Multiple models and neural networks based decoupling control of ball mill coal-pulverizing systems

Abstract Using a ball mill coal-pulverizing system as a motivating/application example, a class of complex industrial processes is investigated in this paper, which has strong couplings among loops, high nonlinearities and time-varying dynamics under different operation conditions. Focusing on such processes, an intelligent decoupling control method is developed, where the effects of nonlinearities are dealt with by neural network compensations and coupling effects are handled by specifically designed decoupling compensators, while the effect of time-varying dynamics is treated by a switching mechanism among multiple models. The stability and convergence of the closed-loop system are analyzed. The proposed method has been applied to the ball mill coal-pulverizing systems of 200 MW units in a heat power plant in China. Application results show that the system outputs are maintained in desired scopes, the electric energy consumption per unit coal has been reduced by 10.3%, and the production rate has been increased by 8%.

[1]  Bin Yao,et al.  Neural network adaptive robust control of nonlinear systems in semi-strict feedback form , 2001, Autom..

[2]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[3]  Duncan A. Mellichamp,et al.  A decoupling pole placement self‐tuning controller for a class of multivariable processes , 1986 .

[4]  B. Wittenmark,et al.  Adaptive decoupling of multivariable systems , 1987 .

[5]  I. J. Leontaritis,et al.  Input-output parametric models for non-linear systems Part II: stochastic non-linear systems , 1985 .

[6]  Tao Zhang,et al.  Stable Adaptive Neural Network Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[7]  Tian You Chai.  Direct adaptive decoupling control for general stochastic multivariable systems , 1990 .

[8]  Shuzhi Sam Ge,et al.  Adaptive MNN control for a class of non-affine NARMAX systems with disturbances , 2004, Syst. Control. Lett..

[9]  Graham C. Goodwin,et al.  A parameter estimation perspective of continuous time model reference adaptive control , 1987, Autom..

[10]  Kumpati S. Narendra,et al.  Nonlinear adaptive control using neural networks and multiple models , 2001, Autom..

[11]  Thomas J. McAvoy,et al.  Interaction analysis : principles and applications , 1983 .

[12]  H. Rosenbrock.  Design of multivariable control systems using the inverse Nyquist array , 1969 .

[13]  Rajat Majumder,et al.  Application of multiple-model adaptive control strategy for robust damping of interarea oscillations in power system , 2004, IEEE Transactions on Control Systems Technology.

[14]  Tong Heng Lee,et al.  A fuzzy controller with decoupling for multivariable nonlinear servo-mechanisms, with application to real-time control of a passive line-of-sight stabilization system , 1997 .

[15]  P. Ramadge,et al.  Discrete-time multivariable adaptive control , 1979 .

[16]  Panos J. Antsaklis,et al.  Set-valued observer design for a class of uncertain linear systems with persistent disturbance and measurement noise , 2003 .

[17]  Shi-Jun Lang,et al.  A multivariable generalized self-tuning controller with decoupling design , 1986 .

[18]  P. Atkinson,et al.  Process Control Systems , 1968 .

[19]  Petros A. Ioannou,et al.  Robust Adaptive Control , 2012 .

[20]  Chih-Min Lin,et al.  Decoupling control by hierarchical fuzzy sliding-mode controller , 2005, IEEE Trans. Control. Syst. Technol..

[21]  Ioan Doré Landau,et al.  Robust adaptive control of a flexible transmission system using multiple models , 2000, IEEE Trans. Control. Syst. Technol..

[22]  Tianyou Chai,et al.  A new decoupling design of self‐tuning multivariable generalized predictive control , 1999 .

[23]  Marios M. Polycarpou,et al.  High-order neural network structures for identification of dynamical systems , 1995, IEEE Trans. Neural Networks.