期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 48, 期 9, 页码 1600-1606出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2003.816984
关键词
constraints; multiparametric programming; optimal control; receding horizon control (RHC); robustness
For discrete-time uncertain linear systems with constraints on inputs and states, we develop an approach to determine state feedback controllers based on a min-max control formulation., Robustness is achieved against additive. norm-bounded input, disturbances. and/or polyhedral parametric uncertainties in the state-space matrices. We show that the finite-horizon robust optimal control law is a continuous piecewise affine function of the state vector and can be calculated by solving a sequence of multiparametric linear programs. When the optimal control law is implemented in a receding horizon scheme, only,a piecewise affine function needs to be evaluated on line at each time step. The technique computes the robust optimal feedback controller for a rather general class of systems with modest computational effort without needing to resort to gridding of the state-space.
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