In Model Predictive Control, the enforcement of hard state constraints can be overly conservative or even infeasible, especially in the presence of disturbances. This work presents a soft constrained MPC approach that provides closed- loop stability even f ...
Explicit Model Predictive Control (EMPC) produces control laws defined over a set of polytopic regions in the state space. In this paper we present a method to create a binary search tree for point location in such polytopic sets, in order to provide a fas ...
A predictive mechanism is proposed in order to reduce price volatility linked to large fluctuations from de- mand and renewable energy generation in competitive electricity markets. The market participants are modelled as price-elastic units, price-inelast ...
This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ...
In this paper an algorithm for nonlinear explicit model predictive control is introduced based on multiresolution function approximation that returns a low complexity approximate receding horizon control law built on a hierarchy of second order interpolets ...
Robust state-feedback model predictive control (MPC) of discrete-time periodic affine systems is considered. States and inputs are subject to periodically time-dependent, hard, convex, polyhedral constraints. Disturbances are additive, bounded and subject ...
The application of nuclear norm regularization to system identification was recently shown to be a useful method for identifying low order linear models. In this paper, we consider nuclear norm regularization for identification of simulated moving bed proc ...
The application of nuclear norm regularization to system identification was recently shown to be a useful method for identifying low order linear models. In this paper, we consider nuclear norm regularization for identification of LTI systems with missing ...
In this paper, an algorithm is introduced based on classical wavelet multiresolution analysis that returns a low complexity explicit model predictive control law built on a hierarchy of second order interpolating wavelets. It is proven that the resulting i ...
Linear quadratic model predictive control (MPC) with input constraints leads to an optimization problem that has to be solved at every instant in time. Although there exists computational complexity analysis for current online optimization methods dedicate ...
In this paper we use bilevel programming to find the maximum difference between a reference controller and a low-complexity controller in terms of the infinity-norm difference of their control laws. A nominal MPC for linear systems with constraints, and a ...
State-feedback model predictive control (MPC) of discrete-time linear periodic systems with possibly time-dependent state and control input dimension is considered. States and inputs are subject to hard, mixed, polytopic constraints. It is described how di ...
A linear quadratic model predictive controller (MPC) can be written as a parametric quadratic optimization problem whose solution is a piecewise affine (PWA) map from the state to the optimal input. While this `explicit solution' can offer several orders o ...
Many model predictive control (MPC) schemes suffer from high computational complexity. Especially robust MPC schemes, which explicitly account for the effects of disturbances, can result in computationally intractable problems. So-called move-blocking is a ...
Recent results have suggested that online Model Predictive Control (MPC) can be computed quickly enough to control fast sampled systems. High-speed applications impose a hard real-time constraint on the solution of the MPC problem, which generally prevents ...
A standard model predictive controller (MPC) can be written as a parametric optimization problem whose solution is a piecewise affine (PWA) map from the measured state to the optimal control input. The primary limitation of this optimal `explicit solution ...
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 ...
This paper deals with model predictive control of uncertain linear discrete-time systems with polytopic constraints on the input and chance constraints on the states. When having polytopic constraints and bounded disturbances, the robust problem with an op ...
We consider the class of piecewise affine optimal state feedback control laws applied to discrete-time piecewise affine systems, motivated by recent work on the computation of closed-form MPC controllers. The storage demand and complexity of these optimal ...
An explicit (or closed-form) solution to Model Predictive Control (MPC) results in a polyhedral subdivision of the state-space when the system and constraints are linear, and the cost is linear or quadratic. Within each region the optimal control law is an ...
