Constrained receding-horizon predictive control (CRHPC) is intended for demanding applications where conventional designs can fail. The idea behind CRHPC to optimise a quadratic function over 'costing horizon' subject the condition that output matches reference value further constraint range. Theorems show method stabilises general linear plants (e.g. unstable, nonminimum-phase, dead-time). Simulation studies demonstrate good behaviour with even nearly unobservable systems (where generalised...

In this paper, we address the problem of driving a group agents towards consensus point when have discrete-time single- or double-integrator dynamics and communication network is time-varying. We propose decentralized model predictive control schemes that take into account constraints on agents' input show they guarantee under mild assumptions. Since global cost does not decrease monotonically, it cannot be used as Lyapunov function for proving convergence to consensus. For reason, our...

A receding horizon control scheme for nonlinear time-varying systems is proposed which based on a finite-horizon optimization problem with terminal state penalty. The penalty equal to the cost that would be incurred over an infinite by applying (locally stabilizing) linear law system. Assuming only stabilizability of linearized system around desired equilibrium, new ensures exponential stability equilibrium. As length goes from zero infinity, domain attraction moves basin controller toward...

Abstract This paper describes a model predictive control (MPC) algorithm for the solution of state‐feedback robust problem discrete‐time nonlinear systems. The law is obtained through finite‐horizon dynamic game and guarantees stability in face class bounded disturbances and/or parameter uncertainties. A simulation example reported to show applicability method. Copyright © 2003 John Wiley & Sons, Ltd.

This paper presents a novel distributed estimation algorithm based on the concept of moving horizon estimation. Under weak observability conditions we prove convergence state estimates computed by any sensors to correct even when constraints noise and variables are taken into account in process. Simulation examples provided order show main features proposed method.

A two-layer control scheme based on model predictive (MPC) operating at two different timescales is proposed for the energy management of a grid-connected microgrid (MG), including battery, microturbine, photovoltaic (PV) system, partially non predictable load, and input from electrical network. The high-level optimizer runs slow timescale, relies simplified in charge computing nominal conditions each MG component over long time horizon, typically one day, with sampling period 15 min, so as...

This article addresses the data-based modeling and optimal control of district heating systems (DHSs). Physical models such large-scale networked are governed by complex nonlinear equations that require a large amount parameters, leading to potential computational issues in optimizing their operation. A novel methodology is hence proposed, exploiting operational data available physical knowledge attain accurate computationally efficient DHSs dynamic models. The proposed idea consists...

In this note, regional input-to-state stability (ISS) is introduced and studied in order to analyze the domain of attraction nonlinear constrained systems with disturbances. ISS derived by means a non smooth ISS-Lyapunov function an upper bound guaranteed only sub-region attraction. These results are used study properties model predictive control (MPC) algorithms

Abstract This paper presents stabilizing decentralized model predictive control (MPC) algorithms for discrete‐time nonlinear systems. The overall system under is composed by a number of subsystems, each one locally controlled with an MPC algorithm guaranteeing the input‐to‐state stability (ISS) property. Then, main result derived considering effect interconnections as perturbation terms and showing that also ISS. Both open‐loop closed‐loop min–max formulations robust are considered....

A new model predictive control (MPC) algorithm for nonlinear systems is presented. The plant under control, the state and constraints, performance index to be minimized are described in continuous time, while manipulated variables allowed change at fixed uniformly distributed sampling times. In so doing, optimization performed with respect sequences, as discrete-time MPC, but continuous-time evolution of system considered MPC.

A robust model predictive control algorithm solving the tracking and infeasible reference problems for constrained systems subject to bounded disturbances is presented in this technical note. The proposed solution relies on three main concepts: 1) reformulation of system so-called velocity form obtain offset-free when constant are present, 2) use a tube-based approach cope with non-constant but disturbances, 3) outputs as arguments optimization problem references. Convergence results derived...

This paper presents a novel Model Predictive Control (MPC) algorithm for linear systems subject to stochastic noise and probabilistic constraints on the state control variables. The method is based reformulation of these in terms deterministic ones, use terminal mean value covariance state, binary strategy selection initial conditions be considered at any time instant MPC optimization problem. proposed characterized by computational burden similar one required stabilizing methods systems,...

This paper presents a hierarchical control architecture for the regulation of frequency and nodal voltages microgrid in islanded operation. Considering systems with both dispatchable nondispatchable generation, as well noncontrollable loads, suggested approach allows to coordinate MG devices order maintain network variables inside desired operational ranges. Moreover, proposed algorithm, based on model predictive control, introduces possibility define different resource management strategies...