1,721,041 research outputs found
IEEE Transactions on Automatic Control
No full textIn the IEEE Transactions on Automatic Control, the IEEE Control Systems Society publishes high-quality papers on the theory, design, and applications of control engineering. Two types of contributions are regularly considered:
1) Papers: Presentation of significant research, development, or application of control concepts.
2) Technical Notes and Correspondence: Brief technical notes, comments on published areas or established control topics, corrections to papers and notes published in the Transactions.
In addition, special papers (tutorials, surveys, and perspectives on the theory and applications of control systems topics) are solicited
IEEE Control Systems Letters
No full textThe IEEE Control Systems Letters (L-CSS) publishes peer-reviewed brief articles that provide a rapid and concise account of innovative ideas regarding the theory, design, and applications of all aspects of control engineering.
The Letters aims at ensuring rapid dissemination of the most recent results in the broad field of Systems and Control by providing its readers with the state of the art of research in this area
IEEE Transactions on Control Systems Technology
No full textThe IEEE Transactions on Control Systems Technology publishes high quality technical papers on technological advances in control engineering. The word technology is from the Greek technologia. The modern meaning is a scientific method to achieve a practical purpose. Control Systems Technology includes all aspects of control engineering needed to implement practical control systems, from analysis and design, through simulation and hardware. A primary purpose of the IEEE Transactions on Control Systems Technology is to have an archival publication which will bridge the gap between theory and practice. Papers are published in the IEEE Transactions on Control System Technology which disclose significant new knowledge, exploratory developments, or practical applications in all aspects of technology needed to implement control systems, from analysis and design through simulation, and hardware
A Randomized Block Subgradient Approach to Distributed Big Data Optimization
Full text linkThis paper introduces a novel distributed algorithm over static directed graphs for solving big data convex optimization problems in which the dimension of the decision variable can be extremely high and the objective function can be nonsmooth. In the proposed algorithm nodes in the network update and communicate only blocks of their current solution estimate rather than the entire vector. The algorithm consists of two main steps: a consensus step and a subgradient update on a single block of the optimization variable (which is then broadcast to neighbors). Agents are shown to asymptotically achieve consensus by studying a block-wise consensus protocol over random graphs. Then convergence to the optimal cost is proven in expected value by exploiting the consensus of agents estimates and randomness of the algorithm. Finally, as a numerical example, a distributed linear classification problem is solved by means of the proposed algorithm
Politiche pubbliche. Analisi e valutazione
No full textQuesto manuale innovativo tratta in modo integrato gli aspetti fondamentali delle politiche pubbliche. La prima parte dedicata all'analisi, la seconda ai metodi e all'evidenza empirica, la terza alla valutazione, mentre il capitolo conclusivo è dedicato alla progettazione istituzionale e alla deontologia. Il volume contiene scritti di Umberto Di Maggio, Efisio Espa, Giovanni Frazzica, Giuseppe Notarstefano, Valentina Punzo, Attilio Scaglione. https://www.mulino.it/isbn/978881528493
Distributed partition-based optimization via dual decomposition
No full textIn this paper we consider a novel partition-based framework for distributed optimization in peer-to-peer networks. In several important applications the agents of a network system have to solve an optimization problem with two important features: (i) the dimension of the decision variable is a function of the network size, and (ii) the cost function and the constraints have a sparsity structure that is related to the sparsity of the graph. For this class of problems a straightforward application of existing methods would result in all the nodes reaching consensus on the minimizer. This approach has two inefficiencies: poor scalability and redundancy of shared information. Indeed, the dimension of the vector stored by each node and the size of the local problem to be solved depend on the network size. Furthermore, all the nodes compute the entire solution. In this paper we provide a preliminary contribution in developing and analyzing novel partition based algorithms. We propose a partition-based algorithm based on dual decomposition. We show that, exploiting the problem structure, the solution can be partitioned among the nodes so that each node stores a local copy of just a portion of the decision variable (rather than a copy of the entire decision vector) and solves a small scale local problem
Distributed abstract optimization via constraints consensus: theory and applications
No full textDistributed abstract programs are a novel class of distributed optimization problems where (i) the number of variables is much smaller than the number of constraints and (ii) each constraint is associated to a network node. Abstract optimization programs are a generalization of linear programs that captures numerous geometric optimization problems. We propose novel constraints consensus algorithms for distributed abstract programs with guaranteed finite-time convergence to a global optimum. The algorithms rely upon solving local abstract programs and exchanging the solutions among neighboring processors. The proposed algorithms are appropriate for networks with weak time-dependent connectivity requirements and tight memory constraints. We show how the constraints consensus algorithms may be applied to suitable target localization and formation control problems
A projected SQP method for nonlinear optimal control with quadratic convergence
No full textIn this paper, we propose a discrete-time Sequential Quadratic Programming (SQP) algorithm for nonlinear optimal control problems. Using the idea by Hauser of projecting curves onto the trajectory space, the introduced algorithm has guaranteed recursive feasibility of the dynamic constraints. The second essential feature of the algorithm is a specific choice of the Lagrange multiplier update. Due to this ad hoc choice of the multiplier, the algorithm converges locally quadratically. Finally, we show how the proposed algorithm connects standard SQP methods for nonlinear optimal control with the Projection Operator Newton method by Hauser
On the observability of path and cycle graphs
No full textIn this paper we investigate the observability properties of a network system, running a Laplacian based average consensus algorithm, when the communication graph is a path or a cycle. More in detail, we provide necessary and sufficient conditions, based on simple algebraic rules from number theory, to characterize all and only the nodes from which the network system is observable. Interesting immediate corollaries of our results are: (i) a path graph is observable from any single node if and only if the number of nodes of the graph is a power of two, n = 2^i, i ∈ N, and (ii) a cycle is observable from any pair of observation nodes if and only if n is a prime number. For any set of observation nodes, we provide a closed form expression for the unobservable eigenvalues and for the eigenvectors of the unobservable subspace
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