Iterative learning model predictive control (ILMPC) has become an excellent data-driven intelligent control strategy for digitized batch manufacturing, featured by the progressive improvement of tracking performance along trials, and the persistent rejection of stochastic disturbance along time. The point-to-point learning mechanism of existing ILMPC generally relies on identical operating conditions along trials to guarantee the integrity and accuracy of historical data. However, the variations of production requirements usually lead to trial-varying operating references and durations, resulting in incomplete and inaccurate historical information for the iterative learning of subsequent trials. To promote the adaptability and flexibility of ILMPCs with unconformable prior information, a data-driven self-modification scheme is originally embedded into ILMPC in this article to transfer the prior knowledge contained in the historical operating data into the form consistent with the condition of each current trial. The control actions are imitated along trials by an adaptive deep neural network (DNN), which is then utilized to generate reference control signals for iterative learning in each trial. For attenuating the influence of the considerable DNN approximation error in early trials with limited data accumulation, the 2-D optimization of ILMPC is performed under a tube control frame to ensure the time-domain bounded stability. Based on the intrinsic recursive feasibility and the guaranteed time-domain stability, the iteration-domain bounded convergence of the developed ILMPC system is theoretically validated. Simulations on the nonlinear injection molding process verify the superiority of the proposed method in adapting to significant changes in operating reference and duration.

The stochastic uncertainty of wind speed presents a significant challenge for ensuring reliable power control in wind energy conversion system (WECS). Stochastic model predictive control (SMPC) emerges as a promising strategy for WECS, leveraging its capacity to effectively handle wind uncertainties through probabilistic descriptions. However, the nonlinear dynamics of wind turbine systems impede the advancement of SMPC in WECS. To address this issue, an offset-free SMPC approach for WECS is proposed, aiming to achieve offset-free rated power tracking, while ensuring the security of WECS during operation in the full load region. A Luenberger observer is employed to estimate the model-plant mismatch introduced by the linearization process, while a tube-based SMPC is deployed to handle stochastic wind speed disturbances for fulfilling probabilistic constraints. Simulation results, including experiments conducted on fatigue, aerodynamics, structures, and turbulence (FAST), are presented to validate the effectiveness of the proposed strategy under different scenarios.

Iterative learning model predictive control (ILMPC) has been recognized as an excellent batch process control strategy for progressively improving tracking performance along trials. However, as a typical learning-based control method, ILMPC generally requires the strict identity of trial lengths to implement 2-D receding horizon optimization. The randomly varying trial lengths extensively existing in practice can result in the insufficiency of learning prior information, and even the suspension of control update. Regarding this issue, this article embeds a novel prediction-based modification mechanism into ILMPC, to adjust the process data of each trial into the same length by compensating the data of absent running periods with the predictive sequences at the end point. Under this modification scheme, it is proved that the convergence of the classical ILMPC is guaranteed by an inequality condition relative with the probability distribution of trial lengths. Considering the practical batch process with complex nonlinearity, a 2-D neural-network predictive model with parameter adaptability along trials is established to generate highly matched compensation data for the prediction-based modification. To best utilize the real process information of multiple past trials while guaranteeing the learning priority of the latest trials, an event-based switching learning structure is proposed in ILMPC to determine different learning orders according to the probability event with respect to the trial length variation direction. The convergence of the nonlinear event-based switching ILMPC system is analyzed theoretically under two situations divided by the switching condition. The simulations on a numerical example and the injection molding process verify the superiority of the proposed control methods.

Iterative learning model-predictive control (ILMPC) is very popular in controlling the batch process since it possesses not only the learning ability along batches but also the strong time-domain tracking properties. However, for a fast batch process with strong nonlinear dynamics, the application of the ILMPC is challenging due to the difficulty in balancing the computational efficiency and tracking accuracy. In this article, an efficient iterative learning predictive functional control (ILPFC) is proposed. The original nonlinear system is linearized along the reference trajectory to derive a 2-D tracking-error predictive model. The linearization error is compensated by utilizing the Lipschitz condition so that the objective function can be formulated with the upper bound of the actual tracking error. For enhancing control efficiency, predictive functional control (PFC) is applied in the time domain, which reduces the dimension of the decision variable in order to effectively cut down the computational burden. The stability and convergence of this ILPFC with terminal constraint are analyzed theoretically. Simulations on an unmanned ground vehicle and a typical fast batch reactor verify the effectiveness of the proposed control algorithm.

Edge computing promises higher bandwidth and lower latency to end-users. However, edge servers usually have limited computing resources and are geographically distributed over the edge. This imposes new challenges for efficient system monitoring and control of edge servers.In this paper, we propose EdgeVMI, a framework to monitor and control services running on edge servers with lightweight virtual machine introspection(VMI). The key of our technique is to run the monitor in a lightweight virtual machine which can leverage hardware events for monitoring memory read and writes. In addition, the small binary size and memory footprints of the monitor could reduce the start/stop time of service, the runtime overhead, as well as the deployment efforts.Inspired by unikernels, we build our monitor with only the necessary system modules, libraries, and functionalities of a specific monitor task. To reduce the security risk of the monitoring behavior, we separate the monitor into two isolated modules: one acts as a sensor to collect security information and another acts as an actuator to conduct control commands. Our evaluation shows the effectiveness and the efficiency of the monitoring system, with an average performance overhead of 2.7%.

Iterative learning model predictive control (ILMPC) has been recognized as an effective approach to realize high-precision tracking for batch processes with repetitive nature because of its excellent learning ability and closed-loop stability property. However, as a model-based strategy, ILMPC suffers from the unavailability of accurate first principal model in many complex nonlinear batch systems. On account of the abundant process data, nonlinear dynamics of batch systems can be identified precisely along the trials by neural network (NN), making it enforceable to design a data-driven ILMPC. In this article, by using a control-affine feedforward neural network (CAFNN), the features in the process data of the former batch are extracted to form a nonlinear affine model for the controller design in the current batch. Based on the CAFNN model, the ILMPC is formulated in a tube framework to attenuate the influence of modeling errors and track the reference trajectory with sustained accuracy. Due to the control-affine structure, the gradients of the objective function can be analytically computed offline, so as to improve the online computational efficiency and optimization feasibility of the tube ILMPC. The robust stability and the convergence of the data-driven ILMPC system are analyzed theoretically. The simulation on a typical batch reactor verifies the effectiveness of the proposed control method.

Model predictive iterative learning control (MPILC) is a popular approach to control batch systems with repetitive nature, as it is capable of tracking the plant reference trajectory with high accuracy and guaranteed closed-loop stability. However, varying reference trajectory often happens in batch processes, so that the tracking performance of a general MPILC can be deteriorated. With the reference variation being treated as bounded disturbance, this paper incorporates H∞control with MPILC to constitute a robust MPILC (RMPILC), so as to restrain the fluctuation of tracking error caused by the varying reference. This RMPILC adopts the linear parameter varying (LPV) model to represent the dynamic property of the nonlinear system. It then solves the input vector of controlled system by optimizing the tracking performance objective function with constraints in the form of linear matrix inequalities. The robust stability and convergence condition of RMPILC are analyzed. The effectiveness of proposed algorithm is verified thorough the simulations on a numerical example, and also a continuous stirred tank reactor (CSTR) system.

Many edge computing systems rely on virtual machines (VMs) to deliver their services. It is challenging, however, to deploy the virtualization mechanisms on edge computing hardware infrastructures. In this paper, we introduce the engineering and research trends of achieving efficient VM management in edge computing. We elaborate on: 1) the virtualization frameworks for edge computing developed in both the industry and the academia; 2) the virtualization techniques tailored for edge computing; 3) the placement and scheduling algorithms optimized for edge computing; and 4) the research problems in security related to virtualization of edge computing.

Mobile users across edge networks require seamless migration of offloading services. Edge computing platforms must smoothly support these service transfers and keep pace with user movements around the network. However, live migration of offloading services in the wide area network poses significant service handoff challenges in the edge computing environment. In this paper, we propose an edge computing platform architecture which supports seamless migration of offloading services while also keeping the moving mobile user “in service” with its nearest edge server. We identify a critical problem in the state-of-the-art tool for Docker container migration. Based on our systematic study of the Docker container storage system, we propose to leverage the layered nature of the storage system to reduce file system synchronization overhead, without dependence on the distributed file system. In contrast to the state-of-the-art service handoff method in the edge environment, our system yields a 80 percent (56 percent) reduction in handoff time under 5 Mbps (20 Mbps) network bandwidth conditions.

A microgrid is a distributed networked generation system, which can effectively integrate various sources of distributed generation, especially renewable energy sources into the information network. The standalone wind/solar/battery power system is a typical standalone microgrid, in which the wind and solar power generations are the intermittent systems with complex dynamics and multiconstraints. Coordinated optimization between the wind power and solar power generations can effectively meet the load demand, reduce wear and tear of generating units, prolong the lifetime and, thus, guarantee the safety of the power grid. Regarding the large-scale, geographically dispersed standalone wind/solar/battery power generation system, this paper constituted a hierarchical distributed model predictive control (HDMPC). In this HDMPC, the upper layer utilizes an iterative distributed control strategy to realize the coordination of the power dispatch. It thus reaches the economic object, e.g., reducing the torsional shaft torque transmitted to gearbox in wind turbine system. The lower layer utilizes the supervisory predictive control to realize both the economic and tracking property. Under this hierarchical structure, the back-calculation from the lower control layer to the upper layer is utilized to keep the consistency of constraints. Through coordinated optimization among the subsystems, the proposed HDMPC realizes the plug and play of distributed energy. The simulation and the experiment validate the advantages of the proposed method in that it can realize the reliability, high efficiency, flexibility, and interactivity for the microgrid control.