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Self-tuning run-time reconfigurable PID controller

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Digital PID control algorithm is one of the most commonly used algorithms in the control systems area. This algorithm is very well known, it is simple, easily implementable in the computer control systems and most of all its operation is very predictable. Thus PID control has got well known impact on the control system behavior. However, in its simple form the controller have no reconfiguration support. In a case of the controlled system substantial changes (or the whole control environment, in the wider aspect, for example if the disturbances characteristics would change) it is not possible to make the PID controller robust enough. In this paper a new structure of digital PID controller is proposed, where the policy-based computing is used to equip the controller with the ability to adjust it's behavior according to the environmental changes. Application to the electro-oil evaporator which is a part of distillation installation is used to show the new controller structure in operation.
Rocznik
Strony
189--205
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
  • Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, ul. Mikolajczyka 5, 45-272 Opole, Poland
Bibliografia
  • [1] R. ANTHONY: (n.d.) http://www.policyautonomics.net.
  • [2] R. J. ANTHONY: A policy-definition language and prototype implementation li-brary for policy-based autonomie systems. Proc. of 3rd Int. Conf. on Autonomie Computing (ICAC2006), (2006), 265-276.
  • [3] R. J. ANTHONY, M. PELC and W. Byrski: Context-aware reconfiguration of autonomie managers in real-time control applicaitons. Proc. of 7th IEEE Conf. on Autonomie Computing, (2010), 73-74.
  • [4] R. J. Anthony, M. Pelc, P. Ward and J. Hawthorne: Flexible and robust run-time configuratioń for self-managing systems. SASO '08: Proc. of the 2008 Second IEEE Int. Conf. on Self-Adaptive and Self-Organizing Systems, (2008), 491-492.
  • [5] W. Byrski: Observers and their applications in adaptive control systems. Scien-tific Bulletins ofThe University of Mining and Metallurgy, 1551(65), (1993).
  • [6] W. Byrski and S. Fuksa: Optimal finite parameter observer. An application to synthesis of stabilizing feedback for a linear system. Control and Cybernetics 13(1), (1984).
  • [7] W. Byrski and M. Pelc: Modelling and simulation of state observers in the computer control systems. Proc. of 39th Int. Conf. on Modelling and Simulation of Systems, (2005).
  • [8] W. Byrski and M. Pelc: Continuous-and discrete integral state observers in on-linę control systems. Proc. Of 39th Int. Conf. on Modelling and Simulation of Systems, (2006).
  • [9] W. Chia-Ju: Genetic timing of PID controllers using a neural network model: A seesaw example. J. Intell. Robotics Syst, 25 (1999), 43-59.
  • [10] J. I. CORCAU and E. Stoenescu: An adaptive pid fuzzy controller for synchronous generator. World Scientific and Engineering Academy and Society (WSEAS), (2008).
  • [11] T. FUJINAKA, Y. KISHIDA, M. Yoshioka and S. Omatu: Stabilization of double inverted pendulum with self-tuning neuro-PID. IEEE Computer Society, (2000).
  • [12] G. Jie and T. Shengjing: Application of parameter self-tuning fuzzy PID controller in guidance loop of unmanned aircraft. IEEE Computer Society, (2009).
  • [13] O. Karakasal, E. Yesil, M. Guzelkaya and I. Eksin: Implementation of a new self-tuning fuzzy pid controller on PLC. Turkish J. of Electrical Engineering, 13(2), (2005), 277-286.
  • [14] C. Kuan-Yu, T. PI-CHENG, T. Mong-Tao and F. Yi-Hua: A self-tuning fuzzy PID-type controller design for unbalance compensation in an active mag-netic bearing. Expert Syst. Appl, 36 (2009), 8560-8570.
  • [15] J. A. M. Lima and A,E. Ruano: Neuro-genetic pid autotuning: time invariant case. Math. Comput. Simui, 51 (2000), 287-300.
  • [16] F. Lin, R.D. Brandt; and G. Saikalis: Self-tuning of PID controllers by adap-tive interaction. Proc. of American Control Conf., 2000, 3676-1681.
  • [17] M. Pelc, R. Anthony and W. BYRSKI: Policy supervised exact state reconstruc-tion in real-time embedded control systems. Proc. of 7th Workshop on Advanced Control and Diagnostics ACD2009, (2009).
  • [18] M. Pelc, R. Anthony and W. Byrski: Context-aware real-time systems with autonomie controllers, Proc. of 5th Int. Conf. on Pervasive Computing and Applications, (2010).
  • [19] H. Shiuh-Jer and L. YI-Ho: Metal chamber temperaturę control by using fuzzy pid gain auto-tuning strategy, WSEAS Trans. Sys. Ctrl., 4 (2009), 1-10.
  • [20] P. Ward, M. Pelc, J. Hawthorne and R. J. Anthony: Embedding dynamie behavior into a self-configuring software system. Proc. of 5th Int. Conf. on Autonomie and Trusted Computing (Springer LNCS), (2008). 373-387.
  • [21] J. YAU-TARNG, C. YUN-TIEN and H. Chih-Peng: Design of fuzzy PID con-trollers using modified triangular memjjership functions. Inf. Sci, 178 (2008), 1325-1333.
  • [22] A. Yazdizadeh, A. Mehrafrooz, J. Jouzdani and R. Barzamini: Adap-tive neuro-PID controller design with application to nonlinear water level in NEKA power plant. J. of Applied Sciences, (2009).
  • [23] M. ZAHEER-UDDIN and N. Tudoroiu: Neuro-PID tracking control of a discharge air temperaturę system. Energy Comersion and Management, (2004).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0081-0011
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