Real-time digital control of time-delay systems: From smith predictor to MPC

Holis, Radek; Bobál, Vladimír; Vojtěšek, Jiří

doi:10.1109/ice.2017.8279897

Cited by 3 publications

(1 citation statement)

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“…Notably, the proposed coupling algorithm is generally applicable without requiring knowledge of the RVP's system behavior. This constitutes a significant difference from previous works, where the coupling algorithm relies on precise mathematical models of all coupled prototypes, such as the utilization of a Smith predictor [23], a Luenberger observer [24], or model predictive control methods [25] for latency compensation.…”

Section: Introductionmentioning

confidence: 91%

Enhancing the Coupling of Real-Virtual Prototypes: A Method for Latency Compensation

Baumann,

Kotte,

Mikelsons

et al. 2024

Electronics

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Currently, innovations in mechatronic products often occur at the system level, requiring consideration of component interactions throughout the entire development process. In the earlier phases of development, this is accomplished by coupling virtual prototypes such as simulation models. As the development progresses and real prototypes of certain system components become available, real-virtual prototypes (RVPs) are established with the help of network communication. However, network effects—all of which can be interpreted as latencies in simplified terms—distort the system behavior of RVPs. To reduce these distortions, we propose a coupling method for RVPs that compensates for latencies. We present an easily applicable approach by introducing a generic coupling algorithm based on error space extrapolation. Furthermore, we enable online learning by transforming coupling algorithms into feedforward neural networks. Additionally, we conduct a frequency domain analysis to assess the impact of coupling faults and algorithms on the system behavior of RVPs and derive a method for optimally designing coupling algorithms. To demonstrate the effectiveness of the coupling method, we apply it to a hybrid vehicle that is productively used as an RVP in the industry. We show that the optimally designed and trained coupling algorithm significantly improves the credibility of the RVP.

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Section: Introductionmentioning

confidence: 91%