This paper investigates the output feedback model predictive control (OFMPC) for Takagi-Sugeno fuzzy networked control systems with bounded disturbance, where data quantization and data loss occur simultaneously. The quantization error is treated as sector bound uncertainties by using the sector bound approach and the data loss process is modeled as a time-homogeneous Markov chain. Invoking S-procedure and the notion of quadratic boundedness which can specify closed-loop stability for system with disturbance, the state observer is offline designed and the networked output feedback model predictive controller is provided which explicitly considers the satisfaction of input constraints. Two online synthesis algorithms of OFMPC are presented, one parameterizing the infinite horizon control moves into a single feedback law, the other into one free control move followed by the single feedback law based on the state observer. A new formula is introduced to refresh the ellipsoidal bound of estimation error which can guarantee the recursive feasibility of optimization problem. An example is given to demonstrate the effectiveness of the proposed new design techniques.

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91smart", so the network intelligence is always deployed at the terminal of network edge. However, with the rapid development of network technology, a large number of new access technologies come out, as well as new heterogeneous network and new applications. These new technologies and applications not only promote the progress of the entire field of communication, but also make the traditional Internet structure face enormous challenges. In addition, the users who wants to establish a set of IT system has to purchase the infrastructure such as broadband, hardware, and the appropriate software. Moreover, users also needs a professional team of workers who can maintenance the IT systems. Therefore, a large number of hardware, software, personnel and maintenance will bring a high expenditure to users.The emergence of cloud computing not only makes a good integration of existing heterogeneous network resources, but also provides a large number of new services easily[1]. So, users do not need to worry about the high costs of hardware and workers for the completion of their business any more. And task scheduling is inevitable in the implementation of cloud computing, which affects the utilization of resources and the overall effectiveness directly. In the cloud computing system, cloud users only care about whether their requirements can be guaranteed, whether their interests can be achieved, and whether the service is convenient; providers of cloud computing also hope to meet Abstract: Resource scheduling algorithm for ForCES (Forwarding and Control Element Separation) networks need to meet the flexibility, programmability and scalability of node resources. DBC (Deadline Budget Constrain) algorithm relies on users select cost or time priority, then scheduling to meet the requirements of users. However, this priority strategy of users is relatively simple, and cannot adapt to dynamic change of resources, it is inevitable to reduce the QoS. In order to improve QoS, we refer to the economic model and resource scheduling model of cloud computing, use SAL (Service Level Agreement) as pricing strategy, on the basis of DBC algorithm, propose an DABP (Deadline And Budget Priority based on DBC) algorithm for ForCES networks, DABP combines both budget and time priority to scheduling. In simulation and test, we compare the task finish time and cost of DABP algorithm with DP (Deadline Priority) algorithm and BP (Budget Priority) algorithm, the analysis results show that DABP algorithm make the task complete with less cost within deadline, benifical to load balancing of ForCES networks.