The paper considers the economic and production essence of information and communication technologies (ICT), as well as their importance for the development of digital industrial production. The main digital technolog...
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The paper considers the economic and production essence of information and communication technologies (ICT), as well as their importance for the development of digital industrial production. The main digital technologies are Big Data, artificial intelligence, blockchain, robotics, and new technologies of virtual and augmented realities. The implementation of algorithms and software for the verification of large programs for cloud applications is among the key areas of research and development in the field of ICT. The need to create neoclusters for innovative industrial development is noted, as neoclustering is the process of organizing and operating clusters based on digital information and communication technologies.
This technical communique presents a novel approach for fast computation of convex combination coefficients based on ray shooting. Such a problem arises in the real-time implementation of many set -based control techn...
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This technical communique presents a novel approach for fast computation of convex combination coefficients based on ray shooting. Such a problem arises in the real-time implementation of many set -based control techniques, which require a computationally efficient solution to cope with systems of high dimension or fast dynamics. The proposed method poses no constraints on the polytope structure and requires only a list of vertices and boundary hyperplanes. In test cases involving polytopes of various dimensions and numbers of vertices, the proposed method provided up to seven-fold reductions in the 95% percentile of computation time, compared with a state-of-the-art technique. (c) 2023 Elsevier Ltd. All rights reserved.
This paper introduces a novel time-freezing reformulation and numerical methods for optimal control of complementarity Lagrangian systems (CLS) with state jumps. We cover the difficult case when the system evolves on ...
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This paper introduces a novel time-freezing reformulation and numerical methods for optimal control of complementarity Lagrangian systems (CLS) with state jumps. We cover the difficult case when the system evolves on the boundary of the dynamic's feasible set after the state jump. In nonsmooth mechanics, this corresponds to inelastic impacts. The main idea of the time-freezing reformulation is to introduce a clock state and an auxiliary dynamical system whose trajectory endpoints satisfy the state jump law. When the auxiliary system is active, the clock state is not evolving, hence by taking only the parts of the trajectory when the clock state was active, we can recover the original solution. The resulting time-freezing system is a Filippov system that has jump discontinuities only in the first time derivative instead of the trajectory itself. This enables one to use the recently proposed Finite Elements with Switch Detection (Nurkanovic et al., 2022), which makes high accuracy numerical optimal control of CLS with impacts and friction possible. We detail how to recover the solution of the original system and show how to select appropriate auxiliary dynamics. The theoretical findings are illustrated on a nontrivial numerical optimal control example of a hopping one-legged robot. (c) 2023 Elsevier Ltd. All rights reserved.
For the fast approximate solution of Mixed-Integer Non-Linear Programs (MINLPs) arising in the context of Mixed-Integer Optimal Control Problems (MIOCPs) a decomposition algorithm exists that solves a sequence of thre...
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For the fast approximate solution of Mixed-Integer Non-Linear Programs (MINLPs) arising in the context of Mixed-Integer Optimal Control Problems (MIOCPs) a decomposition algorithm exists that solves a sequence of three comparatively less hard subproblems to determine an approximate MINLP solution. In this work, we propose a problem formulation for the second algorithm stage that is a convex approximation of the original MINLP and relies on the Gauss-Newton approximation. We analyze the algorithm in terms of approximation properties and establish a first-order consistency result. Then, we investigate the proposed approach considering a numerical case study of Mixed -Integer Optimal Control (MIOC) of a renewable energy system. The investigation shows that the proposed formulation can yield an improved integer solution regarding the objective of the original MINLP compared with the established Combinatorial Integral Approximation (CIA) algorithm. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
This article discusses the process of creating a software and hardware complex for obtaining meteorological data, based on which it is possible to draw up recommendations for choosing the most optimal energy supply sy...
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ISBN:
(纸本)9781728145907
This article discusses the process of creating a software and hardware complex for obtaining meteorological data, based on which it is possible to draw up recommendations for choosing the most optimal energy supply system using renewable energy sources. A method for obtaining data on wind speed and direction has been implemented, a methodology has been considered for processing these data to assess the potential of wind energy for a particular area. The Arduino microcontroller and corresponding measuring devices are used to obtain, collect and convert data. A software module that calculates the value of incoming solar radiation at a specific location of the consumer is considered. The use of this complex will allow evaluating the feasibility of involving renewable energy sources in the consumer's energy system.
We present the procedure for transforming delocalized molecular orbitals into the localized property-optimized orbitals (LPOs) designed for building the most accurate, in the Frobenius norm sense, approximation to the...
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We present the procedure for transforming delocalized molecular orbitals into the localized property-optimized orbitals (LPOs) designed for building the most accurate, in the Frobenius norm sense, approximation to the first-order reduced density matrix in form of the sum of localized monoatomic and diatomic terms. In this way, a decomposition of molecular properties into contributions associated with individual atoms and the pairs of atoms is obtained with the a priori known upper bound for the decomposition accuracy. Additional algorithm is proposed for obtaining the set of "the Chemist's LPOs" (CLPOs) containing a single localized orbital, with nearly double occupancy, per a pair of electrons. CLPOs form an idealized Lewis structure optimized for the closest possible reproduction of one-electron properties derived from the original many-electron wavefunction. The computational algorithms for constructing LPOs and CLPOs from a general wavefunction are presented and their implementation within the open-source freeware program JANPA (/) is discussed. The performance of the proposed procedures is assessed using the test set of density matrices of 33 432 small molecules obtained at both Hartree-Fock and second-order Moller-Plesset theory levels and excellent agreement with the chemist's Lewis-structure picture is found.
An output-feedback approach to model predictive control that combines state estimation and control into a single min-max optimization is introduced for discrete-time nonlinear systems. Specifically, a criterion that i...
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An output-feedback approach to model predictive control that combines state estimation and control into a single min-max optimization is introduced for discrete-time nonlinear systems. Specifically, a criterion that involves finite forward and backward horizons is minimized with respect to control input variables and is maximized with respect to the unknown initial state as well as disturbance and measurement noise variables. Under appropriate assumptions that encode controllability and observability, we show that the state of the closed-loop remains bounded and that a bound on tracking error can be found for trajectory tracking problems. We also introduce a primal-dual interior-point method that can be used to efficiently solve the min-max optimization problem and show in simulation examples that the method succeeds even for severely nonlinear and non-convex problems.(C) 2016 Elsevier Ltd. All rights reserved.
Direct optimal control algorithms first discretize the continuous-time optimal control problem and then solve the resulting finite dimensional optimization problem. If Newton type optimization algorithms are used for ...
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Direct optimal control algorithms first discretize the continuous-time optimal control problem and then solve the resulting finite dimensional optimization problem. If Newton type optimization algorithms are used for solving the discretized problem, accurate first as well as second order sensitivity information needs to be computed. This article develops a novel approach for computing Hessian matrices which is tailored for optimal control. Algorithmic differentiation based schemes are proposed for both discrete and continuous-time sensitivity propagation, including explicit as well as implicit systems of equations. The presented method exploits the symmetry of Hessian matrices, which typically results in a computational speedup of about factor 2 over standard differentiation techniques. These symmetric sensitivity equations additionally allow for a three-sweep propagation technique that can significantly reduce the memory requirements, by avoiding the need to store a trajectory of forward sensitivities. The performance of this symmetric sensitivity propagation is demonstrated for the benchmark case study of the economic optimal control of a nonlinear biochemical reactor, based on the open-source software implementation in the ACADO Toolkit. (C) 2016 Elsevier Ltd. All rights reserved.
This paper investigates the compositional abstraction-based synthesis of least restrictive, controllable, and nonblocking supervisors for discrete event systems that are given as a large number of finite-state machine...
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This paper investigates the compositional abstraction-based synthesis of least restrictive, controllable, and nonblocking supervisors for discrete event systems that are given as a large number of finite-state machines. It compares a previous algorithm that synthesises modular supervisors in the form of state machines, with an alternative that records state maps after each abstraction step and uses these to control the system. The state map-based algorithm supports all abstraction methods used previously, and in addition allows for nondeterminism, hiding, and transition removal. It has been implemented in the software tool Supremica and applied to several large industrial models. The experimental results and the complexity analysis show that state maps can be computed efficiently and in many cases require less memory than state machine-based supervisors. (C) 2016 Elsevier Ltd. All rights reserved.
Nonlinear model predictive control (NMPC) is a control strategy based on finding an optimal control trajectory that minimizes a given objective function. The optimization is recalculated at each control cycle and only...
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Nonlinear model predictive control (NMPC) is a control strategy based on finding an optimal control trajectory that minimizes a given objective function. The optimization is recalculated at each control cycle and only the first control values are actually used. The dynamics of the system can be nonlinear and there can be constraints on states and controls. A new toolkit called VIATOC has been developed that can be used to automatically generate the code needed to implement NMPC. The generated code is self-contained ANSI C and the compiled program has a small footprint. In VIATOC, the gradient projection method is used to solve the nonlinear optimization problem. Barzilai-Borwein type step length selection for the gradient method has also been implemented. The performance of the controllers generated with the toolkit is compared with those solved with the ACADO toolkit and HQP. The performance of the optimization is compared with two different test cases with different numbers of controls and states. The first one is based on a model of a pendulum hanging freely on a movable platform. The second one is a more complex model of a chain of three masses connected by springs. Seven different prediction horizons between 10 and 100 steps are used. When the time to achieve a near optimum solution is measured, VIATOC is in most cases the fastest one when the length of the prediction horizon is shorter than 70 steps. (C) 2015 Elsevier Ltd. All rights reserved.
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