The paper addresses issues associated with implementing GPC controllers in systems with multiple input signals. Depending on the method of identification, the resulting models may be of a high order and when applied to a control/regulation law, may result in numerical errors due to the limitations of representing values in double-precision floating point numbers. This phenomenon is to be avoided, because even if the model is correct, the resulting numerical errors will lead to poor control performance. An effective way to identify, and at the same time eliminate, this unfavorable feature is to reduce the model order. A method of model order reduction is presented in this paper that effectively mitigates these issues. In this paper, the Generalized Predictive Control (GPC) algorithm is presented, followed by a discussion of the conditions that result in high order models. Examples are included where the discussed problem is demonstrated along with the subsequent results after the reduction. The obtained results and formulated conclusions are valuable for industry practitioners who implement a predictive control in industry.
This paper demonstrates the effectiveness of using anomaly detection in cyclic communication as a method aimed at protecting industrial installations from steganographic communication and a wide range of cyberattacks. The analysis was performed for a method based on deterministic finite automaton and the authors’ method using cycles. In this paper, we discuss the cycle detection algorithm and graph construction as well as demonstrate an anomaly detection method for cyberattack detection that utilizes stochastic elements, such as time-to-response and time-between-messages. We present a novel algorithm that combines finite automaton determinism modeling consecutive admissible messages with a time-domain model allowing for random deviations of regularity. The study was conducted for several test scenarios, including C&C steganographic channels generated using the Modbus TCP/IP protocol. Experimental results demonstrating the effectiveness of the algorithms are presented for both methods. All algorithms described in this paper are implemented and run as part of a passive warden system embedded in a bigger commercial IDS (intrusion detection system).
The digital twins technology delivers a new degree of freedom into system implementation and maintenance practice. Using this approach, a technological system can be efficiently modeled and simulated. Furthermore, such a twin offline system can be efficiently used to investigate real system issues and improvement opportunities, e.g., improvement of the existing control system or development of a new one. This work describes the development of a control system using the digital twins methodology for a gas system delivering a specific mixture of gases to the time-of-flight (ToF) multipurpose detector (MPD) used during high-energy physics experiments in the Joint Institute for Nuclear Research (Dubna, Russia). The gas system digital twin was built using a test stand and further extended into target full-scale installation planned to be built in the near future. Therefore, conducted simulations are used to validate the existing system and to allow validation of the planned new system. Moreover, the gas system digital twin enables testing of new control opportunities, improving the operation of the target gas system.
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