Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems

In this paper, we provide expressions for the secrecy outage probability (SOP) for suboptimal and optimal opportunistic scheduling schemes in a reconfigurable intelligent surface (RIS) aided single antenna system with multiple eavesdroppers in approximate closed form. A suboptimal scheduling (SS) scheme is analyzed, which is used when the channel state information (CSI) of the eavesdropping links is unavailable, and the optimal scheduling (OS) scheme is also analyzed, which is used when the global CSI is available. For each scheme, we provide a simplified expression for the SOP in the high signal-to-noise ratio (SNR) regime to demonstrate its behavior as a function of the key system parameters. At high SNR, the SOP saturates to a constant level which decreases exponentially with the number of RIS elements in the SS scheme and with the product of the number of RIS elements and the number of users in the OS scheme. We also show that the derived SOP of the SS scheme can directly provide the SOP for the best antenna-user pair scheduling scheme in a multiple antenna system. We compare the performance of the opportunistic user scheduling schemes with that of a non-orthogonal multiple access (NOMA) based scheduling scheme which chooses a pair of users in each time slot for scheduling and we show that the opportunistic schemes outperform the NOMA-based scheme. We also derive a closed-form expression for the SOP of a decode-and-forward (DF) relay-aided scheduling scheme in order to compare it with that of the RIS-aided system. It is found that the RIS-aided system outperforms the relay-aided system when the number of RIS elements is sufficiently large. An increased number of RIS elements is required to outperform the relay-aided system at higher operating frequencies.

Secrecy outage analysis of multi-user multi-eavesdropper cellular networks in the face of cochannel interference

In this article, a kind of predictive control (PC) for permanent magnet synchronous motor (PMSM) rotor position control is introduced, and more stable and rapid performances are achieved comparing with other PCs. Nonlinear predictive control (NLPC) is shown to outperform the linear in terms of performance for nonlinear controlled plants because of containing nonlinear part and longer prediction horizons. A novel direct speed NLPC scheme for PMSM is presented based on this strategy. The response and reference signals are predicted with Taylor series expansion to guarantee the accuracy of the system. Prediction horizons are selected according to Shannon with its maximum value to eliminate steady-state error and delay between actual and reference signals. Furthermore, the proposed method is designed and analyzed in detail, and tested by simulations and experiments. Results with respect to the rotor position trace tracking, control performance, and weighting factors sensitivity demonstrate the efficiency of the proposed method.

Markovian jumping systems have arisen naturally in the mathematical modeling of phenomena spanning disciplines in the social sciences, natural sciences, and engineering. This kind of stochastic dynamical systems can be employed to model the dynamics when parameters are subject to random abrupt changes due to sudden environment changes, subsystem switching, system noises, executor faults, and so forth. Much attention has been given to modeling, optimization, and real applications of such stochastic dynamical systems in the literature in recent years. As the advanced control and optimization will provide a basis for the design and application of such stochastic systems, these advanced techniques would result in substantial and sustainable benefits. The accepted papers in this special issue include stochastic stability, stabilization, stochastic control optimization, system modeling and identification methods, predictive control, signal processing, robust filtering, multiagent systems, networked control systems, time-delayed systems, neural networks, the Takagi-Sugeno fuzzy systems, simulated annealing, and fault detection methods.

As industrial plants embrace modern technologies, such as edge computing and wireless networks, industrial control systems have evolved into multi-tier cyber-physical systems. While traditional local controllers enjoy reliable connectivity to sensors/actuators, they suffer from the limited computation capacity of embedded devices. In contrast, edge servers introduce more computation resources connected to sensors/actuators through wireless networks. Offloading control functions to edge servers presents new opportunities to enhance control performance but also poses critical challenges. As wireless networks have limited bandwidth and varying reliability, it is important to optimize control performance by dynamically offloading a subset of the control functions to edge servers under the bandwidth constraint. Furthermore, the selection of offloaded control functions depends on both the cyber (wireless) and physical states of the wireless control systems. In this paper, we tackle the problem of optimizing the control performance of multiple control loops through dynamic edge offloading. We establish a data-driven model to predict the control performance of each feedback control loop based on its cyber-physical states. We then develop a dynamic edge offloading approach to optimize the overall control performance of a system with multiple feedback control loops while guaranteeing their stability under fluctuating cyber-physical conditions. Finally, we demonstrate the efficacy of the data-driven model and offloading approach in case studies comprising simulations of up to twenty industrial robots.

In this paper, a learning-based predictive control (LPC) scheme is proposed for adaptive optimal control of discrete-time nonlinear systems under stochastic disturbances. The proposed LPC scheme is different from conventional model predictive control (MPC), which uses open-loop optimization or simplified closed-loop optimal control techniques in each horizon. In LPC, the control task in each horizon is formulated as a closed-loop nonlinear optimal control problem and a finite-horizon iterative reinforcement learning (RL) algorithm is developed to obtain the closed-loop optimal/suboptimal solutions. Therefore, in LPC, RL and adaptive dynamic programming (ADP) are used as a new class of closed-loop learning-based optimization techniques for nonlinear predictive control with stochastic disturbances. Moreover, LPC also decomposes the infinite-horizon optimal control problem in previous RL and ADP methods into a series of finite horizon problems, so that the computational costs are reduced and the learning efficiency can be improved. Convergence of the finite-horizon iterative RL algorithm in each prediction horizon and the Lyapunov stability of the closed-loop control system are proved. Moreover, by using successive policy updates between adjoint time horizons, LPC also has lower computational costs than conventional MPC which has independent optimization procedures between two different prediction horizons. Simulation results illustrate that compared with conventional nonlinear MPC as well as ADP, the proposed LPC scheme can obtain a better performance both in terms of policy optimality and computational efficiency.

In this paper, we study scheduling schemes for a two-user two-way relaying system. Two transmission modes are considered, i.e., point-to-point direct transmission and two-way amplify-and-forward (AF) relaying. An optimal scheduling method that opportunistically selects the best transmission mode for each user with instantaneous channel state information (CSI) is proposed to minimize the sum bit-error-rate (BER). However, for optimal scheduling, the scheduler requires the knowledge of all the channels. To reduce the feedback information of CSI, we also propose a suboptimal scheduling scheme that only the CSI of the two direct links is explored to select the transmission mode. Simulation results show that both the optimal and suboptimal schemes outperform the fixed direct transmission and fixed two-way relayed transmission, and the performance gap between the optimal and suboptimal scheduling scheme is small. These results show that the suboptimal scheduling scheme provides a good trade-off between implementation complexity and system performance.

Industrial automation is embracing wireless sensor-actuator networks (WSANs). Despite the success of WSANs for monitoring applications, feedback control poses significant challenges due to data loss and stringent energy constraints in WSANs. Holistic control adopts a cyber-physical system approach to overcome the challenges by orchestrating network reconfiguration and process control at run time. Fundamentally, it leverages self-awareness across control and wireless boundaries to enhance the resiliency of wireless control systems. In this article, we explore efficient holistic control designs to maintain control performance while reducing the communication cost. The contributions of this work are five-fold: (1) We introduce a holistic control architecture that integrates Low-power Wireless Bus (LWB) and two control strategies, rate adaptation and self-triggered control ; (2) We present heuristics-based and optimal rate selection algorithms for rate adaptation; (3) We design novel network adaptation mechanisms to support rate adaptation and self-triggered control in a multi-hop WSAN; (4) We build WCPS-RT, a real-time network-in-the-loop simulator that integrates MATLAB/Simulink and a physical WSAN testbed to evaluate wireless control systems; (5) We empirically explore the tradeoff between communication cost and control performance in holistic control approaches. Our studies show that rate adaptation and self-triggered control offer advantages in control performance and energy efficiency, respectively, in normal operating conditions. The advantage in energy efficiency of self-triggered control, however, may diminish under harsh physical and wireless conditions due to the cost of recovering from data loss and physical disturbances.

Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling

For many old buildings in the world, due to the legacy devices problem, it is hard to supply appropriate energy for them. In order to reduce the energy consumption of buildings under the premise of satisfying user requirements, we use software control systems whose core part is the scheduling strategy, to reconstruct them. It is time consuming to choose a good scheduling strategy due to many uncertain factors, among which user actions are of the most influence. In this paper, we propose an Support Vector Machine (SVM) based approach to explore the relation between user action and the best scheduling strategy of a control system. The main contributions include: (1) obtaining the sample set by collecting data at the model level using Statistical Model Checking (SMC) based method; (2) using SVM algorithm to learn the relation model between user actions and the best scheduling strategies; and (3) applying the relation model to predict a best scheduling strategy. Finally a real case study is conducted showing the efficiency of our approach. Keywords—Energy Aware Building; User Actions; Scheduling Strategy; Support Vector Machine; Statistical Model Checking.

This paper aims at developing near optimal traffic signal control for multi-intersections in city. As a new optimization technique, adaptive dynamic programming (ADP) combines concepts of reinforcement learning and dynamic programming. ADP could learn continually from experience to achieve a near optimal control policy under varying conditions. However, without the cooperation among adjacent intersections, the near optimal control for each individual intersection can not guarantee a larger traffic area composing several intersections to be near optimal. This paper presents a new signal control method based on a model-free action-dependent ADP (ADHDP). This method can be used for cooperative control of multiple intersections. In every intersection, an ADHDP signal controller is adopted to adjust signal time according to an integrated unity parameter. The unity parameter is designed to consider not only the control performance in local intersection but also those in the neighbor intersections. Thus the designed controllers could achieve a set of near optimal control police for multi-intersections in a long run. Simulation results show that the trained controller achieves shorter average vehicular delay.

The success of the smart grid majorly depends on the advanced communication architectures. An advanced smart grid network should satisfy the future demands of the electric systems in terms of reliability and latency. The latest 4th-generation (4G) wireless technology, the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE), is a promising choice for smart grid wide area networks (WAN), due to its higher data rates, lower latency and larger coverage. However, LTE is not a dedicated technology invented for smart grid, and it does not provide Quality of Service (QoS) guarantee to the smart grid applications. In this paper, we propose an optimal LTE uplink scheduling scheme to provide scheduling timeguarantee at the LTE base station for different class of traffic, with a minimal number of total resource blocks. A lightweight heuristic algorithm is proposed to obtain the optimal allocation of resource blocks for each class of traffic. In the simulation, we compare the proposed optimal scheduling scheme and two existing scheduling schemes, the Large-Metric-First scheduling scheme and the Guaranteed Bit Rate (GBR) /Non-GBR scheduling scheme. The comparison results demonstrate that the proposed optimal scheduling can use less resource blocks to satisfy the scheduling time requirements than the other two existing scheduling schemes.

The huge parameter scale and data access of existing CNN models, as well as the diversity of computing modes of different CNN models or different layers in the same model, lead to the low overall computing efficiency of computing systems that can only support a single or fixed mapping mode and on-chip data cache. Using the strategy of hardware and software coordination design, we combine the adaptive memory access optimization mechanism based on deep reinforcement learning with the dynamic partition method of on-chip elastic cache, and automatically searches the optimal cyclic scheduling strategy for different CNN operation layers according to the relevant parameters of the storage structure in the reconfigurable CNN processor. By designing the on-chip interconnection structure, address mapping logic and dynamic partition method, elastic storage can dynamically partition the address mapping space according to different scheduling strategies during accelerator operation. In the CNN processor architecture based on Eyeriss and TPU architectures, compared with the optimal scheduling strategy, the proposed scheduling strategy and on-chip cache partitioning method can improve the energy efficiency of the two reconfigurable CNN processors by almost 3 times and 4 times respectively. Moreover, compared with the fixed capacity dual cache structure, the elastic storage partitioning method proposed in this paper can reduce the power consumption by 30.28% and 18.43%, respectively, using the same scheduling strategy. Compared with the latest research based on FPGA platform, the reconfigurable CNN processor in this paper can improve the computational efficiency and computational energy efficiency by almost 10 times and 2 times respectively.

Recent years have witnessed adoption of wireless sensor-actuator networks (WSANs) in process control. Many real-world process control systems must handle various emergency alarms under stringent timing constraints in addition to regular control loops. However, despite considerable theoretical results on wireless control, the problem of incorporating emergency alarms in wireless control has received little attention. This paper presents, to the best of our knowledge, the first systematic approach to incorporate emergency alarms into wireless process control. The challenge in emergency communication lies in the fact that emergencies occur occasionally, but must be delivered within their deadlines when they occur. The contributions of this work are three-fold: (1) we propose efficient real-time emergency communication protocols based on slot stealing and event-based communication; (2) we build an open-source WirelessHART protocol stack in the Wireless Cyber-Physical Simulator (WCPS) for holistic simulations of wireless control systems; (3) we conduct systematic studies on a coupled water tank system controlled over a 6-hop 21-node WSAN. Our results demonstrate our real-time emergency communication approach enables timely emergency handling, while allowing regular feedback control loops to effectively share resources in WSANs during normal operations.

Wireless sensor-actuator networks (WSANs) are being adopted in process industries because of their advantages in lowering deployment and maintenance costs. While there has been significant theoretical advancement in networked control design, only limited empirical results that combine control design with realistic WSAN standards exist. This paper presents a cyber-physical case study on a wireless process control system that integrates state-of-the-art network control design and a WSAN based on the WirelessHART standard. The case study systematically explores the interactions between wireless routing and control design in the process control plant. The network supports alternative routing strategies, including single-path source routing and multi-path graph routing. To mitigate the effect of data loss in the WSAN, the control design integrates an observer based on an Extended Kalman Filter with a model predictive controller and an actuator buffer of recent control inputs. We observe that sensing and actuation can have different levels of resilience to packet loss under this network control design. We then propose a flexible routing approach where the routing strategy for sensing and actuation can be configured separately. Finally, we show that an asymmetric routing configuration with different routing strategies for sensing and actuation can effectively improve control performance under significant packet loss. Our results highlight the importance of co- joining the design of wireless network protocols and control in wireless control systems.