Compare closed-loop vs open-loop output distribution under random load variation using Monte Carlo simulation to verify feedback's robustness improvement.
The previous two lessons compared load change and VIN step as individual scenarios. This lesson uses Monte Carlo (100 samples) with the load resistor set to agauss(100,20,3) normal distribution, running both closed-loop and open-loop circuits to directly compare their statistical output distribution.
Circuit probes: mc_avg_out measures avg(out), mc_rload_r measures avg(param(rload,r)). After 100 runs, closed-loop avg(out) clusters in 6.18–6.24V while open-loop (Ch3 Lesson 3's mc_open_loop.sch) under the same load distribution gives 7.45–8.15V. Feedback compresses distribution range by 98%.
This dramatic output distribution compression shows feedback not only stabilizes output under static conditions but also statistically enhances circuit robustness. Residual error comes from the error amplifier's finite gain (×10). Higher gain can further reduce error but affects loop stability — the next chapter explores balancing regulation accuracy with dynamic stability.
Closed-loop, R_load~N(100,σ≈20)Ω, 100 samples. avg(out) clusters in 6.18–6.24V (open-loop 7.45–8.15V). Feedback compresses distribution range by 98%.
After reading this section, run the simulation and observe the waveforms. To explore further, open the example in a standalone page.