论文信息 - Adaptive general predictive controller for nonlinear systems

Adaptive general predictive controller for nonlinear systems

A nonlinear general predictive controller (NLGPC) is described which is based on the use of a Hammerstein model within a recursive control algorithm. A key contribution of the paper is the use of a novel, one-step simple root solving procedure for the Hammerstein model, this being a fundamental part of the overall tuning algorithm. A comparison is made between NLGPC and nonlinear deadbeat control (NLDBC) using the same one-step nonlinear components, in order to investigate NLGPC advantages and disadvantages.

K. Warwick | J. Douce | Quanmin Zhu

[1] David Clarke,et al. Generalised predictive control with input constraints , 1988 .

[2] Kevin Warwick. Adaptive deadbeat control of stochastic systems , 1986 .

[3] David Clarke,et al. Self-tuning control of offset: a unified approach , 1985 .

[4] Kevin Warwick,et al. Adaptive control algorithms for intelligent robot manipulators , 1988 .

[5] Kevin Warwick,et al. Weighted input predictive controller , 1988 .

[6] L. Patnaik,et al. Self-tuning minimum-variance control of nonlinear systems of the Hammerstein model , 1981 .

[7] Peter A. Cook,et al. Nonlinear dynamical systems , 1986 .

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

[9] Kevin Warwick. Implementation of self tuning controllers , 1988 .

[10] S. Billings,et al. Theory of separable processes with applications to the identification of nonlinear systems , 1978 .

[11] Dale E. Seborg,et al. Self-tuning controllers for nonlinear systems , 1987, Autom..

[12] A. Payne,et al. Adaptive one-step-ahead control subject to an input-amplitude constraint , 1986 .

[13] Rolf Isermann,et al. Experimental comparison and application of various parameter-adaptive control algorithms , 1980, Autom..