Difference between revisions of "EECI08: Optimization-Based Control"

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m (NCS: Optimization-Based Control moved to EECI: Optimization-Based Control)

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In this lecture we describe how real-time optimization can be used to design feedback control algorithms for nonlinear, constrained systems. The receding horizon control (RHC) principle is described and the main ingredients required for its stability are discussed. Efficient numerical methods can then be used to find trajectories that satify the system dynamics and constraints, as well as minimizing a cost function. We concentrate on methods for real-time trajectory generation, and in particular the NTG software package.

Lecture Materials


  • Constrained model predictive control: Stability and optimality, D. Q. Mayne, J. B. Rawlings, C. V. Rao and P. O. M. Scokaert. Automatica, 2000, Vol. 36, No. 6, pp. 789-814. This is one of the most referenced comprehensive survey papers on MPC. Gives a nice overview about its history and explains the most important issues and various approaches.

  • Online Control Customization via Optimization-Based Control, R. M. Murray et al. In Software-Enabled Control: Information Technology for Dynamical Systems, T. Samad and G. Balas (eds.), IEEE Press, 2001. This paper talks about the CLF-based nonlinear RHC approach and its application on the Caltech ducted fan using NTG.

Additional Resources