Industry engagement with control research: Perspective and messages

Maximizing the Impact of Control at All Levels

“…Each step forward in this direction implies reducing the theory-practice gap and, therefore, improving the technology transfer process. In particular, in publications, the key drivers described in Samad et al (2020) for the improvement of industrial products/processes should be taken into account as much as possible. In addition to the classical performance indices used in control systems (speed of response, robustness, etc.…”

“…One of the possible reasons why it is recognized that the impact of control engineering can be significantly increased is the well-known theory-practice gap, which has been discussed for many years and which seems to be more relevant in the control field than in other engineering fields. This issue has been very well analyzed in a recent article (Samad et al, 2020) outlined. From a slightly different perspective, this can be seen as a problem of technology transfer from academic researchers to industry.…”

“…Suggestions to the control research community to reduce the gap have been given in Samad et al (2020) and should be carefully considered. Between them, the role of education deserves to be underlined because it is really important to train people to face the problem.…”

“…We can think about, for example, health and medicine, energy, industrial manufacturing processes, transportation, and urban planning. In all these areas, the Institute of Electrical and Electronics Engineers Control Systems Society and the International Federation of Automatic Control have made extensive analysis of what the main research challenges are (Samad and Annaswamy, 2011;Lamnabhi-Lagarrigue et al, 2017), and for sure, it appears that control systems will be the cornerstone of the society of the future, given their capability to provide sustainable progress.…”

Predictive Cost Adaptive Control: A Numerical Investigation of Persistency, Consistency, and Exigency

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Among the multitude of modern control methods, model predictive control (MPC) is among the most successful [1]- [3]. As noted in "Summary," this success is largely due to the ability of MPC to respect constraints on controls and enforce constraints on outputs, both of which are difficult to handle with linear control methods, such as LQR and LQG, and nonlinear control methods, such as feedback linearization and sliding mode control.

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