4,820 research outputs found
Reliability-based economic model predictive control for generalized flow-based networks including actuators' health-aware capabilities
This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalized flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamically allocate safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the considered case study.Peer ReviewedPostprint (author's final draft
Constrained LQR Using Online Decomposition Techniques
This paper presents an algorithm to solve the infinite horizon constrained
linear quadratic regulator (CLQR) problem using operator splitting methods.
First, the CLQR problem is reformulated as a (finite-time) model predictive
control (MPC) problem without terminal constraints. Second, the MPC problem is
decomposed into smaller subproblems of fixed dimension independent of the
horizon length. Third, using the fast alternating minimization algorithm to
solve the subproblems, the horizon length is estimated online, by adding or
removing subproblems based on a periodic check on the state of the last
subproblem to determine whether it belongs to a given control invariant set. We
show that the estimated horizon length is bounded and that the control sequence
computed using the proposed algorithm is an optimal solution of the CLQR
problem. Compared to state-of-the-art algorithms proposed to solve the CLQR
problem, our design solves at each iteration only unconstrained least-squares
problems and simple gradient calculations. Furthermore, our technique allows
the horizon length to decrease online (a useful feature if the initial guess on
the horizon is too conservative). Numerical results on a planar system show the
potential of our algorithm.Comment: This technical report is an extended version of the paper titled
"Constrained LQR Using Online Decomposition Techniques" submitted to the 2016
Conference on Decision and Contro
Model predictive control techniques for hybrid systems
This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581
Vibration suppression in multi-body systems by means of disturbance filter design methods
This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort
Distributed model predictive control of steam/water loop in large scale ships
In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method
Learning to Predict the Cosmological Structure Formation
Matter evolved under influence of gravity from minuscule density
fluctuations. Non-perturbative structure formed hierarchically over all scales,
and developed non-Gaussian features in the Universe, known as the Cosmic Web.
To fully understand the structure formation of the Universe is one of the holy
grails of modern astrophysics. Astrophysicists survey large volumes of the
Universe and employ a large ensemble of computer simulations to compare with
the observed data in order to extract the full information of our own Universe.
However, to evolve trillions of galaxies over billions of years even with the
simplest physics is a daunting task. We build a deep neural network, the Deep
Density Displacement Model (hereafter DM), to predict the non-linear
structure formation of the Universe from simple linear perturbation theory. Our
extensive analysis, demonstrates that DM outperforms the second order
perturbation theory (hereafter 2LPT), the commonly used fast approximate
simulation method, in point-wise comparison, 2-point correlation, and 3-point
correlation. We also show that DM is able to accurately extrapolate far
beyond its training data, and predict structure formation for significantly
different cosmological parameters. Our study proves, for the first time, that
deep learning is a practical and accurate alternative to approximate
simulations of the gravitational structure formation of the Universe.Comment: 8 pages, 5 figures, 1 tabl
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