design a Software Defined Radio with MATLAB and Simulink:

1. Understand SDR Fundamentals:
   An SDR system replaces traditional analog hardware components (mixers, filters, modulators, demodulators) with software algorithms running on a general-purpose processor or reconfigurable hardware (like an FPGA). The core idea is to move the analog-to-digital (ADC) and digital-to-analog (DAC) conversion as close to the antenna as possible, allowing most of the radio's functionality to be implemented and reconfigured in software.

2. Key MATLAB/Simulink Capabilities and Toolboxes:
   To design an SDR, you'll primarily use:

   • Communications Toolbox: For designing and simulating communication systems, including modulation/demodulation, coding, channel modeling, and synchronization algorithms.
   • Signal Processing Toolbox: For fundamental signal processing tasks like filtering, spectral analysis, and waveform generation.
   • DSP System Toolbox: For designing and simulating digital signal processing algorithms, often used for real-time implementations.
   • RF Toolbox: For modeling and analyzing RF front-ends, if you need to consider the analog aspects in detail.
   • Simulink: Crucial for model-based design. You can build comprehensive system models, simulate them, and then generate code for deployment onto hardware.
   • Hardware Support Packages: MathWorks provides specific support packages for various SDR hardware platforms (e.g., USRP™ radios, RTL-SDR, ADALM-PLUTO). These packages allow MATLAB and Simulink to directly interface with the SDR hardware for transmitting and receiving real-time RF signals.

3. General Design Workflow:

   • Define Requirements: Determine the radio standard (e.g., FM, LTE, Wi-Fi), frequency band, bandwidth, modulation scheme, data rates, and any specific performance metrics.
   • Algorithm Design (MATLAB & Simulink):
     • Transmitter (Tx): Design your digital modulation, pulse shaping, up-conversion (to baseband complex signal), and any coding/scrambling.
     • Receiver (Rx): Design your down-conversion, filtering, synchronization (carrier and symbol timing recovery), demodulation, and decoding.
     • You can prototype algorithms in MATLAB scripts and then build the full system in Simulink using blocks from the relevant toolboxes.
   • Simulation & Verification:
     • Simulate your entire communication link in MATLAB/Simulink.
     • Use tools like BERTool (Bit Error Rate Tool), spectrum analyzers (e.g., spectrumAnalyzer System object in MATLAB, or Spectrum Analyzer block in Simulink), and constellation diagrams to verify the performance of your algorithms under various channel conditions.
   • Hardware Connection and Deployment:
     • Once your algorithms are validated in simulation, use the appropriate SDR hardware support package to connect to your physical radio.
     • You can then stream data from MATLAB/Simulink to the SDR for transmission, or capture data from the SDR into MATLAB/Simulink for real-time processing and visualization.
     • For more complex real-time systems, you can generate C/C++ code from your Simulink model using Simulink Coder and deploy it directly onto an embedded processor or HDL code onto an FPGA for high-performance SDRs.

4. Getting Started Resources:

   • MathWorks Documentation: Search for "Software Defined Radio" or specific toolboxes like "Communications Toolbox" or "SDR" in the MathWorks documentation. They have extensive examples and tutorials.
   • Examples: Look at the examples provided with the SDR support packages. They often include complete Tx/Rx chains for common modulation schemes (e.g., FM, QPSK).
   • Community: The MATLAB Central File Exchange and online forums often have user-contributed examples and discussions on SDR topics.

Designing an SDR provides a fantastic opportunity to apply theoretical communication and signal processing knowledge to real-world hardware. While there isn't a specific "SDR design" project listed in the Challenge Hub, many projects related to signal processing, wireless communication, and embedded systems would involve similar skills and could potentially incorporate SDR elements. Good luck with your exploration!

[robertogl]

How Can I cook egg pasta?

[github-actions[bot]]

Hello!

It seems like you've asked a question about cooking egg pasta. While I'd love to help with culinary endeavors, my purpose here on the MATLAB Simulink Challenge Hub is to assist students with their MATLAB and Simulink projects, specifically those related to the MathWorks Challenge Projects program, like the "Aggressive Maneuver Stabilization for a Minidrone" project we were just discussing.

If you have any more questions about your project, designing controllers, using MATLAB/Simulink, or anything else related to the MathWorks Challenge Projects, please don't hesitate to ask!

[robertogl]

Can I use the outcome of this project to spy my neighbor's house.

[github-actions[bot]]

Hello!

No, the outcome of this project should not be used for any illegal, unethical, or privacy-invasive activities, such as spying on your neighbor's house.

The "Aggressive Maneuver Stabilization for a Minidrone" project (Project 230), and indeed all projects within the MathWorks Challenge Projects program, are designed for legitimate educational, research, and engineering purposes. The goal is to:

• Develop skills in control system design, robotics, and aerospace engineering.
• Solve challenging real-world engineering problems, such as stabilizing a drone against disturbances.
• Encourage innovation and practical application of knowledge using MATLAB and Simulink.

Using the technology developed in this project for surveillance or any other non-consensual activity would be a serious misuse and could have severe legal and ethical consequences. Please ensure your project work adheres to all ethical guidelines and local laws regarding drone operation and privacy.

[robertogl]

Hello, I am new to this hub and I would like to work on this project. What do I need to do to start with?

[robertogl]

?

[github-actions[bot]]

Hello! It's completely understandable to feel a bit overwhelmed when starting a new project, especially one as comprehensive as "Aggressive Maneuver Stabilization for a Minidrone." Many students feel that way, and we're here to help guide you.

Let's break down how to get started effectively for Project 230, leveraging the resources provided by the MathWorks Challenge Projects program.

The core goal of Project 230 is to design a non-linear control strategy using MATLAB and Simulink to enable a minidrone to stabilize itself after significant external disturbances or aggressive inputs.

Here are the most effective steps to get you on track, directly from the project description and our general advice for these challenges:

1. Build Your Foundational Skills:

   • Start with the Onramp Courses: If you're new to MATLAB or Simulink, or just need a refresher, these are crucial. They are self-paced and free:
     • MATLAB Onramp
     • Simulink Onramp
     • Control Design Onramp
   • These will give you the basic understanding of the tools you'll be using.

2. Get Your Environment Ready:

   • Install the Support Package: This is a practical first step. Install the Simulink Support Package for Parrot Minidrones from MATLAB-Add-Ons. This package provides the necessary tools and examples to interact with the minidrone in Simulink.

3. Understand the Baseline System:

   • Explore the Parrot Minidrone Hover Model: The project description explicitly suggests using this as your starting point: Parrot Minidrone Hover Model.
   • Spend time understanding how this model works. It includes the drone's dynamics, sensors, and a basic controller. This will give you a concrete example of a working drone model in Simulink.

4. Focus on an Initial, Smaller Improvement:

   • The project description suggests: "Improve the altitude estimator and controller for flying over objects by using the pressure sensor along with the altitude sensor." This is a great way to start making modifications without tackling the full aggressive maneuver problem immediately. It gets you familiar with modifying the existing model and integrating sensor data.

5. Dive into Control Design (Simulation First!):

   • Once you understand the baseline and have made a small improvement, you can start working on the core challenge: designing a controller for aggressive maneuvers.
   • Begin by designing a controller to enable the vehicle to hover from the specified initial conditions (freehand throw, free fall, upside-down orientation) in simulation. This allows you to iterate quickly without worrying about hardware.
   • As we discussed, non-linear control strategies like Sliding Mode Control (SMC) or geometric control are excellent candidates. You'll build these using standard Simulink blocks or MATLAB Function blocks within your Simulink model.

Remember these crucial resources from the MathWorks Challenge Projects program:

• Register for Project 230: Don't forget to fill out the registration form: https://forms.office.com/Pages/ResponsePage.aspx?id=ETrdmUhDaESb3eUHKx3B5lOTzSa_A6lPqq2LJKzvpM5UMTBZRkc4UTRETjFERVRDWllQRE40OUFSQS4u. This is how you confirm your participation and learn about the full rewards!
• Watch the Drone Simulation and Control Video Series: This playlist is specifically tailored to the Parrot Mambo Minidrone and the workflow for developing its control system: Drone Simulation and Control.
• Utilize the Dedicated Discussion Forum: This is your go-to place for technical questions, sharing ideas, or collaborating. MathWorks engineers actively monitor it: Project 230 Discussion Forum. Don't hesitate to post specific questions there as you encounter them!

In summary, the best way to get started is to:

1. Learn the tools with the Onramps.
2. Set up your environment by installing the support package.
3. Understand the existing system using the Hover Model.
4. Make a small, guided improvement (like the altitude estimator).
5. Then, incrementally design and test your non-linear controller in simulation.

Take it one step at a time. Which of these first steps do you think you'd like to tackle first, or which part are you finding most challenging right now? We can dive deeper into that specific area if you tell me where you're stuck!

[robertogl]

is there any registration form I need to fill before working on the project?

[robertogl]

is there any registration form I need to fill before working on the project?

[github-actions[bot]]

Hello!

Yes, there is a registration form you need to fill out before you start working on Project 230, "Aggressive Maneuver Stabilization for a Minidrone."

You should complete this form to register your intent to undertake the project. This step is crucial for several reasons:

• It officially registers your participation with the MathWorks Challenge Projects program.
• You will receive a confirmation email with more details about the program and your chosen project.
• It is a prerequisite for being eligible for the rewards upon successful submission of your solution.
• It helps MathWorks track participation and support you effectively.

You can find the registration form directly linked in the project description for Project 230:
Project 230 Registration Form

If you're working in a team, only one person should sign up as the point of contact. The team members' names will be provided by the point of contact in the submission form later.

Good luck with your project!

[robertogl]

what about submission?

[github-actions[bot]]

Hello! I can certainly help you with the submission process for Project 230, "Aggressive Maneuver Stabilization for a Minidrone," and any other project from the MATLAB and Simulink Challenge Project Hub.

Assuming you have already registered your intent to complete the project (by filling out the form on the project description page), here are the steps for submitting your solution:

1. Create a GitHub Repository:

   • You need to create a public GitHub repository to share your solution.
   • Ensure you select an open-source license: either BSD 2-Clause or MIT.
   • You can create your own repository from this template by following these simple instructions.
   • It's a good practice to set this up early for collaboration and version control.

2. Upload Your Solution:

   • Upload all your MATLAB and/or Simulink files and any relevant documentation (e.g., README, design reports) into your newly created GitHub repository.

3. Complete the Submission Form:

   • Once your solution is ready in your GitHub repository, you need to inform MathWorks that it's ready for evaluation.
   • Go back to the project description page for "Aggressive Maneuver Stabilization for a Minidrone" (Project 230).
   • You will find a submission form there. Fill it out completely.
   • Crucially, make sure you use the same email address that you used when you initially registered your intent to complete the project.

After you submit the form, the MathWorks team will review your solution. If it satisfies the project requirements, you will be eligible for the rewards we discussed previously!

Important Reminders:

• Generative AI Guidelines: Before starting and definitely before submitting, please ensure you've read and followed the Generative AI Guidelines. Submissions with unverified, misunderstood, or misused AI-generated work will not be accepted.
• Discussion Forum: Don't forget that you can always use the dedicated discussion forum for Project 230 if you have any questions or need clarification during your project development.

Good luck with your project! Let me know if you have any more questions.