Model Predictive Control (MPC) in MATLAB: From Theory to Running Simulation in 30 Minutes

Model Predictive Control (MPC) in MATLAB: From Theory to Running Simulation in 30 Minutes is most useful as a research topic when the simulation is treated as an experiment rather than a demonstration. The central objective is MPC formulation with prediction model, constraints and objective-function tuning. A strong study fixes the plant and test conditions, defines a baseline, changes one research factor at a time and reports numerical evidence alongside plots.

For doctoral and postgraduate work, the model should make every assumption visible: rated values, data sources, solver settings, controller sampling, initial conditions, boundary conditions and disturbance definitions. This makes the results easier to defend in a thesis, reproduce later and convert into a publication-oriented comparison.

1. Define states, inputs, outputs and sampling time.
2. Build/linearise the prediction model or select a nonlinear MPC formulation.
3. Specify input, output and rate constraints.
4. Choose horizons and objective weights.
5. Test reference changes and disturbances against a baseline PID/controller.
6. Evaluate tracking and control effort under constraint activation.

Use numerical metrics that map directly to the research objective. Recommended outputs for this topic include:

• tracking RMSE
• settling time
• constraint violations
• control effort
• computation time
• robustness to model mismatch

To turn this topic into a stronger research contribution, start with one baseline and one proposed method, then extend the validation using adaptive MPC, economic MPC, robust/tube MPC. The final results section should explain why the proposed method changes the engineering behaviour, not only whether the output curve looks smoother. Include failure cases or operating limits when they reveal the boundary of the method.

• adaptive MPC
• economic MPC
• robust/tube MPC
• embedded real-time implementation