Download Control of Complex Systems: Structural Constraints and by Aleksandar Zecevic, Dragoslav D. Siljak PDF
By Aleksandar Zecevic, Dragoslav D. Siljak
Control of complicated structures: Structural Constraints and Uncertainty specializes in keep watch over layout below details constitution constraints, with a specific emphasis on large-scale structures. The complexity of such platforms poses severe computational demanding situations and critically restricts the categories of suggestions legislation that may be utilized in perform. This publication systematically addresses the most matters, and offers a few purposes that illustrate strength layout tools, so much which use Linear Matrix Inequalities (LMIs), that have turn into a well-liked layout instrument over the last twenty years. Authors Aleksandar I. Zecevic and Dragoslav D. Siljak use their years of expertise within the keep an eye on box to additionally:
- Address the problems of large-scale structures as they relate to powerful regulate and linear matrix inequalities
- Discuss a brand new method of making use of commonplace LMI strategies to large-scale platforms, combining graphic-theoretic decomposition strategies with acceptable low-rank numerical approximations and dramatically lowering the computational effort
- Providing various examples and a large choice of purposes, starting from electrical strength structures and nonlinear circuits to mechanical difficulties and dynamic Boolean networks
Control of advanced platforms: Structural Constraints and Uncertainty will entice working towards engineers, researchers and scholars operating on top of things layout and different comparable components.
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Additional resources for Control of Complex Systems: Structural Constraints and Uncertainty
Sample text
Decentralized estimation and control with overlapping input, state and output decomposition. Automatica, 29, 511–516. ˇ Ikeda, M. and D. D. Siljak (1986). Overlapping decentralized control with input, state and output inclusion. Control Theory and Advanced Technology, 2, 155–172. , D. D. Siljak and D. E. White (1984). An inclusion principle for dynamic systems. IEEE Transactions on Automatic Control, 29, 244–249. Peponides, G. M. and P. K. Kokotovic (1983). Weak connections, time scales, and aggregation of nonlinear systems.
Ti = ⎢ ... . . ⎢ ⎢ 0 · · · −1 · · · ⎢ ⎢ . .. .. ⎣ .. . 67) 0 .. 0 .. 1 .. 0 ⎤ ··· 0 . ⎥ .. .. ⎥ ⎥ ··· 0 ⎥ ⎥ .. ⎥ .. . ⎥ ⎥ ··· 0 ⎥ ⎥ . ⎥ .. .. 68) and x1 = [x11 , . . , x1n ]T . 70) The gains obtained using the proposed LMI approach are optimal in the sense that they maximize the value of parameter α for a given bound on the gain norm. To see what this means in terms of transient stability, we evaluated the response of the IEEE 39 bus system (Pai, 1989) with 10 generators for a variety of disturbances.
5. LOW-RANK CORRECTIONS IN VEHICLE CONTROL z(t) Wi 45 Si xi (t) Kii Vi Vi x i (t) Fig. 5 Computation tasks for processor i In this scheme, processor i performs multiplications involving matrices Wi , Vi and Kii , which are of dimension mi × r, r × ni and ni × ni , respectively. 94) j=1 The only communication tasks involved are single-node gather and scatter operations, which are known to result in low overhead. If necessary, the front end processor can also periodically recompute matrices Wi , Vi and Kii , in response to changes in the system configuration.