Closed-Loop Feedback Systems

How Does a Control Valve Positioner Really Work? A control valve positioner is essentially a closed-loop electropneumatic servo mechanism that ensures the valve stem reaches and maintains the exact position demanded by the controller. Here’s the technical flow: ➡️ Signal Conversion and Loop Power The DCS or PLC sends a 4–20 mA analog control signal, which also powers most 2-wire loop-powered positioners. This current represents the requested valve position (setpoint). ➡️I/P Conversion (Electro-Pneumatic Interface) Inside the positioner, the electrical signal drives an I/P converter, often using flapper-nozzle systems, piezoelectric valves, or force-balance torque motors. This converts 4–20 mA into a standardized 3–15 psi pneumatic output for actuator control. ➡️Pneumatic Relay and Pressure Amplification The low I/P output is boosted by a pneumatic relay/booster to actuator-level pressures (typically 20–80 psi). This ensures fast stroking and stable control under high thrust or shutoff force conditions. ➡️Actuator Motion The actuator converts pressure into mechanical motion through diaphragms, springs, cylinders, rack-and-pinion or Scotch yoke mechanisms. This motion drives the valve stem or shaft toward the demanded position. ➡️Stem Position Feedback (Closed Loop Control) A feedback element (mechanical linkage, magnetic sensor, potentiometer, Hall-effect sensor, or optical encoder) measures the actual stem position. The positioner continuously compares actual vs. commanded position and corrects air pressure until the error is zero. This creates a true PID-like servo loop operating directly on the valve. Example conversion 4 mA → 3 psi → valve 0% open 12 mA → 9 psi → valve 50% open 20 mA → 15 psi → valve 100% open Advanced technical insights • Auto-stroking and adaptive tuning to minimize hysteresis and deadband • Friction/stiction detection for early identification of packing or actuator issues • Valve signature and step-response curves for predictive maintenance • Partial Stroke Testing (PST) for ESD/SIS valves • Air consumption optimization and reduced bleed losses • Self-diagnostics for travel deviation, supply pressure issues, or I/P drift Modern digital positioners are no longer simple signal converters. They operate as intelligent field-level control devices that improve accuracy, reduce process variability, enhance reliability, and support SIL and IEC 61511 requirements when used with safety-instrumented valves.

🌉 “Phase Margin & Gain Margin — The Safety Nets of Feedback Systems” You wouldn’t drive a car without brakes, right? Then why close a feedback loop without phase and gain margins? Let’s take a ride through the world of stability margins — the invisible bodyguards of your op-amps, PLLs, LDOs, and all feedback circuits. ⸻ 🚗 The Highway Analogy: Think of your circuit’s feedback loop like a high-speed car racing down a winding highway (the transfer function path). • The speed is your gain • The steering angle is your phase • The road conditions? That’s your loop bandwidth and delays! Now imagine: If you turn the wheel too slowly (low phase margin) or drive too fast on sharp turns (high gain at 180° phase shift), you’ll skid right off into instability! ⸻ 🧠 What Is Phase Margin (PM)? At the frequency where the loop gain hits 0 dB (gain = 1), how far is the phase from –180°? • More margin = smoother control • Low PM (<45°) = ringing, overshoot • Zero PM = oscillation • Negative PM = instability 📌 Typical Target: 60° for general-purpose designs ⸻ ⚖️ What Is Gain Margin (GM)? At the frequency where the phase hits –180°, how far below 0 dB is the gain? • GM tells you how much more gain you can add before you hit instability • High GM = more forgiving system 📌 Typical Target: >10 dB ⸻ 💥 Why Are PM & GM Critical? • In PLL design: Poor PM causes peaking in jitter transfer! • In Op-Amps: Low PM = nasty overshoots in settling behavior • In LDOs: Sudden load changes can cause ringing without good PM • In Regulators: Poor margin = risk of oscillation under dynamic load ⸻ 🔍 Intuition Check — Dancing with Delay Imagine you’re dancing with a partner (feedback loop). You respond to their moves with a delay. If your delay is too much, you both trip — that’s instability. 🕺 More phase margin means you’re keeping time even if there’s delay. 🎵 More gain margin means you can still dance if the music (gain) gets louder. ⸻ 🧮 The Core Equation (Simplified) Let’s say your loop gain is: A(s) = A₀ / (1 + s/ω₀) The Bode plot shows phase lag increases as frequency rises. At 0 dB crossover frequency ω_c: • Phase Margin = 180° + ∠A(jω_c) • Gain Margin = 20log₁₀|A(jω₁₈₀)| ← when phase = –180° ⸻ 🧠 Golden Takeaways: 🎯 PM = How stable is the phase at unity gain? 🎯 GM = How stable is the gain at –180° phase? 📉 PM → affects overshoot, damping 📉 GM → affects gain peaking, oscillation margin ⸻ 🔧 Design Tip: Always analyze both open-loop Bode plots AND closed-loop step/jitter responses. Stability is not just “will it oscillate” — it’s “how gracefully does it behave?” ⸻ 🌟 Remember: A stable system isn’t just a working one — it’s one that performs predictably and gracefully under all conditions.

𝗜𝗳 𝗬𝗼𝘂’𝗿𝗲 𝗡𝗼𝘁 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽𝘀, 𝗬𝗼𝘂’𝗿𝗲 𝗡𝗼𝘁 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀, 𝗬𝗼𝘂’𝗿𝗲 𝗚𝘂𝗲𝘀𝘀𝗶𝗻𝗴. In service design and journey management, we talk a lot about touchpoints, channels, and experiences. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵: - No journey gets better without feedback. - No system evolves without learning loops. A feedback loop is the engine that turns friction into insight, and insight into action. In great systems, feedback loops are: 1. 𝗩𝗶𝘀𝗶𝗯𝗹𝗲 – Customers, brokers, employees can see the impact of their feedback 2. 𝗧𝗶𝗺𝗲𝗹𝘆 – Data isn’t stuck in a quarterly report, it’s now 3. 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 – It doesn’t just inform, it drives change 4. 𝗖𝗹𝗼𝘀𝗲𝗱 – People know they’ve been heard 𝗜𝗻 𝗯𝗿𝗼𝗸𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗱𝗶𝗲𝘀 𝗶𝗻:  🚫 Static maps and surveys nobody reads  🚫 Call logs without analysis  🚫 Dashboards with no ownership  🚫 “That’s just how the process works” 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁: - If a customer hits the same billing error twice, that’s not bad luck, it’s a broken loop. - If frontline staff keep hacks and workarounds to themselves, that’s a missed loop. - If leadership only hears what’s escalated, that’s a distorted loop. 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗶𝘀 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲𝗮𝘁𝗲𝗿. 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗮𝗿𝗲 𝗱𝗲𝘀𝘁𝗶𝗻𝗲𝗱 𝘁𝗼 𝗳𝗮𝗶𝗹. 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗱𝗼 𝘁𝗼𝗱𝗮𝘆? ✅ Embed feedback into your journeys—not after them ✅ Make insights operational, not optional ✅ Connect customer data to employee experience ✅ Design loops at every level—from micro-interactions to org-wide transformation 𝗬𝗼𝘂 𝗰𝗮𝗻’𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗹𝗶𝘀𝘁𝗲𝗻 𝘁𝗼. 𝗔𝗻𝗱 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝗹𝗲𝗮𝗱 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺. #ServiceDesign #OrganizationalDesign #BusinessDesign #SystemsDesign #Research

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

Hey everyone, building on our last post about how CM2 isn't just for engineers. Today, I want to share one of the biggest game-changers I've seen in making that appearingly magical cross-functional collaboration happen: it's all about having an 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗰𝗹𝗼𝘀𝗲𝗱-𝗹𝗼𝗼𝗽 𝗰𝗵𝗮𝗻𝗴𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. And no, that's not just a fancy term. This is the beating heart of the CM2 framework. Think of it this way: For proper Configuration Management knowing what you have is not enough, it also requires knowing how ‘what you have’ changes – from the very first idea, all the way through to its final retirement. I've seen many organizations wrestling with messy, fragmented change management. A new requirement pops up, engineering jumps on it, but does finance even know about the cost implications? Did procurement order the right new parts? Is customer service prepped for the update? So often, these connections are manual, stuck in their own little silos, and just screaming for mistakes to happen. Leading to endless delays, tons of rework, and sometimes even tricky compliance nightmares. So, what does a good closed-loop change process, backed by CM2, do for you? It makes sure of a few key things:  🐾 𝗧𝗼𝘁𝗮𝗹 𝗧𝗿𝗮𝗰𝗲𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Every single step – from the first idea to the final product – is linked. Imagine being able to trace anything back to its origin, understanding exactly why and how it came to be! 🔄 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲'𝘀 𝗜𝗻 𝘁𝗵𝗲 𝗟𝗼𝗼𝗽: When a change is suggested, everyone who's affected (and I mean everyone!) gets a heads-up. Finance, legal, sales, service – they're all in the loop. No more surprises or working in the dark. ✅ 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗱 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: No more "just doing it" or those sneaky "shadow changes." Changes are carefully planned, executed perfectly according to a clear plan, and double-checked against requirements. It's about control, not chaos. 🧑🏫 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: And this is where the "closed loop" part gets really powerful. It's not over when the change is done. We feed performance data, customer feedback, and lessons learned back into the system. This directly fuels future improvements and keeps us constantly evolving. Watching this unfold in companies, where teams go from constantly fighting fires to executing in perfect sync and doubling the amount of changes they process. It empowers your whole organization to innovate super fast while keeping control and clarity. Think of it as shifting from reaction mode to orchestration—CM2 gives you the tools to design change, not just survive it. So, for this round of "How do YOU CM2?", I'm curious: Have you tried implementing a closed-loop change process? What were your biggest wins, or even the biggest headaches, getting everyone on board and connected? Let's keep building a more robust future for Enterprise CM together! #CM2 #ConfigurationManagement #ChangeManagement #Agility #PLM #HowDoYOUCM2 #MDUX #IpX #ECM

4 loops beat 2, and here's why: Inner and outer loops were fine for 2005. They fix incidents, they close tickets, and they make dashboards look super busy. They also cap your upside and make you measure the wrong thing (e.g., problem solved vs. email delivered). I have seen “closed the loop” everywhere while revenue still leaked and costs kept rising. It's also a dated philosophy that too many push and isn't helping you create long-term customer value. First, some definitions: The inner loop is direct recovery with one customer after a bad moment. The outer loop is fixing the root cause. Useful, but mostly reactive. We cannot solve tomorrow’s problems with yesterday’s control loops. Now, let's modernize the stack a bit, shall we? 1. Recovery loop is 1-to-1 service recovery from any signal, not just surveys. 2. Removal loop is a two-week sprint eliminating the defect and verifying it's gone. 3. Orchestration loop is turning customer signals into the next-best-action for growth and efficiency across flows and channels. 4. Learning loop is the write-back of outcomes so models, rules, and playbooks get smarter, and corporate debt like tech debt gets cut. Closing the loop is a receipt. Compounding the loop is a result. This only works when leaders run it together: CX develops the priority and the value lens from the customer's perspective. Product and Engineering own removal with a real backlog and delivery dates. Sales and Marketing run orchestration so the right accounts get the right nudge or education at the right time. Service and Customer Success lead recovery with clear SLAs and authority to make it right. Data brings the signals together with field level controls. Finance verifies lift and keeps us honest. Legal and Risk set boundaries that protect customers and the brand. You hold a bi-weekly value standup to review prioritization for value at risk and value unlocked. Put it on one page with the owners named. Additionally, have a monthly review with Finance & Executives to greenlight bigger system changes only when the value story is clear. You want to focus on throughput here. Here's a concrete example. A commercial payments portal sees Friday 3 p.m. file upload failures spike. Recovery loop fixes impacted clients within an hour and credits fees where needed. The Removal loop delivers a batch size fix and a clearer progress widget within two sprints. The Orchestration loop sends a short in-app guide on Thursdays to high-risk users and alerts bankers for top accounts. The Learning loop shows failures down 62 percent, Friday contacts down 35 percent, and three at-risk clients adopting premium file services within a month. That is compounding value. Comment 1, 2, 3, or 4 with the loop your team is missing and the single constraint blocking it. Type "Fix the Loop" below, and I will share a Google Doc checklist you can steal for your team. #customerexperience #productmanagement #sales #engineering

That’s the thing about feedback—you can’t just ask for it once and call it a day. I learned this the hard way. Early on, I’d send out surveys after product launches, thinking I was doing enough. But here’s what happened: responses trickled in, and the insights felt either outdated or too general by the time we acted on them. It hit me: feedback isn’t a one-time event—it’s an ongoing process, and that’s where feedback loops come into play. A feedback loop is a system where you consistently collect, analyze, and act on customer insights. It’s not just about gathering input but creating an ongoing dialogue that shapes your product, service, or messaging architecture in real-time. When done right, feedback loops build emotional resonance with your audience. They show customers you’re not just listening—you’re evolving based on what they need. How can you build effective feedback loops? → Embed feedback opportunities into the customer journey: Don’t wait until the end of a cycle to ask for input. Include feedback points within key moments—like after onboarding, post-purchase, or following customer support interactions. These micro-moments keep the loop alive and relevant. → Leverage multiple channels for input: People share feedback differently. Use a mix of surveys, live chat, community polls, and social media listening to capture diverse perspectives. This enriches your feedback loop with varied insights. → Automate small, actionable nudges: Implement automated follow-ups asking users to rate their experience or suggest improvements. This not only gathers real-time data but also fosters a culture of continuous improvement. But here’s the challenge—feedback loops can easily become overwhelming. When you’re swimming in data, it’s tough to decide what to act on, and there’s always the risk of analysis paralysis. Here’s how you manage it: → Define the building blocks of useful feedback: Prioritize feedback that aligns with your brand’s goals or messaging architecture. Not every suggestion needs action—focus on trends that impact customer experience or growth. → Close the loop publicly: When customers see their input being acted upon, they feel heard. Announce product improvements or service changes driven by customer feedback. It builds trust and strengthens emotional resonance. → Involve your team in the loop: Feedback isn’t just for customer support or marketing—it’s a company-wide asset. Use feedback loops to align cross-functional teams, ensuring insights flow seamlessly between product, marketing, and operations. When feedback becomes a living system, it shifts from being a reactive task to a proactive strategy. It’s not just about gathering opinions—it’s about creating a continuous conversation that shapes your brand in real-time. And as we’ve learned, that’s where real value lies—building something dynamic, adaptive, and truly connected to your audience. #storytelling #marketing #customermarketing

To fully utilise the potential of AI within our organisations, we need to embrace nonlinearity. The first move in this direction is to get our organisation's data into a connected graph. We tend to think about cause and effect linearly. In other words, we tend to think one thing affects another, which in turn affects another. Like a line of dominoes, you knock over the first one and the cascade of cause and effects ripples down the line. A causes B, which causes C. Reality is more subtle and complex than this. Causality can contain circles where A causes B, which causes C, which loops back to affect A again. These circles in time are known as feedback loops. Two distinct categories of feedback loops exist: balancing and reinforcing. Balancing feedback loops maintain a system's equilibrium. Consider the thermostat regulating your home's temperature, adjusting the hot water based on the proximity of the current temperature to the desired target. Balancing feedback loops are involved in the regulation of blood sugar levels, supply-demand equilibrium, and the carbon cycle. These loops are pivotal in upholding stability within systems. Conversely, reinforcing feedback loops function as engines of change. Imagine a conversation between a couple where one partner raises their voice slightly, causing the other partner to raise their voice, which in turn causes the first partner to raise theirs even higher, and before you know it, they are throwing plates at each other! Examples of reinforcing feedback loops include bacterial proliferation, compounding interest, and human population. These loops play a pivotal role in driving growth and decay within systems. Feedback loops do not exist in isolation; rather, they are interconnected into entangled systems. The best data structure for depicting these systems is a graph, as graphs possess the capacity to model cyclic relationships among data elements. In other words, we can use graphs to model the nonlinear dynamics of Complex Systems. As the AI revolution gains momentum, we stand at the cusp of a potentially volatile phase, marked by the ascendancy of specific reinforcing feedback loops driving exponential transformation. Graphs offer us the means to comprehend these feedback loops and harness their profound power. Undoubtedly, all organisations are themselves complex systems, sustained and propelled by interwoven feedback loops. A better understanding of the nonlinear dynamics of these systems could very well end up being a major competitive advantage in the age of AI. Embrace Complexity: https://lnkd.in/ef9N26gy

🚀 Rethinking AI Risk Through the Lens of Control Theory: Introducing an Agentic AI Risk Assessment Framework As we enter the agentic AI era systems that actively pursue complex, multi-step goals in open environments, traditional risk frameworks feel like using a thermometer to navigate a spaceship. These are dynamic, non-linear, goal-directed systems. The only serious way to govern them is control-theoretic governance. Here’s the framework I’ve been refining, built explicitly on classical and modern control theory. 🌀 Controllability Can we reliably steer the agent from any state to a desired safe state in finite time, even under uncertainty or adversarial inputs? (Think: rank of the controllability Gramian in discrete-time systems, or the existence of a stabilizing feedback policy under partial observability.) 👁️ Observability & Interpretability Can we reconstruct the internal goal representation, planning horizon, and latent intentions from observable outputs alone? Weak observability → emergent deception or reward hacking becomes undetectable. 🎯 Stability (Robustness to Perturbations) Is the agent’s behavior BIBO stable (bounded-input → bounded-output) under distribution shift, goal misspecification, or malicious prompting? More critically: is it asymptotically stable around the intended objective, or does it exhibit chaotic or runaway amplification? 🔄 Feedback Bandwidth & Correction Latency How quickly can human-in-the-loop or automated guardrails detect and correct deviations? A system with high control delay is effectively uncontrollable in fast-moving environments (e.g., recursive self-improvement scenarios). 🛡️ Disturbance Rejection & Adversarial Robustness What is the H∞ norm of the closed-loop system? In plain English: how much worst-case disruption (prompt injection, data poisoning, objective tampering) can the system tolerate before catastrophic failure? Control theory gives us what today’s governance lacks: provable worst-case bounds, formal verification tools, and the actual engineering language used for rockets, grids, and reactors. Bank for International Settlements – BIS leaders (Trichet, Haldane, Carstens, Borio, others) have used exactly these concepts for 15+ years to explain why some financial systems survive crises and others explode. The Monetary Authority of Singapore (MAS) Nov 2025 consultation paper on Responsible Use of AI explicitly adopts the FEAT Principles I proposed in 2018 — and its sections on generative/autonomous systems are effectively demanding this control-theoretic approach. We already know how to build controllable, observable, stable systems at scale.  Will we finally treat agentic AI with nuclear-reactor seriousness instead of consumer-app casualness? Is control theory the bridge we need for scalable oversight?  Or do mesa-optimisation, ontology shifts, etc. break it? Thoughts welcome. #AgenticAI #AISafety #ControlTheory #AIGovernance #Alignment #FEATPrinciples #MAS #BIS

𝐂𝐨𝐧𝐬𝐭𝐚𝐧𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐩𝐨𝐰𝐞𝐫 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐥𝐨𝐨𝐤𝐬 𝐥𝐢𝐤𝐞 𝐚 𝐬𝐨𝐥𝐯𝐞𝐝 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Set the reactive power setpoint, let the inverter hold it. No voltage regulation, no voltage feedback — just maintain Q. Simple. Except it isn't. The inverter still has to measure reactive power, compare it to the setpoint, and adjust current accordingly. That's a closed loop. And closed loops go unstable when the model doesn't match what the hardware actually does. The instability typically shows up in 𝐰𝐞𝐚𝐤 𝐠𝐫𝐢𝐝 𝐜𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 — where the grid's 𝐯𝐨𝐥𝐭𝐚𝐠𝐞 𝐢𝐬 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐭𝐨 𝐫𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐩𝐨𝐰𝐞𝐫 𝐢𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧. The Q the inverter pushes out changes the terminal voltage, which affects the measured Q, which drives further correction. If the controller's integral gain was tuned against a stiff grid model, or if the actual 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭 𝐟𝐢𝐥𝐭𝐞𝐫𝐬 𝐚𝐧𝐝 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐝𝐞𝐥𝐚𝐲𝐬 are faster or slower than assumed, the loop margins disappear. What makes this problem frustrating is that it usually passes in simulation. The model has 𝐜𝐥𝐞𝐚𝐧 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬, 𝐧𝐨 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐝𝐞𝐥𝐚𝐲𝐬, and a grid impedance that may not reflect site conditions. Everything looks stable until you're on-site. From what I've seen, the mismatch tends to come down to three things: the actual measurement 𝐟𝐢𝐥𝐭𝐞𝐫 𝐭𝐢𝐦𝐞 𝐜𝐨𝐧𝐬𝐭𝐚𝐧𝐭𝐬 don't match the model, the 𝐏𝐏𝐂 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐝𝐞𝐥𝐚𝐲 is longer than assumed, or the 𝐠𝐫𝐢𝐝 𝐚𝐭 𝐭𝐡𝐞 𝐩𝐨𝐢𝐧𝐭 𝐨𝐟 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐬 𝐰𝐞𝐚𝐤𝐞𝐫 than the design case. None of these are exotic. All of them are easy to miss if you're treating Q-control as a solved problem. Has anyone run into Q-control instability in what should have been a straightforward commissioning? Curious how the root cause was eventually tracked down.