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Constrained model predictive control with simultaneous identification using wavelets
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DOI：10.1016/0098-1354(96)00176-7.png)
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Model Predictive Control (MPC) often requires that a new process model be developed from data collected while the process remains under feedback control. How can a good model be obtained for a process under constrained MPC, without excessive perturbation of the process? We propose a novel approach, model predictive control and identification (MPCI), that relies on augmenting the standard on-line MPC optimization with a series of persistent excitation (PE) constraints that current and future process inputs must satisfy over a finite horizon. For linear processes, the resulting on-line optimization problem is solved by solving a series of semi-definite programming (SP) problems, for which efficient numerical methods with guaranteed convergence exist. The addition of the PE constraint to standard MPC paves the way for a number of different MPCI variants. A variant of MPCI involving time-frequency process identification with wavelets is proposed in the paper.
Keyword:
MULTIVARIABLE SYSTEMS
STABILITY
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