EECI08: Optimization-Based Control - Revision history

Murray: EECI: Optimization-Based Control moved to EECI08: Optimization-Based Control

2009-03-01T20:13:52Z

EECI: Optimization-Based Control moved to EECI08: Optimization-Based Control

← Older revision		Revision as of 20:13, 1 March 2009
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Murray at 01:00, 29 March 2008

2008-03-29T01:00:48Z

← Older revision		Revision as of 01:00, 29 March 2008
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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.		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}}

Murray at 00:57, 29 March 2008

2008-03-29T00:57:43Z

← Older revision		Revision as of 00:57, 29 March 2008
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	{{eeci-sp08 header\|prev=[[~~NCS~~: Trajectory Generation and Differential Flatness\|Trajectory Generation]]\|next=[[EECI: 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.

Murray at 15:20, 28 March 2008

2008-03-28T15:20:23Z

← Older revision		Revision as of 15:20, 28 March 2008
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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=[[NCS: 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.

Murray: NCS: Optimization-Based Control moved to EECI: Optimization-Based Control

2008-03-28T15:19:25Z

NCS: Optimization-Based Control moved to EECI: Optimization-Based Control

← Older revision		Revision as of 15:19, 28 March 2008
(No difference)

Murray at 20:34, 16 March 2008

2008-03-16T20:34:18Z

New page

{{eeci-sp08 header|prev=[[NCS: Trajectory Generation and Differential Flatness|Trajectory Generation]]|next=[[NCS: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.

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

== Reading ==
* [http://dx.doi.org/10.1016/S0005-1098(99)00214-9 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.
* [http://www.cds.caltech.edu/~murray/papers/2001n_mur+03-sec.html 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 ==
* [http://www.eng.newcastle.edu.au/eecs/cdsc/books/cce/ Constrained Control and Estimation - An Optimisation Approach], G. C. Goodwin, M. M. Seron, J. A. De Dona. Springer Verlag, 2005. This is a recent book treating constrained control and estimation in a unified framework (including finite horizon optimal control and RHC) using discrete-time formulation. The website has a lot of additional useful and interesting material.
* [http://www.seas.upenn.edu/~jadbabai/papers/TAC_jh_final.pdf Unconstrained Receding-Horizon Control of Nonlinear Systems], A. Jadbabaie, J. Yu and J. Hauser. IEEE Transactions on Automatic Control, May 2001, Vol. 46, No. 5, pp. 776-783. This paper might be a little dense for the first read, but contains an essence of A. Jadbabaie's PhD thesis on CLF-based nonlinear RHC.
* [http://www.seas.upenn.edu/~jadbabai/papers/Phdthesis.pdf Nonlinear Receding Horizon Control: A Control Lyapunov Function Approach], A. Jadbabaie. PhD Thesis, 2000.
* [http://www.cds.caltech.edu/~murray/papers/2003_milam03-phd.html Real-Time Optimal Trajectory Generation for Constrained Dynamical Systems], M. Milam. PhD Thesis, 2003.
* [http://www.cds.caltech.edu/~murray/software/2002a_ntg.html NTG software], version 2.2a, 2002. This is the last publically released version of [[NTG]]. The documentation is a bit sparse, but the examples are heavily commented.
* [http://aero.tamu.edu/people/raktim/sw.html Optragen], version 1.0, 2006. This is a new MATLAB toolbox for optimal trajectory generation written by Raktim Bhattacharya, a former postdoc at Caltech. This version does not run in real-time, but has a much more user-friendly interface than NTG.