Compare reference response, tracking error, and adaptive-gain history to see how stronger adaptation improves or destabilizes tracking.
Parameters
Plant time constant
s
Response speed of the controlled plant.
Model time constant
s
Desired reference-model speed.
Adaptation gain γ
-
Strength of parameter update.
Disturbance level
%
Load variation or unmodeled disturbance.
Results
—
Steady tracking error
—
Settling time
—
Oscillation tendency
—
Adaptation progress
Reference model response
Tracking error history
Adaptive gain history
Model and equations
$$\dot\theta=-\gamma e x,\quad y_m=\frac{1}{\tau_m s+1}r$$
MRAC adjusts controller parameters so the plant follows a reference model. Too much adaptation gain can amplify disturbance and modeling error.
How to read it
The response view compares plant output with the reference model.
The error plot shows whether larger adaptation lowers error or introduces oscillation.
The gain history checks whether parameter updates settle.
Learn Adaptive Control MRAC Simple by dialogue
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When reading Adaptive Control MRAC Simple, where should I look first? Moving Plant time constant changes both the plots and the result cards.
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Start with Steady tracking error, but do not treat the number as the whole answer. Use Reference model response to confirm the assumed state, then read Tracking error history for the distribution or trend. The response view compares plant output with the reference model.
🙋
I can see why Plant time constant changes Steady tracking error. How should I judge the influence of Model time constant?
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Move Model time constant in small steps and watch Settling time. That reveals which term is controlling the result. MRAC adjusts controller parameters so the plant follows a reference model. Too much adaptation gain can amplify disturbance and modeling error. A single operating point is not enough; sweep the realistic scatter range.
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What is Adaptive gain history for? It feels like the ordinary curve already tells the story.
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Adaptive gain history is for finding boundaries where the condition becomes risky or margin collapses quickly. The error plot shows whether larger adaptation lowers error or introduces oscillation. In Concept studies for adaptive control, the important question is often what happens after a small change, not only the nominal value.
🙋
So if Steady tracking error is within the target, can I accept the condition?
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Treat this as a first-pass review. It helps with Checking mismatch between model speed and plant speed and Initial adaptation-gain tuning under disturbance, but final decisions still need standards, measured data, detailed analysis, and vendor limits. The gain history checks whether parameter updates settle.
Practical use
Concept studies for adaptive control.
Checking mismatch between model speed and plant speed.
Initial adaptation-gain tuning under disturbance.
FAQ
Start with Steady tracking error and Settling time. Then use Reference model response to confirm the assumed state and Tracking error history to read distribution or bias. The response view compares plant output with the reference model
Move Plant time constant alone, then move Model time constant by a comparable amount and compare the change in Steady tracking error. Adaptive gain history shows combinations where margin or performance changes quickly.
Use it for Concept studies for adaptive control. Instead of trusting a single point, widen the input range and check whether Steady tracking error keeps enough margin before moving to detailed analysis.
MRAC adjusts controller parameters so the plant follows a reference model. Too much adaptation gain can amplify disturbance and modeling error. Final decisions still require standards, measured data, detailed analysis, and vendor limits.