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Journal articles on the topic 'Verification of control systems'

Author: Grafiati

Published: 6 September 2023

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1

Chen, Mo, and Claire J. Tomlin. "Hamilton–Jacobi Reachability: Some Recent Theoretical Advances and Applications in Unmanned Airspace Management." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 333–58. http://dx.doi.org/10.1146/annurev-control-060117-104941.

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Autonomous systems are becoming pervasive in everyday life, and many of these systems are complex and safety-critical. Formal verification is important for providing performance and safety guarantees for these systems. In particular, Hamilton–Jacobi (HJ) reachability is a formal verification tool for nonlinear and hybrid systems; however, it is computationally intractable for analyzing complex systems, and computational burden is in general a difficult challenge in formal verification. In this review, we begin by briefly presenting background on reachability analysis with an emphasis on the HJ formulation. We then present recent work showing how high-dimensional reachability verification can be made more tractable by focusing on two areas of development: system decomposition for general nonlinear systems, and traffic protocols for unmanned airspace management. By tackling the curse of dimensionality, tractable verification of practical systems is becoming a reality, paving the way for more pervasive and safer automation.

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2

De Smet, Olivier, Jean-Jacques Lesage, and Jean-Marc Roussel. "Formal Verification of Industrial Control Systems." IFAC Proceedings Volumes 34, no. 17 (September 2001): 183–88. http://dx.doi.org/10.1016/s1474-6670(17)33277-9.

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3

Zhang, Chi, Wenjie Ruan, and Peipei Xu. "Reachability Analysis of Neural Network Control Systems." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 15287–95. http://dx.doi.org/10.1609/aaai.v37i12.26783.

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Neural network controllers (NNCs) have shown great promise in autonomous and cyber-physical systems. Despite the various verification approaches for neural networks, the safety analysis of NNCs remains an open problem. Existing verification approaches for neural network control systems (NNCSs) either can only work on a limited type of activation functions, or result in non-trivial over-approximation errors with time evolving. This paper proposes a verification framework for NNCS based on Lipschitzian optimisation, called DeepNNC. We first prove the Lipschitz continuity of closed-loop NNCSs by unrolling and eliminating the loops. We then reveal the working principles of applying Lipschitzian optimisation on NNCS verification and illustrate it by verifying an adaptive cruise control model. Compared to state-of-the-art verification approaches, DeepNNC shows superior performance in terms of efficiency and accuracy over a wide range of NNCs. We also provide a case study to demonstrate the capability of DeepNNC to handle a real-world, practical, and complex system. Our tool DeepNNC is available at https://github.com/TrustAI/DeepNNC.

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4

Hoxha, Bardh. "Verification and Control for Autonomous Mobile Systems." Electronic Proceedings in Theoretical Computer Science 361 (July 10, 2022): 7–8. http://dx.doi.org/10.4204/eptcs.361.3.

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5

HASEGAWA, Masami. "S172026 SIL Verification of Safety Control Systems." Proceedings of Mechanical Engineering Congress, Japan 2013 (2013): _S172026–1—_S172026–4. http://dx.doi.org/10.1299/jsmemecj.2013._s172026-1.

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6

Feketa, Petro, Sergiy Bogomolov, and Thomas Meurer. "Safety Verification for Impulsive Systems." IFAC-PapersOnLine 53, no. 2 (2020): 1949–54. http://dx.doi.org/10.1016/j.ifacol.2020.12.2589.

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7

Rasina, Irina Viktorovna, and Oles Vla\-di\-mi\-ro\-vich Fesko. "Sufficient relative minimum conditions for discrete-continuous control systems." Program Systems: Theory and Applications 11, no. 2 (May 10, 2020): 61–73. http://dx.doi.org/10.25209/2079-3316-2020-11-2-61-73.

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In this paper, we derive sufficient relative minimum conditions for discrete-continuous control systems on the base of Krotov’s sufficient optimality conditions counterpart. These conditions can be used as verification conditions for suggested control mode and enable one to construct new numerical methods.

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8

Rawlings, Blake C., Jinkyung Kim, Il Moon, and B. Erik Ydstie. "Symbolic Verification of Control Systems and Operating Procedures." Industrial & Engineering Chemistry Research 53, no. 13 (February 28, 2014): 5299–310. http://dx.doi.org/10.1021/ie402998g.

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9

Mosterman, Pieter J., Gautam Biswas, and Janos Sztipanovits. "Hybrid Modeling and Verification of Embedded Control Systems." IFAC Proceedings Volumes 30, no. 4 (April 1997): 33–38. http://dx.doi.org/10.1016/s1474-6670(17)43608-1.

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10

Norman, Gethin, David Parker, and Xueyi Zou. "Verification and control of partially observable probabilistic systems." Real-Time Systems 53, no. 3 (March 8, 2017): 354–402. http://dx.doi.org/10.1007/s11241-017-9269-4.

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11

Karolak, Juliusz, Wiktor B. Daszczuk, Waldemar Grabski, and Andrzej Kochan. "Temporal Verification of Relay-Based Railway Traffic Control Systems Using the Integrated Model of Distributed Systems." Energies 15, no. 23 (November 29, 2022): 9041. http://dx.doi.org/10.3390/en15239041.

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Relay-based traffic control systems are still used in railway control systems. Their correctness is most often verified by manual analysis, which does not guarantee correctness in all conditions. Passenger safety, control reliability, and failure-free operation of all components require formal proof of the control system’s correctness. Formal evidence allows certification of control systems, ensuring that safety will be maintained in correct conditions and the in event of failure. The operational safety of systems in the event of component failure cannot be manually checked practically in the event of various types of damage to one component, pairs of components, etc. In the article, we describe the methodology of automated system verification using the IMDS (integrated model of distributed systems) temporal formalism and the Dedan tool. The novelty of the presented verification methodology lays in graphical design of the circuit elements, automated verification liberating the designer from using temporal logic, checking partial properties related to fragments of the circuit, and fair verification preventing the discovering of false deadlocks. The article presents the verification of an exemplary relay traffic control system in the correct case, in the case of damage to elements, and the case of an incorrect sequence of signals from the environment. The verification results are shown in the form of sequence diagrams leading to the correct/incorrect final state.

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12

BUI, Dinh Ba, Naoki UCHIYAMA, and Shigenori SANO. "D022 Friction compensation in contouring control for biaxial feed drive systems and experimental verification." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2013.7 (2013): 547–52. http://dx.doi.org/10.1299/jsmelem.2013.7.547.

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13

Kim, Dong Hwan, Moon-Chul Choi, and Joo-Hoon Baek. "Performance Verification of Semi-Active and Active Impact Control Systems." Journal of Vibration and Control 10, no. 6 (June 2004): 811–36. http://dx.doi.org/10.1177/1077546304036612.

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A semi-actively controlled impact systemis studied, which adjusts an impulse exerted by the external impact. Also, an active impact control system using a servo valve is introduced, and these performances are compared. The systems should respond to the extremely short impulse and control the impulse within a highly fast interval. Both the semi-active and the active control systems utilize an external orifice in the form of a proportional valve or a servo valve, which adjusts its opening area to control the pressure and piston displacement. These devices overcome the temperature and viscosity variations due to continuant operations, and keep the desired pressure difference and the displacement at a desirable level. In this work, two prototypic impact systems controlled by semi-active and full active operations are designed and manufactured. Through computer simulations and experiments, the possibility of controlling the pressure and displacement of an impact control system is verified.

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14

Mohajerani, Sahar, and Stephane Lafortune. "Transforming Opacity Verification to Nonblocking Verification in Modular Systems." IEEE Transactions on Automatic Control 65, no. 4 (April 2020): 1739–46. http://dx.doi.org/10.1109/tac.2019.2934708.

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15

Easton, Colin. "Safety Integrity Verification of Legacy Systems." Measurement and Control 42, no. 6 (July 2009): 185–89. http://dx.doi.org/10.1177/002029400904200605.

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16

Balun, Jiří, and Tomáš Masopust. "On Opacity Verification for Discrete-Event Systems." IFAC-PapersOnLine 53, no. 2 (2020): 2075–80. http://dx.doi.org/10.1016/j.ifacol.2020.12.2524.

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17

Szcześniak, Paweł, Iwona Grobelna, Mateja Novak, and Ulrik Nyman. "Overview of Control Algorithm Verification Methods in Power Electronics Systems." Energies 14, no. 14 (July 19, 2021): 4360. http://dx.doi.org/10.3390/en14144360.

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The paper presents the existing verification methods for control algorithms in power electronics systems, including the application of model checking techniques. In the industry, the most frequently used verification methods are simulations and experiments; however, they have to be performed manually and do not give a 100% confidence that the system will operate correctly in all situations. Here we show the recent advancements in verification and performance assessment of power electronics systems with the usage of formal methods. Symbolic model checking can be used to achieve a guarantee that the system satisfies user-defined requirements, while statistical model checking combines simulation and statistical methods to gain statistically valid results that predict the behavior with high confidence. Both methods can be applied automatically before physical realization of the power electronics systems, so that any errors, incorrect assumptions or unforeseen situations are detected as early as possible. An additional functionality of verification with the use of formal methods is to check the converter operation in terms of reliability in various system operating conditions. It is possible to verify the distribution and uniformity of occurrence in time of the number of transistor switching, transistor conduction times for various current levels, etc. The information obtained in this way can be used to optimize control algorithms in terms of reliability in power electronics. The article provides an overview of various verification methods with an emphasis on statistical model checking. The basic functionalities of the methods, their construction, and their properties are indicated.

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18

Grobelna, Iwona. "Formal Verification of Control Modules in Cyber-Physical Systems." Sensors 20, no. 18 (September 10, 2020): 5154. http://dx.doi.org/10.3390/s20185154.

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The paper proposes a novel formal verification method for a state-based control module of a cyber-physical system. The initial specification in the form of user-friendly UML state machine diagrams is written as an abstract rule-based logical model. The logical model is then used both for formal verification using the model checking technique and for prototype implementation in FPGA devices. The model is automatically transformed into a verifiable model in nuXmv format and into synthesizable code in VHDL language, which ensures that the resulting models are consistent with each other. It also allows the early detection of any errors related to the specification. A case study of a manufacturing automation system is presented to illustrate the approach.

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19

Hai, Lin. "Hybrid Dynamical Systems: An Introduction to Control and Verification." Foundations and Trends® in Systems and Control 1, no. 1 (2014): 1–172. http://dx.doi.org/10.1561/2600000001.

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20

Alwi, Saifulza, and Yasutaka Fujimoto. "Formal Verification of Logic Control Systems with Nondeterministic Behaviors." IEEJ Journal of Industry Applications 2, no. 6 (2013): 306–14. http://dx.doi.org/10.1541/ieejjia.2.306.

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21

Girgis, G. K., and H. D. Vu. "Verification of limiter performance in modern excitation control systems." IEEE Transactions on Energy Conversion 10, no. 3 (1995): 538–42. http://dx.doi.org/10.1109/60.464879.

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22

Branicky, Michael S., Michael M. Curtiss, Joshua Levine, and Stuart Morgan. "SAMPLING-BASED PLANNING, CONTROL, AND VERIFICATION OF HYBRID SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 271–76. http://dx.doi.org/10.3182/20050703-6-cz-1902.00330.

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23

Yau, S. S., and W. Hong. "Verification of concurrent control flow in distributed computer systems." IEEE Transactions on Software Engineering 14, no. 4 (April 1988): 405–17. http://dx.doi.org/10.1109/32.4662.

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24

Völker, Norbert, and Bernd J. Krämer. "Modular Verification of Function Block Based Industrial Control Systems." IFAC Proceedings Volumes 32, no. 1 (May 1999): 159–64. http://dx.doi.org/10.1016/s1474-6670(17)39981-0.

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25

Völker, Norbert, and Bernd J. Krämer. "Automated Verification of Function Block Based Industrial Control Systems." Electronic Notes in Theoretical Computer Science 25 (1999): 97–110. http://dx.doi.org/10.1016/s1571-0661(04)00135-5.

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26

Broquet, J., B. Claudinon, E. d'Andrimont, and A. Benoit. "GNC and Automatic Control Systems Development, Validation and Verification." IFAC Proceedings Volumes 25, no. 22 (September 1992): 11–21. http://dx.doi.org/10.1016/s1474-6670(17)49631-5.

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27

Völker, Norbert, and Bernd J. Krämer. "Automated verification of function block-based industrial control systems." Science of Computer Programming 42, no. 1 (January 2002): 101–13. http://dx.doi.org/10.1016/s0167-6423(01)00028-4.

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28

Yang, MengFei, Zheng Wang, GeGuang Pu, ShengChao Qin, Bin Gu, and JiFeng He. "The stochastic semantics and verification for periodic control systems." Science China Information Sciences 55, no. 12 (December 2012): 2675–93. http://dx.doi.org/10.1007/s11432-012-4750-0.

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29

Branicky, M. S., S. Morgan, J. Levine, and M. M. Curtiss. "Sampling-based planning, control and verification of hybrid systems." IEE Proceedings - Control Theory and Applications 153, no. 5 (September 1, 2006): 575–90. http://dx.doi.org/10.1049/ip-cta:20050152.

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30

Sanchez-Reillo, R., and C. Sanchez-Avila. "Fingerprint verification using smart cards for access control systems." IEEE Aerospace and Electronic Systems Magazine 17, no. 9 (September 2002): 12–15. http://dx.doi.org/10.1109/maes.2002.1039788.

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31

Lucia, W., D. Famularo, G. Franzè, and A. Furfaro. "Verification and Control of Hybrid Systems Under Safety Requirements." IFAC-PapersOnLine 51, no. 25 (2018): 61–66. http://dx.doi.org/10.1016/j.ifacol.2018.11.082.

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32

Moon, Il, Gary J. Powers, Jerry R. Burch, and Edmund M. Clarke. "Automatic verification of sequential control systems using temporal logic." AIChE Journal 38, no. 1 (January 1992): 67–75. http://dx.doi.org/10.1002/aic.690380107.

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33

Kartbayev, Amandyk. "An initial study of quality assurance techniques for automated water level control systems." E3S Web of Conferences 402 (2023): 03039. http://dx.doi.org/10.1051/e3sconf/202340203039.

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This study aims to formulate requirements for models and programs of cyber-physical systems through an investigation of existing approaches to dynamic verification of control programs. Building on this research, we propose a formal model for dynamic verification of process-oriented control programs in cyber-physical systems. Additionally, our goal is to develop a software package based on the proposed methods and models. The research focuses on hyperprocesses, which involve multiple interacting processes with executable states. The complexity of the models being created and the prevalence of routine operations present challenges in applying formal methods to their verification. Neglecting the verification of process-oriented software poses significant risks in system development. Therefore, addressing this challenge involves research and development of dynamic software verification methods that combine testing and simulation techniques.

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34

Sakai, Kazuya, Min-Te Sun, Wei-Shinn Ku, Hua Lu, and Ten H. Lai. "Data Verification in Integrated RFID Systems." IEEE Systems Journal 13, no. 2 (June 2019): 1969–80. http://dx.doi.org/10.1109/jsyst.2018.2865571.

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35

KOGISO, Kiminao, Hiroshi MINEMURA, and Kenji HIRATA. "Verification of Reference Governor Control Approaches to Experimental Systems with Control Constraints." Transactions of the Japan Society of Mechanical Engineers Series C 69, no. 681 (2003): 1238–46. http://dx.doi.org/10.1299/kikaic.69.1238.

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36

Zhao, Chunna, Murong Jiang, and Yaqun Huang. "Formal Verification of Fractional-Order PID Control Systems Using Higher-Order Logic." Fractal and Fractional 6, no. 9 (August 30, 2022): 485. http://dx.doi.org/10.3390/fractalfract6090485.

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Fractional-order PID control is a landmark in the development of fractional-order control theory. It can improve the control precision and accuracy of systems and achieve more robust control results. As a theorem-proving formal verification method, it can be applied to an arbitrary system represented by a mathematical model. It is the ideal verification method because it is not subject to limits on state numbers. This paper presents the higher-order logic (HOL) formal verification and modeling of fractional-order PID controller systems. Firstly, a fractional-order PID controller was designed. The accuracy of fractional-order PID control can be supported by simulation, comparing integral-order PID controls. Secondly, the superior property of fractional-order PID control is validated via higher-order logic theorem proofs. An important basic property, the relationship between fractional-order differential calculus and integral-order differential calculus, was analyzed via a higher-order logic theorem proof. Then, the relations between the fractional-order PID controller and integral-order PID controller were verified based on the fractional-order Grünwald–Letnikov definition for higher-order logic theorem proofs. Formalization models of the fractional-order PID controller and the fractional-order closed-loop control system were established. Finally, the stability of the fractional-order control systems was verified based on established formal models and theorems. The results show that the fractional-order PID controllers can be conducive to the control performance of control systems, and the higher-order logic formal verification method can ensure the reliability and security of fractional-order control systems.

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37

Luther, Mark E., Guy Meadows, Earle Buckley, Sherryl A. Gilbert, Heidi Purcell, and Mario N. Tamburri. "Verification of Wave Measurement Systems." Marine Technology Society Journal 47, no. 5 (September 1, 2013): 104–16. http://dx.doi.org/10.4031/mtsj.47.5.11.

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AbstractGiven the societal importance of reliable and accurate ocean observations, the wave monitoring community (including academic researchers, agency scientists, resource managers, and representatives from wave instrument manufacturers) came together to develop a set of protocols for the test and evaluation of wave measurement systems in support of the 2009 National Operational Wave Observation Plan. These protocols are focused on a wide range of wave measurement instruments and their respective performance in successfully recovering the “First-5” Fourier components of the incident wave field. Performance is determined by comparing each system’s output with a verifiable reference method over a predetermined range of wave frequencies. It is recommended that permanent wave test facilities are created on the West Coast (Monterey Bay, CA—deep water) and the East Coast (Duck, NC—shallow water) for continued evaluations of existing and new technologies. It was recognized that no absolute standard exists for the determination of the “First-5” across all spatial domains. Therefore, it was agreed that the Directional Waverider DWR-MkIII system was the best available reference/standard for the deep and intermediate water wave evaluations as verified by the laser array (LASAR) at the ConocoPhillips Ekofisk offshore platform complex in the North Sea. The long linear array at the U.S. Army Corps of Engineers’ Field Research Facility could be used as the standard for shallow water wave evaluations. Finally, given the significance of wave measurements, an appropriate level of quality assurance and quality control procedures must be included as part of any test and evaluation effort. The details of the proposed protocols for the verification of wave measurement systems are described.

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38

Araiza-Illan, Dejanira, Michael Fisher, Kevin Leahy, Joanna Isabelle Olszewska, and Signe Redfield. "Verification of Autonomous Systems [TC Spotlight]." IEEE Robotics & Automation Magazine 29, no. 1 (March 2022): 99–101. http://dx.doi.org/10.1109/mra.2022.3143966.

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39

Lechner, Mathias, Đorđe Žikelić, Krishnendu Chatterjee, and Thomas A. Henzinger. "Stability Verification in Stochastic Control Systems via Neural Network Supermartingales." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7326–36. http://dx.doi.org/10.1609/aaai.v36i7.20695.

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We consider the problem of formally verifying almost-sure (a.s.) asymptotic stability in discrete-time nonlinear stochastic control systems. While verifying stability in deterministic control systems is extensively studied in the literature, verifying stability in stochastic control systems is an open problem. The few existing works on this topic either consider only specialized forms of stochasticity or make restrictive assumptions on the system, rendering them inapplicable to learning algorithms with neural network policies. In this work, we present an approach for general nonlinear stochastic control problems with two novel aspects: (a) instead of classical stochastic extensions of Lyapunov functions, we use ranking supermartingales (RSMs) to certify a.s. asymptotic stability, and (b) we present a method for learning neural network RSMs. We prove that our approach guarantees a.s. asymptotic stability of the system and provides the first method to obtain bounds on the stabilization time, which stochastic Lyapunov functions do not. Finally, we validate our approach experimentally on a set of nonlinear stochastic reinforcement learning environments with neural network policies.

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40

Cheminod, Manuel, Luca Durante, Lucia Seno, and Adriano Valenzano. "Semiautomated Verification of Access Control Implementation in Industrial Networked Systems." IEEE Transactions on Industrial Informatics 11, no. 6 (December 2015): 1388–99. http://dx.doi.org/10.1109/tii.2015.2489181.

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41

Rohloff, Kurt, and Stéphane Lafortune. "The Verification and Control of Interacting Similar Discrete-Event Systems." SIAM Journal on Control and Optimization 45, no. 2 (January 2006): 634–67. http://dx.doi.org/10.1137/040610209.

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42

Méry, Dominique, and Jean-Francois Pétin. "Formal Engineering Methods for Modelling and Verification of Control Systems." IFAC Proceedings Volumes 31, no. 15 (June 1998): 141–46. http://dx.doi.org/10.1016/s1474-6670(17)40543-x.

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43

Mosterman, Pieter J., Gautam Biswas, and Janos Sztipanovits. "A hybrid modeling and verification paradigm for embedded control systems." Control Engineering Practice 6, no. 4 (April 1998): 511–21. http://dx.doi.org/10.1016/s0967-0661(98)00045-8.

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44

Tran, Hoang-Dung, Feiyang Cai, Manzanas Lopez Diego, Patrick Musau, Taylor T. Johnson, and Xenofon Koutsoukos. "Safety Verification of Cyber-Physical Systems with Reinforcement Learning Control." ACM Transactions on Embedded Computing Systems 18, no. 5s (October 19, 2019): 1–22. http://dx.doi.org/10.1145/3358230.

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45

Braman, Julia M. B., and Richard M. Murray. "Bisimulation conversion and verification procedure for goal-based control systems." Formal Methods in System Design 38, no. 1 (December 22, 2010): 62–95. http://dx.doi.org/10.1007/s10703-010-0109-6.

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46

Cui, Jingyu, and Yuze Xia. "Diagnosability verification of discrete event systems." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 324–30. http://dx.doi.org/10.54254/2755-2721/6/20230801.

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Fault diagnosis is one of the key topics in the study of computer program operation and automatic control of large complex systems these days. It can be used in wide-spanning areas. As early as the last century, researchers started to study diagnosis structures and made a series of progress. Moreover, the problem of diagnosability verification of a system received much attention from many researchers. Therefore, in this paper, a discrete event system (DES) is proposed and a diagnoser is constructed as an automaton model to verify the diagnosability of a given system. A method is proposed to test if a given system is diagnosable under the discrete event system structure. The states of a system are classified into three categories, and a diagnoser structure with basic algorithms and functions is defined to verify diagnosability. The proposed diagnoser structure can better capture the behavior of the system and verify its diagnosability.

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47

Tomasz, Barnert, Kosmowski Kazimierz, and Śliwiński Marcin. "Security Aspects in Verification of the Safety Integrity Level of Distributed Control and Protection Systems." Journal of Konbin 6, no. 3 (January 1, 2008): 25–40. http://dx.doi.org/10.2478/v10040-008-0056-0.

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Security Aspects in Verification of the Safety Integrity Level of Distributed Control and Protection SystemsThe article addresses some important issues of the functional safety analysis, namely the safety integrity level (SIL) verification of distributed control and protection systems with regard to security aspects. A quantitative method for SIL (IEC 61508) verification, based on so called differential factors, is presented. Taking into account SIL and the evaluation assurance level (EAL), which concerns the level of information security within entire system, two parametrical criterion function is defined for the SIL verification.

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48

Guéguen, Hervé, Marie-Anne Lefebvre, Janan Zaytoon, and Othman Nasri. "Safety verification and reachability analysis for hybrid systems." Annual Reviews in Control 33, no. 1 (April 2009): 25–36. http://dx.doi.org/10.1016/j.arcontrol.2009.03.002.

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49

ISHIHARA, Nijihiko, Yuichi CHIDA, and Masaya TANEMURA. "Experimental verification of multirate and Model Predictive Control for discrete-valued control systems." Mechanical Engineering Journal 8, no. 5 (2021): 21–00043. http://dx.doi.org/10.1299/mej.21-00043.

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50

Pomirski, Janusz, Andrzej Rak, and Witold Gierusz. "Control system for trials on material ship model." Polish Maritime Research 19, Special (October 1, 2012): 25–30. http://dx.doi.org/10.2478/v10012-012-0019-1.

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ABSTRACT The paper presents software environement for fast prototyping and verification of motion control systems for ship. The environement is prepared for isomorphic reduced ship model which is used for training and in research in a area of ship motion control. The control system is build using Matlab-Simulink-xPC package which simplifies and accellerates design and verification of new control algorithms. The systems was prepared also for Hardwarein- the-loop trials when a designed control system is tested inside a virtual environment instead of real actuators, disturbances, communication and measurement devices.

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