In order to apply dynamic optimization methods we must have a dynamic model to optimize. Obtaining a good dynamic model of the design problem is the most important step. A static model is often developed first and can often be augmented to include dynamic elements that relate how the system evolves with time. In this section we discuss some modeling concepts for dynamic systems that can help you develop models for optimization. We also discuss the formulation objectives, constraints, and dynamic data sets.
Fitting Physical Models to Experimental Data
Dynamic models are often constructed with physical models and tuned with experimental data. Physical models are based on the underlying physical principles that govern the problem and result from expressions such as a force or momentum balance and may include quantities such as velocity, acceleration, and position. Other quantities of interest may include anything that changes with respect to time such as reactor composition, temperature, mole fraction, etc. Models likely contain both physical and experimental elements. We will discuss how to reconcile experimental data with the physical model through parameter estimation. A final activity will be to use the physical model to then optimize a particular objective.
Introduction to Dynamic Modeling with MATLAB and Python
Simulate Dynamic Data with Python and MATLAB
Simulink Estimation and Control with APM
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