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Constrained nonlinear optimal control: a converse HJB approach

Nevistić, Vesna and Primbs, James A. (1996) Constrained nonlinear optimal control: a converse HJB approach. California Institute of Technology , Pasadena, CA. (Unpublished)

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Extending the concept of solving the Hamilton-Jacobi-Bellman (HJB) optimization equation backwards [2], the so called converse constrained optimal control problem is introduced, and used to create various classes of nonlinear systems for which the optimal controller subject to constraints is known. In this way a systematic method for the testing, validation and comparison of different control techniques with the optimal is established. Because it naturally and explicitly handles constraints, particularly control input saturation, model predictive control (MPC) is a potentially powerful approach for nonlinear control design. However, nonconvexity of the nonlinear programs (NLP) involved in the MPC optimization makes the solution problematic. In order to explore properties of MPC-based constrained control schemes, and to point out the potential issues in implementing MPC, challenging benchmark examples are generated and analyzed. Properties of MPC-based constrained techniques are then evaluated and implementation issues are explored by applying both nonlinear MPC and MPC with feedback linearization.

Item Type:Report or Paper (Technical Report)
Additional Information:Supported by NSF.
Group:Control and Dynamical Systems Technical Reports
Record Number:CaltechCDSTR:1996.021
Persistent URL:
Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:28127
Deposited By: Imported from CaltechCDSTR
Deposited On:15 Dec 2007
Last Modified:03 Oct 2019 03:29

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