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Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed ResolvabilityA robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.
Document ID
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Acikmese, Behcet A. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Carson, John M., III (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 24, 2013
Publication Date
September 2, 2005
Subject Category
Mathematical and Computer Sciences (General)
Report/Patent Number
Distribution Limits
model predictive control algorithm (MPC)