Simulink® is an environment for multidomain simulation and Model-Based Design for dynamic and embedded systems. It provides an interactive graphical environment and a customizable set of block libraries that let you design, simulate, implement, and test a variety of time-varying systems, including communications, controls, signal processing, video processing, and image processing. Simulink is integrated with MATLAB®, providing immediate access to an extensive range of tools that let you develop algorithms, analyze and visualize simulations, create batch processing scripts, customize the modeling environment, and define signal, parameter, and test data.

References in zbMATH (referenced in 509 articles , 1 standard article )

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  1. Shafai, Bahram: System identification and adaptive control (to appear) (2024)
  2. Carpiuc, Sabin-Constantin; Lazar, Corneliu: Modeling of synchronous electric machines for real-time simulation and automotive applications (2017)
  3. Chen, Mingshuai; Ravn, Anders P.; Wang, Shuling; Yang, Mengfei; Zhan, Naijun: A two-way path between formal and informal design of embedded systems (2017)
  4. Costello, Sean; François, Grégory; Bonvin, Dominique: Crosswind kite control -- a benchmark problem for advanced control and dynamic optimization (2017)
  5. Jino Joy; Ushakumari, S.: Performance comparison of a canonical switching cell with SPWM and SVPWM fed sensorless PMBLDC motor drive under conventional and fuzzy logic controllers (2017)
  6. Kharola, Ashwani; Patil, Pravin: A PID based ANFIS control of inverted pendulum climbing on inclined plane (2017)
  7. Landau, Ioan Doré; Airimiţoaie, Tudor-Bogdan; Castellanos-Silva, Abraham; Constantinescu, Aurelian: Adaptive and robust active vibration control. Methodology and tests (2017)
  8. Schweizer, Wolfgang: Simulating physical systems. Computational physics with MATLAB (2017)
  9. Yazgan, Ramazan; Tunç, Cemil; Atan, Özkan: On the global asymptotic stability of solutions to neutral equations of first order (2017)
  10. Zhao, Yecheng; Zeng, Haibo: An efficient schedulability analysis for optimizing systems with adaptive mixed-criticality scheduling (2017)
  11. Alaimo, A.; Artale, V.; Barbaraci, G.; Milazzo, C.L.R.; Orlando, C.; Ricciardello, A.: LQR-PID control applied to hexacopter flight (2016)
  12. Bayrak, Alper; Tatlicioglu, Enver: A novel online adaptive time delay identification technique (2016)
  13. Cao, Yang; He, Dengbo; Yu, Fan; Luo, Zhe: Generalized predictive control based on vehicle path following strategy by using active steering system (2016)
  14. Chasparis, Georgios C.; Maggio, Martina; Bini, Enrico; Årzén, Karl-Erik: Design and implementation of distributed resource management for time-sensitive applications (2016)
  15. Dimirovski, Georgi M. (ed.): Complex systems. Relationships between control, communications and computing (2016)
  16. Guidorzi, Roberto: Positive output/state maps and quasi-positive realization of MIMO discrete systems (2016)
  17. Hammadih, Mohammad Luai; Hosani, Khalifa Al; Boiko, Igor: Interpolating sliding mode observer for a ball and beam system (2016)
  18. Ha, Phi; Mehrmann, Volker: Analysis and numerical solution of linear delay differential-algebraic equations (2016)
  19. Izadbakhsh, Alireza: Robust control design for rigid-link flexible-joint electrically driven robot subjected to constraint: theory and experimental verification (2016)
  20. Jańczak, Justyna; Kondratiuk, Mirosław; Pawłuszewicz, Ewa: Testing of adaptive nonuniform sampling swith algorithm with real-time simulation-in-the-loop (2016)

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