Control Systems

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By José M. Maestre, Rudy R. Negenborn

The fast evolution of laptop technology, communique, and data expertise has enabled the applying of keep an eye on suggestions to platforms past the probabilities of regulate idea only a decade in the past. serious infrastructures comparable to electrical energy, water, site visitors and intermodal delivery networks are actually within the scope of keep watch over engineers. The sheer dimension of such large-scale structures calls for the adoption of complicated allotted keep watch over methods. dispensed version predictive keep watch over (MPC) is likely one of the promising regulate methodologies for keep an eye on of such systems.

This booklet presents a state of the art evaluation of allotted MPC ways, whereas even as making transparent instructions of analysis that deserve extra consciousness. The center and motive of 35 methods are conscientiously defined. furthermore, distinct step by step algorithmic descriptions of every procedure are supplied. those positive factors make the ebook a accomplished advisor either for these looking an creation to allotted MPC in addition to when you are looking to achieve a deeper perception within the wide variety of dispensed MPC innovations available.

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D. Axehill, A. Hansson, Parallel implementation of hybrid MPC, in Distributed Model Predictive Control Made Easy, ed. M. R. Negenborn (Springer, Dordrecht, 2013), pp. 375–392 3. G. Betti, M. Farina, R. Scattolini, Distributed MPC: A noncooperative approach based on robustness concepts, in Distributed Model Predictive Control Made Easy, ed. M. R. Negenborn (Springer, Dordrecht, 2013), pp. 421–435 4. B. Biegel, J. Stoustrup, P. Andersen, Distributed MPC via dual decomposition, in Distributed Model Predictive Control Made Easy, ed.

An utopia point defined by the set of choices where all players involved in the bargaining achieve at the same time their maximum benefit. In game theory a solution means a determination of the amount of satisfaction each player should expect to get from the situation. In specific games like bargaining ones, the concept of solution is associated with the determination of how much it should be worth to each of these players the right to have a chance for bargaining. , “to assess your added value, you have to ask not only what other players can bring to you but what you can bring to other players” [3].

Maestre Chapter 14 System Type Process Type Type of Model Randomness Type of Control Coupling Source Architecture Controller Knowledge Computation Type Controller Attitude Communication Timing Optimization Variables Optimality Suboptimality Bounds Stability Robustness Chapter 15 Distributed Lyapunov-based MPC MaGloNit-Her [14] Process Multiple Autonomous Systems Decomposed Monolithical System Linear Nonlinear Hybrid Transfer function State Space Deterministic Non-deterministic Regulation Tracking Economic Constraints Objective Input Output State Control Architecture Decentralized Distributed Hierarchical Strictly Local Partially Global Iterative Non-iterative Cooperative Noncooperative Serial Parallel Synchronous Asynchronous Real Integer Theoretical Properties Yes No Yes No Yes No Yes No A distributed reference management scheme in presence of nonconvex constraints: an MPC based approach MaGloNit-Ted [32] Process Multiple Autonomous Systems Decomposed Monolithical System Linear Nonlinear Hybrid Transfer function State Space Deterministic Non-deterministic Regulation Tracking Economic Constraints Objective Input Output State Control Architecture Architecture Decentralized Distributed Hierarchical Controller Knowledge Strictly Local Partially Global Computation Type Iterative Non-iterative Controller Attitude Cooperative Noncooperative Communication Serial Parallel Timing Synchronous Asynchronous Optimization Variables Real Integer Theoretical Properties Optimality Yes No Suboptimality Bounds Yes No Stability Yes No Robustness Yes No System Type Process Type Type of Model Randomness Type of Control Coupling Source 1 On 35 Approaches for Distributed MPC Made Easy Chapter 16 25 The distributed command governor approach in a nutshell MaGloNit-Cas [7] Optimality Suboptimality Bounds Stability Robustness Process Multiple Autonomous Systems Decomposed Monolithical System Linear Nonlinear Hybrid Transfer function State Space Deterministic Non-deterministic Regulation Tracking Economic Constraints Objective Input Output State Control Architecture Decentralized Distributed Hierarchical Strictly Local Partially Global Iterative Non-iterative Cooperative Noncooperative Serial Parallel Synchronous Asynchronous Real Integer Theoretical Properties Yes No Yes No Yes No Yes No Chapter 17 Mixed-integer programming techniques in distributed MPC problems MaGloIt-Pro [30] System Type Process Type Type of Model Randomness Type of Control Coupling Source Architecture Controller Knowledge Computation Type Controller Attitude Communication Timing Optimization Variables System Type Process Type Type of Model Randomness Type of Control Coupling Source Architecture Controller Knowledge Computation Type Controller Attitude Communication Timing Optimization Variables Optimality Suboptimality Bounds Stability Robustness Process Multiple Autonomous Systems Decomposed Monolithical System Linear Nonlinear Hybrid Transfer function State Space Deterministic Non-deterministic Regulation Tracking Economic Constraints Objective Input Output State Control Architecture Decentralized Distributed Hierarchical Strictly Local Partially Global Iterative Non-iterative Cooperative Noncooperative Serial Parallel Synchronous Asynchronous Real Integer Theoretical Properties Yes No Yes No Yes No Yes No 26 R.

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