I am often asked, since we’ve studied the Lambda tuning method and developed related tuning tools, is there a software product for PID parameter self-tuning? The answer is clear: there are no plans to develop a PID parameter self-tuning product at this time. So, can PID parameters be self-tuned? If the process model is known, self-tuning is certainly possible. However, it depends on specific conditions, and risks and costs must be considered. In practice, implementing PID parameter self-tuning is not merely a technical issue.
What exactly is PID parameter self-tuning? True PID parameter self-tuning software would require four functions: automated plant testing, automated system identification, automated parameter calculation, and automated parameter adjustment. Calculating PID parameters from a known process model is straightforward, and our PID Tuning Assistant mini-program already provides automated parameter calculation. However, the other three functions face limitations: plant testing significantly disrupts normal operations, system identification to obtain the process model is technically challenging, and automated parameter adjustment carries the highest safety risks. To minimize the impact of plant testing, some software proposes using historical data to capture process models. Unfortunately, this approach is theoretically unsound, limiting the practical scope of such software. In process control, even with these functionalities, engineers hesitate to use software for PID parameter self-tuning due to concerns about model accuracy, performance robustness, and parameter applicability, which ultimately require human judgment.
Where is PID parameter self-tuning widely applied? It is used in scenarios with clear causal relationships, highly variable process characteristics, and high on-site tuning costs. For example, valve positioners often use PID control. If operating conditions and valve characteristics are mismatched, self-tuning technology enables automated testing, identification, tuning, and parameter downloading, matching the valve to current conditions without engineer intervention, thus improving performance. PID parameter self-tuning is also common in temperature controllers and is a mature technology in such devices.
Can PID parameters be self-tuned in process control? The question is not about feasibility but necessity and practicality. Necessity is low: In process control, many PID loops underperform, but the reasons are complex. Approximately 50% of control loops can achieve satisfactory performance with approximate PID parameters, which engineers can easily tune manually or even use default configuration parameters. Another 20% cannot be resolved through PID tuning alone, requiring a redefinition of the control problem or a new control scheme. The remaining 30% could potentially benefit from self-tuning, but who can accurately identify which loops fall into this category? Demand is low: Engineers are reluctant to modify a functional control loop due to the high uncertainty involved. Changes may be ineffective or even detrimental. If a control scheme is operational, the status quo is often maintained—existence implies reasonableness, and unnecessary changes can cause problems. Even if issues are identified, the root cause may not lie in PID parameters. Benefits are limited: The widespread use of feedback control in the process industry reflects its inherent uncertainty, including model and disturbance uncertainties. In such uncertain conditions, tuning methods relying on precise models for optimal performance often lack robustness. The benefits of self-tuning do not justify the costs. The Lambda tuning method uses only three parameters to describe the process model and applies more robust principles to set PI parameters, reducing complexity and the impact of uncertainty while improving the cost-effectiveness of learning and applying tuning methods.
Testing risks: Conducting step or oscillation tests in a stably operating plant exceeds engineers’ tolerance. Even in advanced control projects, testing time is minimized. Process characteristics change with operating conditions, and without effective excitation, obtaining an accurate process model is challenging, requiring expert analysis of historical data to provide tuning recommendations. In the process industry, PID parameter self-tuning relies heavily on engineers’ judgment. If engineers know how to tune parameters, self-tuning technology becomes less critical. Control loop performance evaluation software or operational analysis can identify issues, but these are superficial. Solving problems requires identifying root causes. If the issue lies in PID parameters, engineers can address it. If not, self-tuning may do more harm than good.
A possible future direction for PID parameter self-tuning lies in the AI era, where tuning is integrated into a broader problem-solving framework. AI could analyze all process data, help engineers identify potential issues, and provide improvement suggestions, enhancing engineers’ problem-solving capabilities. The uniqueness of process control lies in its challenges: PID parameters may underperform, and vendor-provided control schemes may be suboptimal, necessitating the process control discipline. Due to the high uncertainty in process control, self-tuning technologies and precise model-based control methods struggle to gain traction. If engineers simply need tuning or control scheme design suggestions, I recommend exploring AI. However, for AI to be effective as an advisor, engineers must possess the ability to ask the right questions and make informed decisions. Ultimately, the challenge returns to the engineer’s expertise.