Control Performance Assessment in Industrial Automation
Summary
Control Performance Assessment (CPA) underpins the reliable and efficient operation of automated industrial systems by quantifying the quality of control loops and supporting decision-making for maintenance, tuning and optimisation. As industries embrace digitalisation, CPA encompasses both model-based and data-driven approaches that analyse control errors, signal characteristics and process variables to detect degradation or faults. Traditional techniques rely on statistical moments and residual analysis, while contemporary methods exploit entropy measures, fractal and multi-criterion frameworks. Model Predictive Control (MPC) systems demand specialised CPA methods to validate predictive accuracy over extended horizons. Equally, Proportional–Integral–Derivative (PID) loops benefit from graphical and statistical tools that visualise performance evolution. By integrating indicators of control quality, energy consumption and sustainability, modern CPA offers a holistic view of system health. Its global significance is demonstrated across sectors such as chemicals, power generation and manufacturing, where improved control performance translates into enhanced safety, reduced downtime and lower environmental impact.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Control Performance Assessment in Industrial Automation publication trend
The graph below shows the total number of articles in control performance assessment in industrial automation across all publications each year (not limited to Nature Index journals).
Technical terms
Control Performance Assessment (CPA): The process of measuring and analysing control loop quality to support optimisation and maintenance.
Model Predictive Control (MPC): A control strategy that uses a dynamic process model to predict future outputs and optimise control moves over a prediction horizon.
L-moment Ratio Diagram (LMRD): A graphical tool that visualises statistical characteristics of a time series using L-moment ratios to assess homogeneity and robustness.
Index Ratio Diagram (IRD): A multi-criteria plotting technique that displays the relationship between different performance indices, such as energy use and error metrics.
Residual: The difference between a process variable and its setpoint, commonly used as a primary indicator of control quality.
References
- Performance Assessment of Predictive Control—A Survey. Algorithms (2020).
- Assessment of predictive control performance using fractal measures. Nonlinear Dynamics (2017).
- Non-Gaussian Systems Control Performance Assessment Based on Rational Entropy. Entropy (2018).
- PID Control Assessment Using L-Moment Ratio Diagrams. Applied Sciences (2024).
- Energy-Aware Multicriteria Control Performance Assessment. Energies (2024).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.