Nonquadratic Stochastic Model Predictive Control: A Tractable Approach

This paper deals with the nite horizon stochastic optimal control problem with the expectation of the p-norm as the objective function and jointly Gaussian, although not necessarily independent, additive disturbance process. We develop an approximation strategy that solves the problem in a certain class of nonlinear feedback policies while ensuring satisfaction of hard input constraints. A bound on suboptimality of the proposed strategy in this class of nonlinear feedback policies is given for the special case of p = 1. We also develop a recursively feasible receding horizon policy with respect to state chance constraints and/or hard control input constraints in the presence of bounded disturbances. The performance of the proposed policies is examined in two numerical examples.


Published in:
Automatica, 48, 9, 2352–2358
Year:
2012
Publisher:
Oxford, Pergamon-Elsevier Science Ltd
Keywords:
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 Record created 2012-07-07, last modified 2018-01-28

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