This paper proposes a robust output feedback model predictive control (MPC) framework for constrained linear networked control systems (NCSs) subject to random packet dropouts and external disturbances. The proposed output feedback MPC scheme consists of a state observer that accommodates the random measurement loss and a state feedback model predictive controller that stabilizes the perturbed system. According to the proposed observer, the estimation error dynamics can be represented by a switched system. By developing a generalized robust positive invariant (GRPI) set under the switched system formulation, the estimation error can be confined in this invariant set, which serves as the explicit error bound of state estimation. Then, the GRPI set is utilized to tighten the state and input constraints in the MPC optimization problem to alleviate the effects of random packet dropouts and disturbances. As a result, the system can be stabilized by the proposed output feedback model predictive controller while both state and input constraints are fulfilled. Simulation results are provided to validate the effectiveness of the proposed method.

Data-driven innovation and optimization have become an important direction for the intelligent transformation of enterprises. Data processing tasks have been developed and orchestrated to extract data insights, creating direct or indirect data dependencies between tasks or between tasks and the presentation layer. Traditional ETL (Extract-Transformation-Load) solutions share data through persistent storage, which has certain performance bottlenecks in hybrid cloud and multisource data scenarios. In this paper, we propose MicroStream, a distributed data virtualization and caching middleware service. MicroStream shields the direct access of ETL tasks to the storage layer and converts batch access to the source database into microstream access. ETL jobs share data through the distributed in-memory caching of MicroStream. In resource-constrained scenarios, such a solution significantly improves the performance of data transformation while reducing the extra load that the transformation jobs imply on the source persistent layer. We present a detailed performance evaluation of MicroStream and show that its performance compares favorably with traditional database-oriented solutions.

This paper is concerned with the issue of bumpless transfer control for a class of hybrid stochastic systems with a stochastic switching rule. The switching rule is regarded as a hidden semi-Markov switching process by utilizing concepts of emission probability and semi-Markov kernel. In light of the mismatching of mode observation happening in the operating procedure of subsystem, comprehensive bumpless transfer constraints that not only work on the switching instants but also the sojourn process, are proposed. On the basis of the mean-square stability criteria and comprehensive bumpless transfer constraints, numerically testable conditions are established to obtain the stabilizing state-feedback controller with bumpless transfer performance by introducing some mathematical techniques. The theoretical results are testified by a switched RLC electromagnetic oscillation circuit to demonstrate the effectiveness of the designed control strategy.