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Found 6 papers, 0 papers with code
Extending direct data-driven predictive control towards systems with finite control sets
We test the reformulation on a popular electrical drive example and compare the computation times of sphere decoding FCS-DPC with an enumeration-based and a MIQP method.
Towards a unifying framework for data-driven predictive control with quadratic regularization
Data-driven predictive control (DPC) has recently gained popularity as an alternative to model predictive control (MPC).
Implicit predictors in regularized data-driven predictive control
We introduce the notion of implicit predictors, which characterize the input-(state)-output prediction behavior underlying a predictive control scheme, even if it is not explicitly enforced as an equality constraint (as in traditional model or subspace predictive control).
Convex NMPC reformulations for a special class of nonlinear multi-input systems with application to rank-one bilinear networks
We show that a special class of (nonconvex) NMPC problems admits an exact solution by reformulating them as a finite number of convex subproblems, extending previous results to the multi-input case.
Convex reformulations for a special class of nonlinear MPC problems
We show how the solution to NMPC problems for a special type of input-affine discrete-time systems can be obtained by reformulating the underlying non-convex optimal control problem in terms of a finite number of convex subproblems.
A deterministic view on explicit data-driven (M)PC
We show that the explicit realization of data-driven predictive control (DPC) for linear deterministic systems is more tractable than previously thought.
