Title :
Sampled-data GPC (SDGPC) with integral action: the state space approach
Author :
Lu, Guoqiang ; Dumont, Guy A.
Author_Institution :
Dept. of Electr. Eng., British Columbia Univ., Vancouver, BC, Canada
Abstract :
In this paper, a sampled-data generalized predictive control (SDGPC) algorithm is developed. SDGPC is based on a continuous-time state space model with continuous-time quadratic cost function, but the projected future control scenario is assumed to be piecewise constant. In doing so, SDGPC can be implemented digitally without any approximation. By state augmentation, SDGPC produces integral action to track a constant setpoint with zero steady error subject to an unknown constant disturbance. Laguerre filter modeling concepts which have been popular recently in process industry can be integrated into this controller design readily and the resulting sampled-data Laguerre-based GPC (SDLGPC) is suitable for adaptive applications. The closed-loop stability of SDGPC is established and the relation between SDGPC and the discrete-time approach is analyzed. Some simulation examples are presented to illustrate the properties of SDGPC
Keywords :
adaptive control; closed loop systems; predictive control; sampled data systems; stability; state-space methods; tracking; Laguerre filter modeling; adaptive control; closed-loop stability; constant disturbance; constant setpoint tracking; continuous-time quadratic cost function; continuous-time state space model; sampled-data generalized predictive control; Adaptive control; Adaptive filters; Cost function; Industrial control; Industrial relations; Prediction algorithms; Predictive control; Programmable control; Stability analysis; State-space methods;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.411702
