Why do AnalysisPoints change the results of systune optimization?

The difference in the results of the systune optimization when including or excluding AnalysisPoint blocks is primarily due to how systune treats these blocks in the optimization process. Although AnalysisPoint blocks do not directly affect the system dynamics, they provide additional flexibility for tuning and evaluation during the optimization.

Here are the key factors that explain the difference:

1. Additional Tuning Parameters

When AnalysisPoint blocks are included, systune treats them as additional points where performance metrics can be evaluated. These points act as "virtual sensors" or "virtual actuators" that decouple signals for analysis purposes. This allows systune to optimize the closed-loop system in a more granular way by isolating specific signals for evaluation.

• Without AnalysisPoint blocks, the system is optimized as a whole.
• With AnalysisPoint blocks, systune can fine-tune the interactions between these points and the rest of the system.

This additional flexibility can lead to slightly different optimal controller parameters.

2. Normalization of Signals

AnalysisPoint blocks normalize signals for better conditioning during optimization. This normalization can affect how systune scales the internal weights and priorities of tuning goals, potentially leading to different results.

• With AnalysisPoint blocks: The normalization ensures better numerical stability and alignment of the optimization process.
• Without AnalysisPoint blocks: The system dynamics are treated without intermediate normalization, which might influence the optimization trajectory.

3. Intermediate Closed-Loop Gains

When AnalysisPoint blocks are present, they allow systune to access the intermediate closed-loop transfer functions (e.g., TU→XT_{U \to X}TU→X?, TE→XT_{E \to X}TE→X?) explicitly. This could change the optimization outcome if these intermediate transfer functions influence the tuning goal evaluation.

4. Evaluation of Tuning Goals

Some tuning goals, like TuningGoal.StepTracking, depend on evaluating closed-loop responses. If AnalysisPoint blocks are included, they create additional signal paths and modify how the goal is evaluated. For instance:

• With AnalysisPoint blocks: The step tracking goal may be evaluated using decoupled paths.
• Without AnalysisPoint blocks: The goal is evaluated directly on the main signal path.

These differences can alter the optimized controller parameters slightly.

Verifying the Effect

To verify the exact difference caused by AnalysisPoint blocks:

1. Compare the tuned controller parameters (getBlockValue(C)).
2. Analyze the intermediate transfer functions using getIOTransfer.
3. Check the tuning goal satisfaction (viewGoal(CL1, Rtrack) and viewGoal(CL2, Rtrack)).

Recommendations

1. Consistency: If the final application involves analyzing specific signals, include AnalysisPoint blocks consistently to align the optimization process with your analysis needs.
2. Validation: Always validate the final closed-loop performance for both configurations using bode, step, or custom simulations.

In your specific example, the inclusion of AnalysisPoint blocks adds flexibility to the optimization process, which is why the results differ slightly between the two configurations.