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This paper examines the computational complexity certification of the fast gradient method for the solution of the dual of a parametric con- vex program. To this end, a lower iteration bound is derived such that for all parameters from a compact set a solu ...
Well-known model predictive control (MPC) theory for constrained linear time-invariant (LTI) systems is extended to accommodate hard constraints and cost penalizations on the spectra of the system’s output trajectories. Thus the proposed method facilitates ...
Research on sub-optimal Model Predictive Control (MPC) has led to a variety of optimization methods based on explicit or online approaches, or combinations thereof. Its foremost aim is to guarantee essential controller properties, i.e. recursive feasibilit ...
Fast model predictive control on embedded sys- tems has been successfully applied to plants with microsecond sampling times employing a precomputed state-to-input map. However, the complexity of this so-called explicit MPC can be prohibitive even for low-d ...
This paper looks at the efficiency of the parame- terisation of the degrees of freedom within an optimal predictive control algorithm. It is shown that the conventional approach of directly determining each individual future control move is not efficient i ...
This article presents an algorithm for robust nonlinear explicit model predictive control. A low complexity receding horizon control law is obtained by approximating the optimal control law using multiscale basis function approximation. Feasibility and inp ...
State-feedback model predictive control (MPC) of discrete-time linear periodic systems with time-dependent state and input dimensions is considered. The states and inputs are subject to periodically time-dependent, hard, convex, polyhedral constraints. Fir ...
This paper considers linear discrete-time systems with additive bounded disturbances subject to hard control input bounds and constraints on the expected number of state-constraint violations averaged over time, or, equivalently, constraints on the probabi ...
A model predictive control law is given by the solution to a parametric optimization problem that can be pre- computed offline and provides an explicit map from state to control input. In this paper, an algorithm is introduced based on wavelet multiresolut ...
Institute of Electrical and Electronics Engineers2011
Limits on the storage space or the computation time restrict the applicability of model predictive controllers (MPC) in many real problems. Currently available methods either compute the optimal controller online or derive an explicit control law. In this ...
Institute of Electrical and Electronics Engineers2011
