Robust to Early Termination Model Predictive Control Toolbox (REAP-T)

🛠️ Getting Started:

1. Extract the YALMIP zip file to the location of the REAP-T file.
2. Run the DisREAP_UI function in MATLAB.

🚀 Usage:

1. Import your system and desired configurations:
   • After running DisREAP_UI, import your system model and specify your desired configurations, enable plots, and simulate the system to observe results.
2. Execute the package to start the control process.

📋 Input Fields Explained

System Matrices

1. Matrix A
   Represents the state-transition matrix of the system.
   Example:
   A = [0 1; -2 -3]

2. Matrix B
   Input matrix defining how the control inputs affect the system.
   Example: B = [0; 1]

3. Matrix C
   Output matrix, mapping the state to system outputs.
   Example:
   C = [1 0]

4. Matrix D
   Feedthrough matrix, directly connecting input to output.
   Example: D = 0

Constraints

5. X Constraint U.B. (Upper Bound)
   Upper bounds for the system state vector (x).
   Example: [5; 5]

6. X Constraint L.B. (Lower Bound)
   Lower bounds for the system state vector (x).
   Example: [-5; -5]

7. U Constraint U.B. (Upper Bound)
   Upper bounds for the control input vector (u).
   Example: [1]

8. U Constraint L.B. (Lower Bound)
   Lower bounds for the control input vector (u).
   Example: [-1]

Cost Function Weights

9. Matrix Qx
   Weighting matrix for penalizing state deviations in the cost function.
   Example:
   Qx = [1 0; 0 1]

10. Matrix Qu
    Weighting matrix for penalizing control efforts in the cost function.
    Example:
    Qu = [0.01]

11. Initial Condition ((x_0))
    Initial state of the system.
    Example: [0; 0]

Simulation Parameters

12. Prediction Horizon
    The number of future steps the controller optimizes for.
    Example: 10

13. Desired Target
    Desired value for the reference trajectory or desired steady-state configuration (Equilibrium point)

14. # Time Instants
    Total number of time steps for the simulation.
    Example: 100

15. Sampling Period ((\Delta T))
    Time interval between successive steps.
    Example: 0.2

16. Terminal Constraint The mode of the terminal constraints; could be prediction-based or Lyapunov-based.

Plots

17. Select Plots
    Choose which aspects of the system to visualize:

• States: Display state trajectories.
• Control Inputs: Show control signals over time.
• Output: Visualize system output.
• Sigma: Plot algorithm-specific sigma values.

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Algorithm Mode

18. Algorithm Mode Dropdown

• Automatic: Let the system decide optimal parameters (disables Omegastar input).
• Manual: Allows customization of the Omegastar parameter.

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This interface makes it easier to define and simulate discrete-time systems with constraints and optimization. Start exploring your systems now! 🚀

📚 Examples:

For guidance, the package includes two examples:

1. Parrot Bebop 2 Drone:
   • This example demonstrates the implementation of DisREAP on a Parrot Bebop 2 drone.
2. F-16 Aircraft Model:
   • This example showcases DisREAP applied to a F-16 Aircraft Model.

▶️ Running Examples:

1. Navigate to the examples directory:
   • Open the folder containing the examples within the extracted files.
2. Run the example scripts:
   • Follow the instructions in the example scripts to see the results and understand how to set up and execute your own system.

🤝 Support:

For any issues or questions, please refer to the issues page on GitHub.

Citing DisREAP:

If you use the DisREAP Toolbox, please use the following BibTeX entry:

@INPROCEEDINGS{Amiri:REAP-T,
AUTHOR="{Mohsen Amiri and Mehdi Hosseinzadeh",
TITLE="REAP-T: A MATLAB Toolbox for Implementing Robust-to-Early Termination Model Predictive Control",
BOOKTITLE="..... ",
ADDRESS="........",

ABSTRACT="........."}