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Sampled-data generalized predictive control (SDGPC)

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Title: Sampled-data generalized predictive control (SDGPC)
Author: Lu, Guoqiang
Degree Doctor of Philosophy - PhD
Program Electrical and Computer Engineering
Copyright Date: 1996
Abstract: This thesis develops a novel predictive control strategy called Sampled-Data Generalized Predictive Control (SDGPC). SDGPC is based on a continuous-time model yet assumes the projected control profile to be piecewise constant, i.e. to be compatible with zero order hold circuit. It thus enjoys both the advantage of continuous-time modeling and the flexibility of digital implementation. SDGPC is shown to be equivalent to an infinite horizon LQ control law under certain conditions. For well-damped open-loop stable systems, the piecewise constant projected control scenario adopted in SDGPC is shown to have benefits such as reduced computational burden, increased numerical robustness etc. When extending SDGPC to tracking design, it is shown that future knowledge of the setpoint significandy improves tracking performance. A two-degree-of-freedom SDGPC based on optimization of two performance indices is proposed. Actuator constraints are considered in an anti-windup framework. It is shown that the nonlinear control problem is equivalent to a linear time-varying problem. The proposed anti-windup algorithm is also shown to have attractive stability properties. Time-delay systems are treated later. It is shown that the Laguerre-filter-based adaptive SDGPC has excellent performance controlling systems with varying time-delay. An algorithm for continuous-time system parameter estimation based on sampled input output data is presented. The effectiveness and the advantages of continuous-time model estimation and the SDGPC algorithm over the pure discrete-time approach are highlighted by an inverted pendulum experiment.
URI: http://hdl.handle.net/2429/4788
Series/Report no. UBC Retrospective Theses Digitization Project [http://www.library.ubc.ca/archives/retro_theses/]

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