Hi Tim Teng,
I understand that you are trying to integrate a k-step predictor within a real-time Simulink model, where the predictor model is nonlinear and contains integrators. You're facing challenges with executing this predictor model in real-time due to the computational overhead and the limitations of using integrators within each simulation time step.
You can follow the below approches to overcome the challenges:
- Model Referencing: Use Model Reference blocks for integrating the predictor model into the real-time simulation. This method compartmentalizes the predictor, potentially reducing overhead. Ensure effective communication between the real-time model and the predictor.
- Function-Call Subsystems: Encapsulate the predictor model within a Function-Call Subsystem, triggered at each real-time step. Execution time management is crucial to fit within real-time constraints.
- Optimization of the Predictor Model: Optimize the predictor by simplifying the model, reducing blocks, and adjusting solver settings. Using fixed-step solvers and tuning the step size can improve both performance and accuracy.
- Asynchronous Execution: If feasible, run the predictor model asynchronously to the real-time simulation. This involves parallel execution and synchronization mechanisms for integrating outputs without violating real-time constraints.
References:
- For details on using Model Reference blocks, refer to the documentation at https://www.mathworks.com/help/simulink/slref/model.html.
- Information on Function-Call Subsystems can be found at https://www.mathworks.com/help/simulink/ug/using-function-call-subsystems.html
- To explore optimization techniques for Simulink models, the following page might be useful: https://www.mathworks.com/help/sldo/
- For understanding asynchronous executions, see https://www.mathworks.com/help/matlab/matlab_prog/asynchronous-functions.html
Hope this helps!
