Reduced Dimension Approach to Approximate Explicit Model Predictive Control
نویسندگان
چکیده
Exact or approximate solutions to constrained linear model predictive control (MPC) problems can be pre-computed off-line in an explicit form as a piecewise linear state feedback defined on a polyhedral partition of the state space. However, the complexity of the polyhedral partition often increases rapidly with the dimension of the state vector, and the number of constraints. This paper presents an approach for reducing the dimension of the approximate explicit solution to linear constraint MPC problems.
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