Abstract
This paper deals with the application of state space neural network model with delays to design a model predictive control for a laboratory stand of the Two Rotor Aero-dynamical system. The work describes approach based on the so-called instantaneous linearisation of the already trained nonlinear state space model of the system. With obtained linear model it is possible to derive a vector of future controls based on the minimisation of the cost function within one optimisation window. Repeating procedure in each step of simulation and applying the obtained control signal allows for efficiently control of the nonlinear systems. All data used in experiments is obtain from the real-time laboratory stand which is working in Matlab/Simulink RTW environment.
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References
Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Application. Prentice Hall, Englewood Cliffs (1993)
Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M.: Diagnosis and Fault-Tolerant Control. Springer, Berlin (2006)
Czajkowski, A., Patan, K., Korbicz, J.: Stability analysis of the neural network based fault tolerant control for the boiler unit. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 548–556. Springer, Heidelberg (2012)
Fletcher, R.: Practical methods of optimization, 2nd edn. Wiley-Interscience, New York (1987)
INTECO: Two Rotor Aero-dynamical System - Users Manual (2012), www.inteco.com.pl
Isermann, R.: Fault Diagnosis Applications: Model Based Condition Monitoring, Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems. Springer (2011)
Korbicz, J., Koscielny, J.M., Kowalczuk, Z., Cholewa, W. (eds.): Fault Diagnosis. Models, Artificial Intelligence, Applications. Springer, Berlin (2004)
Lee, J., Kim, J.S., Song, H., Shim, H.: A constrained consensus problem using mpc. International Journal of Control, Automation and Systems 9, 952–957 (2011)
Li, D., Xi, Y.: Quality guaranteed aggregation based model predictive control and stability analysis. Science in China Series F: Information Sciences 52, 1145–1156 (2009)
Ljung, L.: System Identification - Theory for the User. Prentice Hall, Englewood Cliffs (1999)
Luzar, M., Czajkowski, A., Witczak, M., Korbicz, J.: Actuators and sensors fault diagnosis with dynamic, state-space neural networks. In: MMAR 2012: 17th International Conference on Methods and Models in Automation and Robotics, pp. 196–201 [CD–ROM] (2012) ISBN: 978-83-7518-453-2
Maciejowski, J.: The implicit daisy-chaining property of constrained predictive control. Applied Mathematics and Computer Science 8(4), 101–117 (1998)
Milman, R., Davison, E.: A fast mpc algorithm using nonfeasible active set methods. Journal of Optimization Theory and Applications 139, 591–616 (2008)
Patan, K.: Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes. Springer, Berlin (2008)
Patan, K., Korbicz, J.: Nonlinear model predictive control of a boiler unit: a faul tolerant control study. International Journal of Applied Mathematics and Computer Science 22(1), 225–237 (2012)
Wang, L.: Model Predictive Control System Design and Implementation Using MATLAB®. Advances in Industrial Control. Springer (2010)
Won, W., Lee, K., Kim, I., Lee, B., Lee, S., Lee, S.: Model predictive control of condensate recycle process in a cogeneration power station: Controller design and numerical application. Korean Journal of Chemical Engineering 25, 972–979 (2008)
Yetendje, A., Seron, M.M., Don, J.A.D.: Robust multisensor fault tolerant model-following mpc design for constrained systems. Internationl Journal of Applied Mathematics and Computer Science 22(1), 211–223 (2012)
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Czajkowski, A., Patan, K. (2014). Model Predictive Control of the Two Rotor Aero-Dynamical System Using State Space Neural Networks with Delays. In: Korbicz, J., Kowal, M. (eds) Intelligent Systems in Technical and Medical Diagnostics. Advances in Intelligent Systems and Computing, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39881-0_9
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DOI: https://doi.org/10.1007/978-3-642-39881-0_9
Publisher Name: Springer, Berlin, Heidelberg
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