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The Advanced Step NMPC Controller: Optimality, Stability and Robustness

Victor M Zavala(vzavala***at***mcs.anl.gov)
Lorenz T Biegler(lb01***at***andrew.cmu.edu)

Abstract: Widespread application of dynamic optimization with fast optimization solvers leads to increased consideration of first-principles models for nonlinear model predictive control (NMPC). However, significant barriers to this optimization-based control strategy are feedback delays and consequent loss of performance and stability due to on-line computation. To overcome these barriers, recently proposed NMPC controllers based on nonlinear programming (NLP) sensitivity have reduced on-line computational costs and can lead to significantly improved performance. In this study, we extend this concept through a simple reformulation of the NMPC problem and propose the advanced-step NMPC controller. The main result of this extension is that the proposed controller enjoys the same nominal stability properties of the conventional NMPC controller without computational delay. In addition, we establish further robustness properties in a straightforward manner through input-to-state stability concepts. A case study example is presented to demonstrate the concepts.

Keywords: Nonlinear model predictive control; Fast; Large scale; Nonlinear programming; Sensitivity; Stability; Lyapunov functions

Category 1: Nonlinear Optimization

Category 2: Applications -- Science and Engineering (Optimization of Systems modeled by PDEs )

Category 3: Applications -- Science and Engineering (Control Applications )


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Entry Submitted: 01/28/2009
Entry Accepted: 01/28/2009
Entry Last Modified: 01/28/2009

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