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

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{{eeci-sp08 header|prev=[[NCS: Trajectory Generation and Differential Flatness|Trajectory Generation]]|next=[[NCS:State Estimation and Sensor Fusion|Sensor Fusion]]}}
{{eeci-sp08 header|prev=[[EECI: Trajectory Generation and Differential Flatness|Trajectory Generation]]|next=[[EECI: State Estimation and Sensor Fusion|Sensor Fusion]]}}


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.
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 ====
==  Lecture Materials ==
* Lecture slides: {{eeci-sp08 pdf|L5_optimal.pdf|Optimization-Based Control}}
* Lecture slides: {{eeci-sp08 pdf|L5_optimal.pdf|Optimization-Based Control}}
* Lecture notes: {{obc08|Chapter 3 - Receding Horizon Control}}
* Lecture notes: {{obc08|Chapter 3 - Receding Horizon Control}}

Latest revision as of 20:13, 1 March 2009

Prev: Trajectory Generation Course home Next: Sensor Fusion

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

Reading

  • 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