Intelligent Control and Digital Twins for Industry 4.0

Tepljakov, Aleksei

doi:10.3390/s23084036

Cited by 2 publications

(2 citation statements)

References 11 publications

(7 reference statements)

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“…Similarly, comparisons with recent modifications of the Smith predictor would also be interesting [84,85]. Various applications in time-delayed systems [84,[86][87][88][89][90][91][92] and Industry 4.0 (as in [93]) are also envisioned. In addition, extensions of the proposed method based on double integrator plus dead-time models compared to alternative approaches (e.g., Model Predictive Control (MPC) [94][95][96]) are also in preparation.…”

Section: Discussionmentioning

confidence: 99%

Parametrization and Optimal Tuning of Constrained Series PIDA Controller for IPDT Models

Huba,

Bistak,

Vrancic

2023

Mathematics

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The new modular approach to constrained control of higher-order processes with dominant first-order dynamics using generalized controllers with automatic resets (ARCs) is addressed. The controller design is based on the multiple real dominant pole (MRDP) method for the integrator plus dead time (IPDT) process models. The controller output constraints are taken into account by inserting the smallest numerator time constant of the controller transfer function into the positive feedback loop representing the automatic reset (integral) term. In the series realization of the proportional–integral–derivative–acceleration (PIDA) controller (and other controllers with even derivative degree), the time constant mentioned is complex, so only the real part of the time constant has been used so far. Other possible conversions of a complex number to a real number, such as the absolute value (modulus), can be covered by introducing a tuning parameter that modifies the calculated real time constant and generalizes the mentioned conversion when designing controllers with constraints. In this article, the impact of the tuning parameter on the overall dynamics of the control loop is studied by simulation. In addition, an evaluation of the stability of the closed-loop control system is performed using the circle criterion in the frequency domain. The analysis has shown that the approximation of the complex zero by its real part and modulus leads to a near optimal response to the set point tracking. The disturbance rejection can be significantly improved by increasing the tuning parameter by nearly 50%. In general, the tuning parameter can be used to find a compromise between servo and regulatory control. The robustness and applicability of the proposed controller is evaluated using a time-delayed process with first-order dominant dynamics when the actual transfer function is much more complicated than the IPDT model. A comparison of the proposed MRDP-PIDA controller with series PI, PID and PIDA controllers based on a modified SIMC method has shown that the MRDP-PIDA controller performs better than the SIMC method, although the SIMC uses a more complex process model.

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Section: Discussionmentioning

confidence: 99%

Parametrization and Optimal Tuning of Constrained Series PIDA Controller for IPDT Models

Huba,

Bistak,

Vrancic

2023

Mathematics

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“…Industry 4.0, i.e., the Fourth Industrial Revolution, involves the widespread implementation of artificial intelligence (AI), robotics, and the Internet of Things (IoT) to make processes more flexible, reduce the workforce, and make complex processes easier to control and manage. Industry 4.0 is applicable to all aspects of industry: analysis, diagnostics, manufacturing, technical control at all stages of production, packaging, and product/service evaluation throughout the life cycle [1][2][3][4]. Fast, accurate, and efficient analysis of production data is a key element of the Industry 4.0 paradigm.…”

Section: Introductionmentioning

confidence: 99%

Extended Fuzzy-Based Models of Production Data Analysis within AI-Based Industry 4.0 Paradigm

et al. 2023

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Fast, accurate, and efficient analysis of production data is a key element of the Industry 4.0 paradigm. This applies not only to newly built solutions but also to the digitalization, automation, and robotization of existing factories and production or repair lines. In particular, technologists’ extensive experience and know-how are necessary to design correct technological processes to minimize losses during production and product costs. That is why the proper selection of tools, machine tools, and production parameters during the manufacturing process is so important. Properly developed technology affects the entire production process. This paper presents an attempt to develop a post-hoc model of already existing manufacturing processes with the increased requirements and expectations resulting from the introduction of the Industry 4.0 paradigm. In particular, we relied on fuzzy logic to support the description of uncertainties, incomplete data, and discontinuities in the manufacturing process. This translates into better controls compared to conventional systems. An analysis of the proposed solution’s limitations and proposals for further development constitute the novelty and contribution of the article.

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Intelligent Control and Digital Twins for Industry 4.0

Abstract: One of the prominent features of the Fourth Industrial Revolution—frequently referred to as Industry 4 [...]

Cited by 2 publications

References 11 publications

Parametrization and Optimal Tuning of Constrained Series PIDA Controller for IPDT Models

Parametrization and Optimal Tuning of Constrained Series PIDA Controller for IPDT Models

Extended Fuzzy-Based Models of Production Data Analysis within AI-Based Industry 4.0 Paradigm

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