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In this paper, we develop two robust moving horizon state observer (MHSO) algorithms which are capable of handling system non-linear uncertainties and physical state constraints. The algorithms employ open-loop and closed-loop prediction strategies to convert the design into multi-parameter quadratic programming (mp-QP), and also utilize the novel rewinding optimization to eliminate the conflict between open-loop prediction and closed-loop implementation. Based on the optimal solutions to mp-QP problems, MHSO is obtained by a series of offline linear/affline observation polices, and computational complexity is reduced dramatically. The convergence of observation errors, as one of challenges for robust MHSO, is also solved by introducing two auxiliary tuning parameters, the arrival weighting and the arrival observer gain. Finally, a simulation example demonstrates that our algorithms are practical and effective. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for diagonal dominance of large-scale systems via eigenstructure assignment. For a given large-scale system in general form, an equivalent descriptor system in the input–output decentralized form is defined. Sufficient conditions for diagonal dominance of the closed-loop system are introduced. These conditions are in terms of the isolated subsystems. Based on them, interactions between subsystems can be considered as external disturbances for each isolated subsystem. Then a previously proposed approach is used for disturbance attenuation via dynamical output compensators based on complete parametric eigenstructure assignment. By attenuating disturbances, closed-loop poles of the overall system are placed in a desirable region, by assigning the eigenstructure of each isolated subsystem appropriately. The presented algorithm alleviates the necessity of choosing a suitable frequency in designing a pre-compensator, as required by previous methods. The designed controller is in the decentralized form and plays the role of pre-compensator as well. An illustrative example is given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]