NGMV control of state dependent multivariable systems

A nonlinear generalized minimum variance control law is derived for the control of nonlinear multivariable systems. The solution for the control law is obtained using a model for the process that includes three different types of subsystem to provide a variety of means of modeling the system. These may not all be present. The first subsystem involves a nonlinear operator of very general form. The second is a state dependent, state equation model of the plant and finally a linear state-equation model represents the output subsystem, disturbance and reference models. The process is assumed to include common delays in input or output channels of magnitude k. The quadratic like cost index involves state, error and control signal costing terms. The controller obtained is simple to implement, particularly in one form, which might be considered a nonlinear state-dependent version of the Smith predictor.

[1]  Evelio Hernández,et al.  Stability of nonlinear polynomial ARMA models and their inverse , 2000, IBM J. Res. Dev..

[2]  M. Grimble A control weighted minimum-variance controller for non-minimum phase systems , 1981 .

[3]  K. Åström Introduction to Stochastic Control Theory , 1970 .

[4]  D. Mayne,et al.  Receding horizon control of nonlinear systems , 1990 .

[5]  Michael J. Grimble,et al.  Generalized minimum variance control law revisited , 2007 .

[6]  A. Isidori Nonlinear Control Systems , 1985 .

[7]  M. J. Grimble,et al.  A note on a compatriot of the real Marcinkiewicz space , 1981 .

[8]  D. W. Clarke,et al.  Design of digital controllers for randomly disturbed systems , 1971 .

[9]  David Clarke,et al.  Self-tuning control , 1979 .

[10]  Kelly D. Hammett Control of nonlinear systems via state feedback state-dependent Riccati equation techniques , 1997 .

[11]  G. Siouris,et al.  Nonlinear Control Engineering , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  P. E. Wellstead,et al.  1.18 – Self-Tuning Controllers , 1995 .

[13]  M. Grimble Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems , 1994 .

[14]  Amir Hussain,et al.  A new neural network and pole placement based adaptive composite controller , 2001, Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century..

[15]  M. J. Grimble H∞ multivariable-control-law synthesis , 1993 .

[16]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.