Abstract
A receding horizon control methodology is proposed for systems with nonlinear dynamics, additive stochastic uncertainty, and both hard and soft (probabilistic) input/state constraints. Jacobian linearization about predicted trajectories is used to derive a sequence of convex optimization problems. Constraints are handled through the construction of tubes and an associated Markov chain model. The parameters defining the tubes are optimized simultaneously with the predicted future control trajectory via online linear programming.
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© 2009 Springer-Verlag Berlin Heidelberg
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Cannon, M., Ng, D., Kouvaritakis, B. (2009). Successive Linearization NMPC for a Class of Stochastic Nonlinear Systems. In: Magni, L., Raimondo, D.M., Allgöwer, F. (eds) Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01094-1_20
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DOI: https://doi.org/10.1007/978-3-642-01094-1_20
Publisher Name: Springer, Berlin, Heidelberg
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