Presenters: Dr Paweł D. Domański, Warsaw University of Technology Institute of Control and Computation Engineering, Warsaw, Poland, Prof. YangQuan Chen, Dept. of Mechanical Engineering, School of Engineering, University of California, Merced
Nowadays the industry is witnessing winds of change. The era of Industry 4.0 transformation approaches. The issues of product throughput maximization, increased environmental protection and efficient energy management that have been pushing systems towards their technological constraints are not enough. A modern plant has to fulfill varying stringent regulations to operate at the edge of technological limitations. It should be accompanied by production horizontal and vertical integration, simulations, autonomous operation and flexibility. Most of these paradigms require the backbone of control engineering technologies and cannot exist without a properly designed and maintained control system. Due to such industrial demands, high control system performance must be closely coupled with the task of an advanced data analytics.
Process improvement is the main raison d'être for control systems. The relationship is straightforward. Better control causes higher performance. Despite this clear relation and common understanding of the fact, the majority of the industrial loops is neither well-tuned nor properly designed. Control engineers require tools and indexes that would measure how good the control system is. Moreover, they require suggestions for what should be done to improve existing poor situation. The research is ongoing. Its importance did not decrease. During fifty years of the interest several different approaches have been investigated, like data driven or model-based approaches defined using different domains. Simultaneously, as new control strategies have emerged, according assessment approaches have developed as well. Almost each control strategy, starting from SISO PID loops up to advanced control predictive and adaptive algorithms, has been addressed in the research and specific methodologies have been proposed.
Big data perspective poses a new challenge in the CPA data analytics. Advanced statistical and fractional-order data-driven methods allow to incorporate multi-criteria decision support into the assessment task. It has to be noted that CPA task has been initiated by industry, is being done for industry and is perpetually validated by industry.
This workshop will prepare our ACC2021 audience with
- Control Performance Assessment methods,
- Industrial perspective of the CPA,
- Control Performance Study as a CPA application case.
Expected learning outcomes:
- Basic knowledge about Control performance task and its positioning within industrial perspective
- Advanced statistical approaches, especially focused on the detection of outliers and anomalies.
- Fractional order approach revealing new perspectives in advanced big data analytics for process control performance assessment.
- Ability to use advanced methods in the industrial control system life-cycle Control Performance Study.
Intended audience: Graduate students, postdocs, engineers and faculty members dealing with complex process control and monitoring tasks.
Suggested prerequisite knowledge of the audience
- Basic knowledge of signals and systems, classical control system (Control-I).
- PID control tuning, MPC control, control system performance monitoring
- (optional) edge computing, deep learning
- Introduction to the Tutorial Workshop (Paweł D. Domański)
- Review of the CPA task and algorithms (Paweł D. Domański)
- Advanced statistical data-driven analytical tools – outliers and anomalies (Paweł D. Domański and YangQuan Chen)
- Coffee break and discussion.
- Advanced big-data fractional order signal processing approach (YangQuan Chen)
- Control Performance Study as a CPA application case in process industry (Paweł D. Domański)
Half-day workshops (1:30 pm – 5:30 pm EST)
For further, updated information on this workshop please visit Advanced big data analytics for control performance assessment in Industry 4.0 era.