Abstract
In this article different schemes for Fault Tolerant Control (FTC) based on Adaptive Control, Artificial Intelligence (AI) and Robust Control are proposed. These schemes includes a Model Reference Adaptive Controller with a Neural Network and a PID controller optimized by a Genetic Algorithm (MRAC-PID-NN), a Model Reference Adaptive Controller with a Sliding Mode Control (MRAC-SMC) and a classical Model Reference Adaptive Controller (MRAC). In order to compare the performance of these schemes, an Industrial Heat Exchanger was used as test bed in which two different types of faults (abrupt and gradual) with different magnitudes (10% and 20%) were simulated. The simulation results showed that the use of AI methods improves the FTC schemes, developing a robust control system against sensor faults and a wider threshold to accommodate actuator faults in comparison with the two other schemes.
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References
Yu, D.L., Chang, T., Yu, D.W.: Fault Tolerant Control of Multivariable Processes Using Auto-Tuning PID Controller. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 32–43 (2005)
Stengel, R.: Intelligent failure-tolerant control. IEEE Control System Magazine 11, 14–23 (1991)
Nieto, J., Garza-Castañón, L., Rabhi, A., El Hajjaji, A., Morales-Menendez, R.: Vehicle Fault Detection and Diagnosis combining AANN and ANFIS. In: 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Barcelona, Spain, pp. 1079–1084 (2009)
Panagi, P., Polycarpou, M.: Decentralized Fault Accommodation of a Class of Interconnected Nonlinear Systems using an Adaptive Approximation Approach. In: 17th Mediterranean Conference on Control & Automation, Thessaloniki, Greece, pp. 546–551 (2009)
Polycarpou, M.: Fault accommodation of a class of multivariable nonlinear dynamical systems using a learning approach. IEEE Transactions on Automatic Control 46(5), 736–742 (2001)
Pashilkar, A., Sundararajan, N., Saratchandran, P.: A Fault-tolerant Neural Aided Controller for Aircraft Auto-landing. Aerospace Science and Technology 10, 49–61 (2006)
Perhinschi, M., Napolitano, M., Campa, G., Fravolini, M., Seanor, B.: Integration of Sensor and Actuator Failure Detection, Identification, and Accommodation Schemes within Fault Tolerant Control Laws. Control and Intelligent Systems 35(4), 309–318 (2007)
Patan, K., Korbicz, J.: Fault detection and accommodation by means of neural networks. Application to the boiler unit. In: 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Barcelona, Spain, pp. 119–124 (2009)
Lian, J., Zhao, J., Dimirovski, G.: Model Reference Adaptive Integral Sliding Mode Control for Switched Delay Systems. International Journal of Innovative Computing, Information and Control, 2025–2032 (2008)
Nagrath, J.: Control Systems Engineering. Anshan Ltd, Indian Institute of Technology, Delhi (2006)
Whitaker, H., Yamron, J., Kezer, A.: Design of Model Reference Adaptive Control Systems for Aircraft, Report R-164. Instrumentation Laboratory. MIT Press, Cambridge (1958)
Nguyen, H., Nadipuren, P., Walker, C., Walker, E.: A First Course in Fuzzy and Neural Control. CRC Press Company, United States (2002)
Mitchell, M.: An introduction to genetic algorithms. MIT Press, Massachusetts (1996)
Khalil, H.: Nonlinear Systems. Prentice Hall, United States (2002)
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Vargas-Martínez, A., Garza-Castañón, L.E. (2010). Combining Adaptive with Artificial Intelligence and Nonlinear Methods for Fault Tolerant Control. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13033-5_4
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DOI: https://doi.org/10.1007/978-3-642-13033-5_4
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