Abstract
Heating, Ventilating and Air Conditioning (HVAC) plant, is a multivariable, nonlinear and non minimum phase system, that its control is very difficult. In this paper, we apply a robust sliding mode fuzzy controller to HVAC system. Our proposed method can achieve very robust and satisfactory performance and could be used to get the desired performance levels. The response time is also very fast despite the fact that the control strategy is based on bounded rationality. To evaluate the usefulness of the proposed method, we compare the response of this method with PID controller. The simulation results show that proposed method has the better control performance than PID controller.
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References
Serrano, B.A., Velez-Reyes, M.: Nonlinear Control of A Heating Ventilation and Air Conditioning Systems with Thermal Load Estimation. IEEE Transaction on Control Systems Technology 7 (1999)
Geng, G., Geary, G.M.: On Performance and Tuning of PID Controllers in HVAC Systems. In: Conference on Control Application (1993)
Newman, H.M.: Direct Digital Controls for Building Systems. John Wiley pub., Chichester (1994)
Zaheeruddin, M.: Optimal Control of A Single Zone Environment Space. Building and Environment (1992)
Tigrek, T., Dasgupta, S., Smith, T.F.: Nonlinear Optimal Control of HVAC Systems. In: Proceeding of IFAC Conference (2002)
Underwood, C.P.: Robust Control of HVAC Plant II: Controller Design. In: Proceeding of CIBSE, Vol. 21(1) (2000)
Osman, A., Mitchell, J.W., Klein, S.A.: Application of General Regression Neural Network in HVAC Process Identification and Control. ASHRAE Transaction 102 (1996)
Curtiss, P.S., Kreider, J.F., Branenuehl, M.J.: Local and Global Control of Commercial Building HVAC Systems Using Artificial Neural Networks. In: American Control Conference, ACC (1994)
Jian, W., Wenjian, C.: Development of an Adaptive Neuro-Fuzzy Method for Supply Air Pressure Control in HVAC System. IEEE Trans. (2000)
Rashidi, F., Lucas, C.: Applying CBRL (Context Based Reinforcement Learning) to Multivariable Control of a HVAC System. WSEAS Trans. on Circuit and Systems (2003)
Semsar, E., Yazdanpanahm, M.J., Lucas, C.: Nonlinear Control and Disturbance Decoupling of an HVAC System via Feedback Linearization and Back-Stepping. In: IEEE Conference on Control Application (2003)
Slotine, J.J., Sastry, S.S.: Tracking control of nonlinear systems using sliding surfaces with application to robot manipulators. International Journal of Control 38(2), 465–492 (1983)
Glower, J.S., Munighan, J.: Designing Fuzzy Controllers from a Variable Structures Standpoint. IEEE Transactions on Fuzzy Systems 5(1), 138–144 (1997)
Hung, J.Y., Gao, W., Hung, J.C.: Variable Structure Control: A Survey. IEEE Transactions on Industrial Electronics 40(1), 2–21 (1993)
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Rashidi, F., Moshiri, B. (2004). Design of a Robust Sliding Mode Fuzzy Controller for Nonlinear HVAC Systems. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_153
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DOI: https://doi.org/10.1007/978-3-540-24844-6_153
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
Online ISBN: 978-3-540-24844-6
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