Analytical Analysis of an OCP with System of ODEs in MATLAB
Version 1.1.0 (9.39 KB) by
Arindam Kumar Paul
Do the analytical analysis of an Optimal Control Problem with 3 ODEs and Objective Functions Graphically.
Just run the code, you are allowed to set 6 symbols here and you can extend. Here, a system with 3 ODEs is preferable. If you have more please do some code. Run, Enter Symbols you need, enter Equations (System of Odes with Control Variables, Input the Objective Function, Get the Necessary and Suficient Proof for the existence of Optimal Control, Get the value of control analytically, get the adjoint system, get the Hamiltonian, Lagrangian etc.)
Use Latex(vars) to get the latex code for the variable/expression.
Just Input like : a - gamma*p - v1*alpha*p- beta*p-delta*p-u1*p (Equations)
Equations be like :
Where, Initial Conditions are : ![](data:image/png;base64,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)
Objective Functional is defined by : ![Objective functional](data:image/png;base64,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)
Nothing, Just Run, If get an error, just turn 2-13 lines as comments by using "%" before the lines.
See the analytical outcomes in the command window.
Output:
Hamiltonian: ![](data:image/png;base64,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)
Adjoint System :
Value of Control :
And others like stability, Equillibriums etc. Will be updated soon.
Contribute.
Enjoy.
Cite As
Arindam Kumar Paul (2025). Analytical Analysis of an OCP with System of ODEs in MATLAB (https://www.mathworks.com/matlabcentral/fileexchange/132907-analytical-analysis-of-an-ocp-with-system-of-odes-in-matlab), MATLAB Central File Exchange. Retrieved .
As a codework for the project and published work entitled "Modeling and Optimal Control Applied to Reduce the Effects of Greenhouse Gases Emitted from the Coal-based Power Plant in Bangladesh".
MATLAB Release Compatibility
Created with
R2022a
Compatible with any release
Platform Compatibility
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Acknowledgements
Inspired by: OpenOCL - Open Optimal Control Library
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Version | Published | Release Notes | |
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1.1.0 | Codes are updated and some bugs fixed. |
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