Signal Temporal Logic (STL) is widely used for specifying complex time-dependent behaviors in cyber-physical systems (CPSs), particularly in safety-critical domains. However, fault diagnosis (FD) and fault tolerant control (FTC) under nested STL (NSTL) specifications remain challenging, especially for nonlinear systems. This paper proposes a collaborative design (CoD) framework that jointly integrates FD and FTC under NSTL constraints to enhance detection accuracy and ensure robust system performance. First, a fault detection observer is developed by constructing fault tolerant feasible sets that can predict whether ongoing system trajectories satisfy NSTL specifications. To address the feasibility issue in real-time control synthesis, we introduce the concept of fault tolerant control with recursive feasibility (FTCRF), enabling the controller to maintain constraint satisfaction and system stability even under faults. A model predictive control scheme guided by control barrier functions (CBFs) ensures safe trajectory tracking within specified bounds. Simulation studies on single integrator and unicycle models demonstrate the effectiveness of the proposed method in accurately detecting faults and maintaining task satisfaction under NSTL constraints.

Socio-psychological studies have identified a common phenomenon where an individual’s public actions do not necessarily coincide with their private opinions, yet most existing models fail to capture the dynamic interplay between these two aspects. To bridge this gap, we propose a novel agent-based modeling framework that integrates opinion dynamics with a decision-making mechanism. More precisely, our framework generalizes the classical Hegselmann-Krause (HK) model by combining it with a utility maximization problem. Preliminary results from our model demonstrate that the degree of opinion-action divergence within a population can be effectively controlled by adjusting two key parameters that reflect agents’ personality traits, while the presence of social network amplifies the divergence. In addition, we study the social diffusion process by introducing a small number of committed agents into the model, and identify three key outcomes: adoption of innovation, rejection of innovation, and the enforcement of unpopular norms, consistent with findings in socio-psychological literature. The strong relevance of the results to real-world phenomena highlights our framework’s potential for future applications in understanding and predicting complex social behaviors.

The privacy-preserving control problem in discrete-event systems at the supervisory control layer is the central focus of this article. The key objective is to cosynthesize an edit function and a supervisor, working together to achieve the following goals: first, ensuring data opacity to prevent external intruders from deducing the system’s secret, second, ensuring the system performance adheres to safety and nonblockingness specification, and third, preserving the covert nature of the edit function, creating uncertainty about its presence to external intruders. By transforming this cosynthesis problem into a distributed supervisor synthesis problem in the Ramadge–Wonham supervisory control framework, this article introduces two heuristic synthesis approaches to incrementally cosynthesize an edit function and a supervisor. The effectiveness of the proposed approaches is demonstrated via a running example on location privacy.

Distributed cooperative tracking control has emerged as a pivotal research focus in multi-agent systems, particularly for platoon control applications where its decentralized architecture offers significant advantages over centralized approaches. However, the direct exchange of sensitive data between agents raises critical privacy risks, hindering its broader adoption across safety-critical applications. This paper presents a privacy-preserving cooperative tracking framework that rigorously maintains bounded coupling errors, which is a crucial requirement for collision avoidance in vehicular platoons. Departing from conventional methods that compromise privacy through explicit state sharing for error mitigation, our proposed algorithm achieves dual objectives: maintaining prescribed error constraints while preserving agent state confidentiality in directed communication networks with time-varying interaction weights. We establish sufficient conditions for achieving cooperative-tracking consensus with predefined error constraints and characterize the quantitative relationship between the asymptotic convergence rate and control gain parameters. Furthermore, we analyse the privacy-preserving performance against internal and external adversaries, demonstrating that the probability of an adversary inferring states within a finite neighborhood of ground-truth values can be rendered arbitrarily small, even while adversaries retain access to identical communication data streams. This extends classical initial-state privacy to the entire operational timeline under time-varying directed topologies. Numerical examples including an application of cooperative adaptive cruise control demonstrate our proposed algorithm’s efficacy.

This paper presents an uncertainty-aware exploration framework for cooperative underwater operations using multiple unmanned underwater vehicles (UUVs). The proposed framework leverages prior environmental information to iteratively integrate task planning, task assignment, and prior belief updating, enabling efficient exploration in unknown underwater environments. An interest area selection strategy is proposed to balance the exploration of uncharted regions and the exploitation of areas with high target likelihood. To optimize interest area task allocation, a simultaneous auctionbased mechanism is developed that assigns each interest area to the most suitable UUV by maxing potential information gain while minimizing operational costs. Additionally, to address the computational constraints of UUV systems, a Sparse Gaussian Process (SGP) with variationally optimized inducing points is employed, enabling rapid and accurate fusion of real-time observations with prior environmental information. This approach facilitates dynamic updates of the probabilistic environment representation and interest point selection without compromising accuracy. Experimental results in the HoloOcean simulator demonstrate the framework’s effectiveness in refining the probabilistic environment representation, achieving efficient exploration and accurate target detection in complex underwater scenarios. The results highlight the framework’s capability to dynamically adapt to environmental uncertainties, showcasing its potential for underwater exploration applications.

Accurate relative state observation of Unmanned Underwater Vehicles (UUVs) for tracking uncooperative targets remains a significant challenge due to the absence of GPS, complex underwater dynamics, and sensor limitations. Existing localization approaches rely on either global positioning infrastructure or multi-UUV collaboration, both of which are impractical for a single UUV operating in large or unknown environments. To address this, we propose a novel persistent relative 6D state estimation framework that enables a single UUV to estimate its relative motion to a non-cooperative target using only successive noisy range measurements from two monostatic sonar sensors. Our key contribution is an observability-enhanced attitude control strategy, which optimally adjusts the UUV’s orientation to improve the observability of relative state estimation using a Kalman filter, effectively mitigating the impact of sensor noise and drift accumulation. Additionally, we introduce a rigorously proven Lyapunov-based tracking control strategy that guarantees long-term stability by ensuring that the UUV maintains an optimal measurement range, preventing localization errors from diverging over time. Through theoretical analysis and simulations, we demonstrate that our method significantly improves 6D relative state estimation accuracy and robustness compared to conventional approaches. This work provides a scalable, infrastructure-free solution for UUVs tracking uncooperative targets underwater.

Commercial UAVs are an emerging security threat as they are capable of carrying hazardous payloads or disrupting air traffic. To counter UAVs, we introduce an autonomous 3D target encirclement and interception strategy. Unlike traditional ground-guided systems, this strategy employs autonomous drones to track and engage non-cooperative hostile UAVs, which is effective in non-line-of-sight conditions, GPS denial, and radar jamming, where conventional detection and neutralization from ground guidance fail. Using two noisy real-time distances measured by drones, guardian drones estimate the relative position from their own to the target using observation and velocity compensation methods, based on anti-synchronization (AS) and an X−Y circular motion combined with vertical jitter. An encirclement control mechanism is proposed to enable UAVs to adaptively transition from encircling and protecting a target to encircling and monitoring a hostile target. Upon breaching a warning threshold, the UAVs may even employ a suicide attack to neutralize the hostile target. We validate this strategy through real-world UAV experiments and simulated analysis in MATLAB, demonstrating its effectiveness in detecting, encircling, and intercepting hostile drones. More details: https://youtu.be/5eHW56lPVto.

Aiming to address the stealthy cyber attacks faced by multiarea interconnected power systems, this article proposes a new decentralized resilient predictive load frequency control (LFC) scheme, which has several prominent features, such as privacy preservation, active attack detection, network blocking defense, and attack tolerance. Compared with the relevant studies in the literature, the novelty of these features is specifically demonstrated by: 1) a dynamic scaling and masking method is constructed to achieve the real-time dynamic protection of network transmission data, which not only enables a closed-loop cyber privacy preservation against eavesdropping attacks, but also guarantees the effectiveness of active detection of attack appearance and disappearance; 2) a scaled one-step-ahead predictive interpolation control strategy is further proposed to achieve dynamic privacy preservation of control algorithm, and to apply a check-before-use mechanism to avoid the false data injected by the stealthy attacks to be loaded into the actuator; and 3) an active attack defense method with fail-safe feature is constructed to block the attacks from penetrating the actuator from the input transmission channels, while providing some finite-time open-loop control capability to suboptimally regulate the LFC before the attacks disappear. A case study of a three-area power system is given to validate the effectiveness of the proposed LFC method.

This paper investigates the robust consensus scheme for a specific type of leader-follower multi-agent systems (MASs) in the context of a human-cyber-physical interactive system. The system encounters frequent communication challenges characterized by packet loss and data leakage. To address these challenges, the paper proposes a secure decentralized scheme that leverages game theory to determine optimal policies in a zero-sum game. The scheme utilizes the adaptive dynamic programming (ADP) method, which involves a decentralized iterative process. The paper demonstrates that the generated sequence exhibits exponential convergence and provides the maximum iteration number based on the convergence error. To mitigate data leakage among players, the scheme incorporates a secure operation that involves encrypted data. This integration seamlessly combines data conversion and encryption-decryption into decentralized computation. Finally, the paper presents a simulation example to validate the efficacy of the consensus scheme.

The wind energy, as a kind of renewable energy resources, has the potential to replace traditional fossil fuels. However, its intermittent power output can incur frequency instability due to the instantaneous unbalance between power generation and load demand. To smooth the penetration of wind energy, this paper presents a robust cooperative load frequency control (LFC) strategy for multi-area power systems, which is a hierarchical control approach. For the low-level wind turbine control, this paper adopts model predictive control (MPC) method to achieve the rated wind power tracking. In the meantime, an improved event-triggered scheme (ETS) considering multiple historic released signals is employed to relieve the computational burden of MPC. For the high-level cooperative LFC, this paper incorporates the robust performance index in the control synthesis to suppress the impact of intermittent wind power on frequency stability. In addition, to address the underlying shift of the steady-state operating point caused by the intermittent wind power supply, this paper improves the commonly used small-signal LFC model by adding an uncertain matrix, which reasonably explains the possible change of system parameters and extends the applicability of the traditional LFC model. Simulations are done on a four-area power system, and the results verify the efficacy of the presented event-triggered scheme and the robust cooperative LFC approach.Note to Practitioners—To promote the penetration of wind energy into power systems, this work explores a robust cooperative LFC approach under multi-agent structure to ensure the stability of the system, aiming at extending the applicability of existing approaches. The proposed approach is hierarchical. At the rated wind power tracking level, the MPC is employed to handle constraints associated with actuating devices, such as heterogeneous convertors. Simultaneously, an improved ETS considering multiple historic triggered signals is integrated in the MPC to reduce the computational burden. At the power system level, the robust performance index is incorporated in the control design to smooth the impacts of intermittent wind power on frequency stability. Additionally, the study accounts for the potential shift of the steady-state operating point and improves the traditional small-signal LFC model by adding an uncertain matrix, which can better explain the variation of system parameters and is more applicable in practical power system engineering. Simulation results demonstrate that the proposed robust cooperative LFC approach can effectively maintain the system frequency within the admissible range under the high penetration of wind energy, whereas the traditional PI controller falls short in this regard.

This article designs a self-triggered adaptive dynamic programming (STADP) algorithm combined with the model predictive control (MPC) mechanism to address the approximate optimal control of a nonlinear system with terminal state constraints. The MPC mechanism transforms the problem into a series of subproblems and then reduces the number of subproblems through the self-triggered mechanism (STM), thereby reducing the amount of computation. The MPC-based STADP (MSTADP) algorithm employs neural networks (NNs) to construct three key modules: the model module, the critic module, and the action module. These modules enable the identification of the unknown system, approximation of the cost function, determination of the terminal penalty, and estimation of the optimal control strategy set. Furthermore, a relaxation factor is incorporated to modulate the convergence rate of the system states online. Finally, experimental validation on two distinct systems demonstrates the algorithm’s effectiveness, and comparative experiments against the traditional HDP algorithm are performed to highlight the superiority of the proposed approach.

In this article, we investigate the problem of distributed fault detection for a class of cyber-physical system whose physical layer consists of numerous subsystems, each modeled as a linear discrete-time system. Considering the influence of process noise and measurement noise, the state estimation of each subsystem is completed using a distributed Kalman filter (DKF), in which the one-step prediction is corrected not only by the local innovation but also by the measurement errors of the neighbors at the previous step. Leveraging the DKF, a local residual generator is designed for each subsystem. The parameters of the DKF are then determined by minimizing the estimation error and the upper bound of its covariance in the fault-free case, which ensures the robustness of the residual. Furthermore, by utilizing the instantaneous $T^{2}$ test statistic and the sliding window-based $T^{2}$ test statistic of the residual signals, the corresponding residual evaluation function and fault detection threshold are established to facilitate fault detection for each subsystem. In the proposed fault detection scheme, each subsystem only transmits information to its neighbors, ensuring that each subsystem can detect its faults in a distributed manner. Additionally, a sufficient condition is provided to guarantee the mean square boundedness of the estimation error in the fault-free case. Finally, a power network system is employed to demonstrate the effectiveness of the proposed scheme.

In this paper, a novel synchronization learning scheme is proposed for secure communication, where the signal transmission architecture with a chaotic encryption process is considered. Firstly, to realize the information security in communication, the original signals are encrypted by fractional order dynamics from the sender, and decrypted by receiver to achieve synchronization. For the process, a hybrid order dynamic optimization is constructed, where the fractional order and the integer order systems are modeled as constraints. Secondly, a transformation formula is developed to convert these constraints into new integer order dynamics, and the equivalence between two dynamic optimizations is obtained. Thirdly, to obtain the synchronization solution, a new iterative learning algorithm is designed, and the adaptive dynamic programming is successfully embedded into the solving process. Finally, we apply the proposed synchronization scheme into the secure image transmission, and the simulation results demonstrate the effectiveness and practicality successfully.

Although distributed renewable energy sources (DRESs) provide a sustainable solution to future microgrids (MGs), their fluctuant power outputs can incur frequency instability. The work studies the load frequency control (LFC) for MGs with the integration of wind energy under a hierarchical architecture. At the DRES level, a model predictive control method is employed together with an intensified event-triggered scheme considering multiple historic released signals to improve the computation efficiency. At the MG level, robustness specification is addressed in mean-square asymptotic stability to relieve the fluctuations caused by wind power penetration. Furthermore, the phasor measurement unit (PMU) failure and intermittent transmissions are considered in the control design, leading to the resilient control policy. Besides, this article extends the applicability of conventional small-signal LFC model by adding an uncertain matrix to tolerant the parameter variation due to the shift of the steady-state operating point caused by wind energy integration. The closed-loop performance based on the deployed resilient LFC strategy is verified through hardware-in-the-loop experiments, by which the frequency regulations against PMU failures and intermittent communication at different levels are effectively exhibited.

This article provides an efficient edge-end implementation solution for deep learning-based surface defect detection to improve the accuracy and efficiency when applied on edge devices with limited resource. An efficient you only look once (YOLO) network YOLOv5s-GhostNet is proposed, which highlights at the lightweight backbone/neck network, efficient feature extraction modules, and a fast learning scheme based on knowledge distillation. The parameter compression ratio is theoretically analyzed to show the decrease of computation complexity. The jointed loss is designed to enhance the generalization ability for new defects. An industrial testing platform with real-time edge-terminal-cloud detection system is developed with Raspberry Pi as edge. The experimental results show that the proposed method gets performances at complexity (floating-point operations per second (FLOPS) 8.2G, pt 7.9M), detection accuracy (precision 97.91$\%$, mean average precision (mAP) 96.66$\%$), efficiency [frames per second (FPS) 294 for single defect], and fast learning convergence (50 epochs). Compared to the existing methods, it reduces model size by 50$\%$on overage, increases the detection efficiency by 4 times and maintains the higher accuracy.

Navigating restricted waterways is widely considered one of the most stressful phases for ship operators due to the confined navigable areas and the uncertainty of encounter situations. Most existing ship collision avoidance methods are developed for open-water navigation and do not adequately address the unique constraints and uncertainties associated with restricted environments. This paper proposes PROMPTER, a probabilistic motion planning framework designed to assist pilots in navigating restricted waterways. PROMPTER transforms motion planning into an inference task by integrating ship maneuverability, situational awareness, and path planning within a Bayesian factor graph, allowing optimal policies to be derived through posterior inference over control. Within the graph, operational restrictions are encoded as prior knowledge to constrain and guide the control inference process. Furthermore, we theoretically prove the closed-form solution and convergence of the constrained control inference. To ensure that PROMPTER is applicable in diverse operational conditions, we consider scenarios with both reliable and unreliable communication, leading to the development of cooperative and non-cooperative collision avoidance strategies. Experiments conducted in both synthetic and real-world restricted waterways demonstrate that PROMPTER outperforms existing methods by generating collision-free and kinematically feasible paths.

Robotics and automation is one of crucial trend in smart manufacturing to improve production efficiency. Au-tomated guided vehicles (AGVs) are a type of mobile robot used for material handling and have become widely utilized to achieve transportation automation. The usage of multiple AGVs introduces potential risks, such as traffic conflicts and safety risk. To meet high production demands, numerous shop floors are set up for large-scale manufacturing. Thus, reasonable and efficient AGV scheduling is vital for real-world operations. This paper proposes an efficient and scalable prioritized planning algorithm for large-scale multiple-AGV scheduling problem in manufacturing. The algorithm sequentially addresses two primary sub-problems: job assignment and conflict-free routing. The results of job assignment dictate the routes taken by the AGVs. In job assignment, jobs are allocated sequentially based on their pickup times. In conflict-free routing, AGV priorities are predefined, ensuring that higher priority AGVs maintain their movement while adjustments are made only to lower priority AGV plans when conflicts arise. Simulation is conducted on two real shop floor layouts and demonstrates the effectiveness and high efficiency of proposed algorithm. Even in a large-scale layout with 500 jobs and 20 AGVs, the computation time is only around 21 seconds.

Human-Robot Collaboration (HRC) is essential for enhancing productivity and flexibility in smart manufacturing, which poses requirements on accurately predicting the future movements of human operators, especially the trajectories of their upper limbs. However, existing model-based studies on human manipulation prediction lacks consideration of stochasticity and variability while the emerging deep learning-based methods are demanding on data size, which yet makes real-time deployment challenging. Therefore, combining the advantages of both model-based and deep learning-based methods, a method for predicting human arm motion, specifically, the position of a worker's wrist in less than 0.5 second, is proposed by hybridizing a Long Short-Term Memory (LSTM) network with an Inverse Kinematics (IK) model. Using historical coordinate sequences of the wrist joint in three-dimensional space in the past multiple frames as input, a neural network is trained to output the predicted coordinates of the wrist joint for the next frame. Then IK (Inverse Kinematics) is used to calculate the arm's motion trajectory based on the predicted wrist coordinates. As the predicted wrist coordinates are sequentially used as the input for the next prediction cycle, the prediction is realized over a sliding time window. Evaluation was conducted using both proprietary and open datasets, results demonstrated that our LSTM-IK method achieved high prediction accuracy, with an average distance error of approximately 5 cm, and can adapt to various task scenarios and individual differences. Additionally, comparison with ground truth illustrated the model's ability to handle complex motion patterns, even with partial occlusions or rapid movements.

As an important part in smart manufacturing under Industry 4.0 era, human-robot collaboration (HRC) features the interaction between human operators and machines, which makes the research of human fatigue come into sight. However, most existing studies on human fatigue or efficiency detection are realized using detectors and models from bioelectronics, whose intrusive detection and decoding of electromyographic signal limits the generality and applicability of such methods. Therefore, this study proposes a human fatigue extraction method based on video data. A new dataset on human manipulation is established by collecting video data of assembly operations to simulate the working status of human operators under smart manufacturing environment. With human skeletal data extracted from the video using a machine learning-based pose extraction tool, MediaPipe, a spatiotemporal analysis for critical skeleton points is implemented for working status categorization and learning using a stochastic gradient descent (SGD) classifier. In this way, the duration taken to complete an assembly task can be extracted as the operation time using the trained SGD classifier, and thus the time-varying operation time series data are obtained to show the trend of human fatigue level. An accuracy of 98.3% is obtained for working status identification for the dataset. Several quantitative indicators like pearson correlation, R-squared value, root mean squared error (RMSE), and Fréchet Distance, are used to evaluate the accuracy of both the extracted operation time and its time-varying curve as compared to the ground truth, with satisfactory results showing the effectiveness of the proposed method.

Extensive research has been conducted to explore vehicle lane changes, while the study on cut-ins has not received sufficient attention. The existing studies have not addressed the fundamental question of why studying cut-ins is crucial, despite the extensive investigation into lane changes. To tackle this issue, it is important to demonstrate how cut-ins, as a special type of lane change, differ from other lane changes. In this paper, we explore to compare driving characteristics of cut-ins and other lane changes based on naturalistic driving data. The highD dataset is employed to conduct the comparison. We extract all lane-change events from the dataset and exclude events that are not suitable for our comparison. Lane-change events are then categorized into the cut-in events and other lane-change events based on various gap-based rules. Several performance metrics are designed to measure the driving characteristics of the two types of events. We prove the significant differences between the cut-in behavior and other lane-change behavior by using the Wilcoxon rank-sum test. The results suggest the necessity of conducting specialized studies on cut-ins, offering valuable insights for future research in this field.

With the improvement of intelligence and integration, automatic supervision of large-scale systems is a current challenge in guaranteeing the high-reliability of edge devices. Hence, fast & accurate anomaly detection (AD) has become an urgent need via the edge computing of the industrial Internet of Things (IIoT). For this purpose, this paper creatively proposes a dual agents based on two-phase adversarial training strategy (2P-DAs) to perform rapid, stable and unsupervised AD for large-scale IIoT-edge devices. It integrates the superiorities of deep autoencoder (AEs) and generative adversarial network (GANs), utilizing normal multivariate time-series as inputs, 1-Encoder vs. 2-Decoders architecture as backbone, and two-phase unsupervised adversarial learning to make it isolate anomalies while providing efficient training. On the one hand, this allows the inherent limitations of AEs to be overcome by training a model capable for recognizing non-anomalies and thus performing a good reconstruction. On the other hand, dual structures allow for stability in adversarial training, thereby solving the issues of collapse and non-convergence encountered in GANs. Two practical industrial data, as cloud & edge data, are used to verify the robustness, inference speed and high detection performance of 2P-DAs in IIoT-edge AD, which demonstrates an impressive performance under multiple evaluation indexes.

The increasing integration of renewable energies, while beneficial for environmental and economic sustainability through decarbonization, poses challenges to frequency stability due to the intermittent nature of renewable power supply. To facilitate smoother integration into the main grid, this study proposes a resilient distributed load frequency control (RDLFC) strategy with a hierarchical structure. At the lower level, wind energy integration is managed using a model predictive control framework enhanced by an improved event-triggered scheme, which can effectively trigger key feedback signals at critical points and tolerates imperfect event modeling and generator dysfunctions. Plug-in electric vehicles are also utilized for fast frequency regulation. At the higher level, the linearized model is improved with an uncertain parameter matrix to account for variations in steady-state operating points due to renewable integration. A robust performance index is incorporated to derive stability conditions, even in the presence of temporary faults in phasor measurement units (PMUs). Validation results confirm the effectiveness of the proposed RDLFC strategy in handling temporary PMU faults.

The microgrid, capable of providing flexible and controllable means to integrate distributed renewable energy sources (DRESs), has long been seen as a most promising solution to convert DRESs into the electrical power. However, the intermittent power output of DRESs, together with the unexpected load perturbations, challenges the frequency stability of microgrids. In this context, the work proposes a robust distributed load frequency control (DLFC) method for multi-area wind energy-dominated microgrids, in which way all interconnected areas can work cooperatively to confront the unbalanced power occurring in partial areas. Considering the flexibility for large-scale deployment, the wind energy is taken as the DRES, while the thermal power plant is chosen as the base power generation. To mitigate the fluctuation of wind power output caused by uncertain wind speed, a sample-based stochastic model predictive control approach is presented. For the high-level DLFC of multi-area microgrids, robust performance index is incorporated in stability analysis and control synthesis. Moreover, temporary phasor measurement unit (PMU) failures are considered in DLFC design and are modeled by random Bernoulli variables to quantitatively analyze their impacts on frequency dynamics. Validations on a four-area microgrid verify the efficacy of the proposed robust DLFC method under different PMU failure probabilities.

To further the penetration level of renewable energy sources (RESs) in power systems, the paper integrates wind turbines into conventional load frequency control (LFC). A resilient model predictive control (MPC) framework is constructed in the context of potential false data injection (FDI) attacks on vulnerable communication networks of multi-area power systems. To reduce the power generation cost, an economic cost function for MPC is firstly formulated. Then, a decentralized-model-based$\chi^{2}$detection unit is presented to distinguish the attacked measurements sent from neighbors. Moreover, to reduce the computation burden of executing the distributed MPC strategy, an intensified event-triggered scheme that can handle incomplete and inaccurate modeling issues is proposed. Validation results illustrate the efficacy of the detection unit and the intensified event-triggered scheme, and conclude the relationships between alarming thresholds and key performance indicators.Note to Practitioners—This paper explores the applicability of LFC with the integration of wind turbines under economic MPC framework. Motivated by the underlying FDI attacks on vulnerable communication networks among different control areas, an intrusion detection unit is proposed to install at each controller side to realize resiliency enhancement. Different from the existing works, this paper meticulously investigates the relationships between alarming thresholds and key performance indicators (KPIs), aiming at providing some valuable references for power operators and managers. Besides, this paper proposes an intensified event-triggered scheme to relieve the computation burden of MPC algorithm. This intensified event-triggered scheme has two advantages. One is that it considers the historic released signals in event-triggered conditions, which makes sure the critical signals at crests or troughs of frequency dynamic curves can be triggered. The other advantage is that the supplementary event-trigged condition can well tolerant the incomplete and inaccurate modeling problems existing in conventional event-triggered conditions. Simulations verify the efficacy and feasibility of the resilient event-triggered MPC strategy for frequency regulation under FDI attacks.

In this work, we investigate the unknown moving-target circumnavigation problem in GPS-denied environments. A minimum of two tasking agents is excepted to circumnavigate the target cooperatively and symmetrically without prior knowledge of its position and velocity in order to achieve optimal sensor coverage persistently for the target. To achieve this goal, we develop a novel adaptive neural anti-synchronization (AS) controller. Based on relative distance-only measurements between the target and two tasking agents, a neural network is used to approximate the displacement of the target such that the position of the target can be estimated accurately and in real time. On this basis, a target position estimator is designed by considering whether all agents are in the same coordinate system. Furthermore, an exponential forgetting factor and a new information utilization factor are introduced to improve the accuracy of the aforementioned estimator. Rigorous convergence analysis of position estimation errors and AS error shows that the closed-loop system is globally exponentially bounded by the designed estimator and controller. Both numerical and simulation experiments are conducted to demonstrate the correctness and effectiveness of the proposed method.

While the Internet of Things (IoT) structure is capable to facilitate the distributed load frequency control (DLFC), the open-air sensors and the intrinsically open communication networks are inevitably vulnerable to uncertain environments. This work endeavors to present a general resiliency enhancement framework for DLFC considering the IoT faults. Multiple fault sources are incorporated, including the intermittent measurements caused by sensor aging, the communication network failures caused by cyberattacks, etc. The framework is equipped with two resilient layers. The first resilient layer focuses on the offline robust DLFC design, in which we consider the intermittent measurements from sensors in system modeling. The second resilient layer concerns the online cyberattack detection, which can further tolerant the incomplete modeling issues of the first resilient layer. Simulation results verify the efficacy of the presented resilient framework.

For a long period, automated vehicles (AVs) or vehicle platoons will coexist with human-driven vehicles (HDVs) in heterogeneous traffic flow, where the cut-in maneuver of human drivers can be frequently expected. In this paper, to understand and simulate the driver decisions on whether to continue the cut-in and when to execute the lane-change during the cut-in process, we propose a two-layer prediction-based decision model by integrating a dynamic prediction module, a continuity decision module, and an execution decision module. To our best knowledge, this is the first study to model the driver decision behavior of the cut-in process. Cut-in experiments are conducted to collect the decision and control data of drivers under one-and two-target-vehicle scenarios, which both include sixty sub-scenarios with different initial velocities, accelerations, or positions of the vehicles. We prove the effectiveness of the proposed model in simulating the driver decision behavior of the cut-in process by comparing the experimental and simulation results under various scenarios over different subjects. Besides, we analyze the effects of some model parameters on the model performance to show their ability to represent different driving styles.

This paper investigates the stochastic moving target encirclement problem in a realistic setting. In contrast to typical assumptions in related works, the target in our work is non-cooperative and capable of escaping the circle containment by boosting its speed to maximum for a short duration. In extreme conditions, where GPS signals are not available, weight restrictions are present, and ground guidance is absent, the agents can rely solely on their onboard single-modality perception tools to measure the distances to the target. The distance measurement allows for creating a position estimator by providing a target position-dependent variable. Furthermore, the construction of the unique distributed anti-synchronization controller (DASC) can guarantee that the two agents track and encircle the target swiftly. The convergence of the estimator and controller is rigorously evaluated using the Lyapunov technique. A real-world UAV-based experiment is conducted to illustrate the performance of the proposed methodology in addition to a simulated Matlab numerical sample. Our video demonstration can be found in the URL https://youtu.be/EDVLvP-bk8M.

Automated Guided Vehicles AGVs play a vital role in enhancing productivity and efficiency within factory environments. However, their safe and effective operation heavily relies on the ability to navigate through complex spaces while avoiding obstacles. The significance of obstacle avoidance in AGV systems is emphasized, considering its impact on ensuring smooth material flow, minimizing collision risks, and optimizing production processes. The existing state of obstacle avoidance applications in factory settings reveals certain limitations and challenges. Current research and industrial implementations often rely on rule-based approaches or predefined paths, which may not adequately adapt to dynamic environments or unexpected obstacles. Additionally, some methods lack the ability to handle diverse obstacle types or efficiently plan optimal paths, leading to sub-optimal navigation or reduced throughput. In response to these challenges, this study proposes a novel approach for dynamic obstacle avoidance of AGVs based on deep reinforcement learning. By leveraging the Deep Deterministic Policy Gradient (DDPG) model, the AGV learns to make real-time decisions and navigate through dynamic obstacles effectively. The integration of deep neural networks with the actor-critic framework enables the AGV to learn and adapt optimal policies for obstacle avoidance in real-time, overcoming the limitations of rule-based methods. Simulation experiments are conducted to validate the performance and feasibility of the proposed approach. The results demonstrate that the DDPG-based method allows the AGV to successfully navigate through both dynamic and static obstacles in a dynamic environment, improving safety and efficiency in intelligent manufacturing applications.

Vehicle platoons (VPs) are groups of vehicles driving together with a short inter-vehicle gap and a harmonized velocity. For a long period, the VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flow, where the cut-in maneuver of the HDVs towards the VPs can be frequently expected. In this paper, to handle such cut-ins, we propose an intention prediction-based control method for the VPs by considering the tradeoff between the platoon integrity and traffic safety. Particularly, the proposed method is designed to prevent as many cut-ins as possible while taking care of the road safety. It consists of a cut-in prediction part, including intention and trajectory prediction algorithms, and a finite state machine (FSM)-based predictive control part, including a high-level FSM and a low-level predictive control. Driver-in-the-loop experiments were conducted in the VP-based driving scenarios to train the intention prediction algorithm and test the proposed method. We show the results detailing the control behavior of the proposed method in a no cut-in test, a mandatory cut-in test, and three discretionary cut-in tests. The results demonstrate that the proposed method can predict the cut-in intention of human drivers in real time. Besides, according to the prediction results, the proposed method can prevent cut-ins for the VPs while taking care of the road safety.

In recent years, the world of vehicular communication is in full progress. With the emergence of the fifth-generation (5G) technology, the high bandwidth and low latency features in the 5G vehicle to everything (5G-V2X) network become possible. However, the current 5G mechanism specified by the Third Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead over the radio access network and the core network when a vehicle platoon moves from a source base station to the target base station. Moreover, it also has several security problems in terms of the failure of key forward secrecy (KFS) and lack of mutual authentication. In this paper, we propose an efficient authentication protocol for vehicle platoons in all handover scenarios. By the proposal, the identities of base stations and vehicles are mutually authenticated by certificateless aggregated signatures, which can also reduce signaling overhead and is free from key escrow problems. The proposed protocol has been formally evaluated by BAN-logic and the Scyther tool to show its ability to resist major typical malicious attacks. It has also been analyzed on its security functionality. The performance evaluation demonstrates that the proposed protocol is efficient in terms of signaling, computational and communication cost.

This article studies the optimal state estimation problem for interconnected systems. Each subsystem can obtain its own measurement in real time, while, the measurements transmitted between the subsystems suffer from random delay. The optimal estimator is analytically designed for minimizing the conditional error covariance. The boundedness of the expected error covariance (EEC) is analyzed. In particular, a new condition that is easy to verify is established for the boundedness of EEC. Further, the properties of EEC with respect to the delay probability are studied. We found that there exists a critical probability such that the EEC is bounded if the delay probability is below the critical probability. Also, a lower and upper bound of the critical probability is derived. Finally, the proposed results are applied to a power system, and the effectiveness of the designed methods is illustrated by simulations.

In an Internet of Vehicles (IoV) network, vehicles periodically broadcast Basic Safety Messages (BSMs) that contain the vehicle's current position, speed, and acceleration. Safety-critical applications like blind-spot warning and lane change warning systems use these BSMs to ensure the safety of road users. However, an attacker can affect the efficacy of such applications by injecting false information into the messages. One such attack is the position falsification attack, where the attacker inserts incorrect information regarding the vehicle's position in the BSMs. The literature has explored the use of Misbehavior Detection Systems (MDSs) to detect position falsification attacks. But the limitation of the existing MDSs is that they are signature-based and require prior knowledge about the attacks for effective detection. To overcome this shortcoming, we propose a Novel Position Falsification Attack Detection System for the Internet of Vehicles (NPFADS for the IoV)that learns and detects new position falsification attacks emerging in IoV networks. The performance of NPFADS is quantitatively measured using the metrics precision, recall, F1 score, and ROC. The Vehicular Reference Misbehavior (VeReMi) dataset is used as the benchmark to analyze the performance of NPFADS. The performance of NPFADS is compared to existing MDSs in the literature, and the analysis shows that NPFADS performs on par with the existing signature-based detection models even when initialized with zero initial knowledge.

The potential ability to increase the capacity and safety on roads, as well as fuel economy gains has made vehicle platooning an appealing prospect. However, its use is yet to be widespread, partially due to the security concerns on it. One particular concern is the admission of fake virtual vehicles into the platoons, allowing them to wreak havoc on the platoon, which is known as a Sybil attack. In this paper, we propose a secure vehicular authentication scheme for platoon admission which is resistant to the threats of Sybil attacks. The proposed scheme offers a mutual authentication on both vehicle identity and message through a combination of a key exchange, a digital signature and an encryption scheme based on Elliptic Curve Cryptography (ECC). The scheme holds its outstanding feature to provide both perfect forward secrecy and group backward secrecy to ensure the protection of anonymity of vehicles and platoons while typical malicious attacks such as replay, and man-in-the-middle attacks can also be resisted. A formal evaluation of the security of the scheme by Canetti-Krawczyk (CK) adversary and random oracle model has been conducted to demonstrate its security functionality. Finally, the performance of the proposed scheme has been evaluated to show its efficiency.

Traffic speed prediction is a significant branch of the intelligent transportation system (ITS). A good prediction could alleviate the non-recurring congestion on the road and provide a strong decision-making basis for traffic management and control. However, it is always a challenging research problem due to the complexity of the road network and the dynamics of traffic conditions. Many deep learning-based methods have been applied to the traffic prediction problem, which could extract both spatial and temporal information efficiently. However, for some dataset that suffers from data paucity problem, the generalization ability of the model is not good and the performance degrades. To tackle this problem, we proposed a novel graph-based generative adversarial network for the traffic speed prediction problem. We design a generative network to generate some fake traffic data and use a discriminative network to distinguish between real and fake targets. The generator consists of a GraphSAGE and LSTM model to learn the representation of spatial-temporal traffic data. Several experiments have been conducted on several real-world traffic datasets, demonstrating that our proposed model outperforms other baseline models. The experiment results illustrate the importance of utilizing GAN in the training process, which improves the generalization ability of the prediction model.

There is no doubt that in the near future, machines will share roads with human drivers [1] [2]. Therefore, the prediction of human drivers' lane changing behavior is imperative. Lane-change prediction is one of the most important ones. Both human drivers and autonomous vehicles should make sure that no other vehicle switches lanes or moves into the same region of the target lane as the ego vehicle. The existing short-term prediction algorithms can only provide a prediction horizon of 3 ~ 5s, leaving only a limited reaction time for drivers and autonomous path planning modules. Additionally, the majority of previous research analysed less on investigate lane segmentation or merging, simply the inference of lane shift in an expressway context. Most of earlier research only focused on the inference of lane-change in an expressway context, as opposed to the more typical urban environment. There are relatively few of these studies that can handle multi-scenario and scenario switching. In this paper, a Scenario Encoding Model (SEM) is proposed to help solve the problem of long-term lane-change prediction and the scenario switching problem in the existing short-term lane-change prediction. Even in the absence of road history data, the SEM can model the road scenario and encode the real-time road scene by using Self-Organizing Map (SOM) In the mean time, the established initial model has the ability to be further evolved into a historical bias model in the background of a large amount of road historical data. The evaluation test of this SEM has been done through the NGSIM dataset.

In recent years, the world of vehicular communication is in full progress. With the emerge of the fifth-generation (5G) technology, the high bandwidth and low latency features in the 5G vehicle to everything (5G-V2X) network become possible. However, the current 5G mechanism specified by the Third Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead over the radio access network and the core network when a vehicle platoon moves from a source base station to the target base station. Moreover, it also has several security problems in terms of the failure of key forward secrecy (KFS) and lack of mutual authentication. In this paper, we propose an efficient authentication protocol for vehicle platoons in all handover scenarios. The proposed protocol has been formally evaluated by the Scyther tool to show its ability to resist major typical malicious attacks. It has also been analysed on its security functionality. The performance evaluation demonstrates that the proposed protocol is efficient in terms of signaling and computational cost.

The lack of standardization and the heterogeneous nature of the Internet of Things (IoT) has exacerbated the issue of security and privacy. In literature, to improve security at the network layer of the IoT architecture, the possibility of using Software-Defined Networking (SDN) was explored. SDN is also plagued by network threats that affect conventional networks. One such threat to a network is the Low-Rate Denial of Service (LR DoS) attack, where the attacker sends precise traffic bursts that force a TCP flow to enter a retransmission timeout state. LR DoS attacks are difficult to detect as their attack signature is similar to benign network traffic. The existing AI-based detection algorithms in the literature are signature-based, and their efficacy in detecting unknown LR DoS attacks was not explored. In this work, an AI-based anomaly detection scheme called FeedForward–Convolutional Neural Network (FFCNN) is proposed to detect LR DoS attacks in IoT-SDN. The Canadian Institute of Cybersecurity Denial of Service 2017 (CIC DoS 2017) dataset is used for the study. An iterative wrapper-based feature selection using Support Vector Machine (SVM) is used to derive the significant features required for detection. The performance of FFCNN is compared to the machine learning algorithms-J48, Random Forest, Random Tree, REP Tree, SVM, and Multi-Layer Perceptron (MLP). The performance of the models is measured using the metrics accuracy, precision, recall, F1 score, detection time per flow, and ROC curves. The empirical analysis shows that FFCNN outperforms other machine learning algorithms on all metrics.

In the complex practical engineering systems, many interferences and attacking signals are inevitable in industrial applications. This article investigates the reinforcement learning (RL)-based resilient control algorithm for a class of Markovion jump systems with completely unknown transition probability information. Based on the Takagi-Sugeno logical structure, the resilient control problem of the nonlinear Markovion systems is converted into solving a set of local dynamic games, where the control policy and attacking signal are considered as two rival players. Combining the potential learning and forecasting abilities, the new integral RL (IRL) algorithm is designed via system data to compute the zero-sum games without using the information of stationary transition probability. Besides, the matrices of system dynamics can also be partially unknown, and the new architecture requires less transmission and computation during the learning process. The stochastic stability of the system dynamics under the developed overall resilient control is guaranteed based on the Lyapunov theory. Finally, the designed IRL-based resilient control is applied to a typical multimode robot arm system, and implementing results demonstrate the practicality and effectiveness.

In leader-following formation, the leader plays a central role and is more vulnerable to malicious attacks. To effectively protect the leader, this article investigates an event-based formation control problem of autonomous surface vehicles (ASVs) with multiple attackers and actuator failure. A novel adaptive formation keeping and interception scheme is developed for ASVs. In this scheme, a formation keeping controller and an interception controller are designed under a unified framework by sharing same event-triggered mechanism and fault-tolerant structure. To bridge these two controllers, a degree and distance driven formation interception ($\text{D}^3$FI) strategy is designed by generating defenders. It is shown that with the proposed scheme, all attackers are intercepted without affecting formation keeping of remaining ASVs if the original topology is connected. Advantages of our scheme are: 1) stable controller switching is ensured during the formation cooperation and 2) the fault-tolerance is obtained with lower actuator updating frequencies and least number of adaptive parameters for each ASV.

A vehicle platoon is a group of vehicles driving together with a harmonized speed and a short inter-vehicle gap by using vehicle automation and vehicle-to-vehicle communication. Platoons have to share road with human-driven vehicles (HDVs) and can only be applied in heterogeneous traffic flow for a long period. Driver cut-in behavior (DCB) towards a platoon can be frequently expected in such driving context. In this paper, to understand and simulate such behavior, we propose a platoon-oriented cut-in behavior (POCB) model by fusing a lateral and a longitudinal control model into the queuing network (QN) cognitive architecture. Platoon-oriented cut-in experiments are conducted to collect driver data under cut-in from back and front scenarios, which both include six sub-scenarios with different platoon gaps or initial velocities. We demonstrate the effectiveness of the proposed model in simulating the DCB towards platoons by comparing experimental and simulation results under various driving scenarios across different subjects.

Inherent uncertainties of the air transportation system (ATS) can induce unexpected anomalies in its operations such as deviations in flight schedules, sudden imbalances of demands and capacities, etc.. Current air traffic flow management (ATFM) models rarely consider both demand and capacity uncertainties in their algorithms, and generally focus on minimizing the flight delays under deterministic constraints. Thus, to bridge this gap, we propose a framework for en-route ATFM while scrutinizing uncertainties in en-route capacity and demand and their imbalance, via a chance constraint based probabilistic approach. The proposed framework plays a key role in ensuring the safety of the overall ATS in terms of maintaining the safety separation between flights and constraining the capacity of the sectors as well. Moreover, flight level assignments scheme is proposed based on the Base of Aircraft Data (BADA) of the European Organization for the Safety of Air Navigation (EUROCONTROL) with the objective of minimizing the fuel consumption. The model further minimizes the overall expected delay of the system using the control actions of ground holding, speed control, rerouting, and flight cancellations. At the implementation stage, two phases of ATFM as pre-tactical and tactical are considered, in which the former focuses on generating optimal trajectories and the latter focuses on real-time updates of flight plans. The computational complexity is reduced by shrinking the feasibility region and decomposing the problem into maximum weighted independent sets. The experimental results of realistic large-scale problems demonstrate the effectiveness and computational feasibility of our ATFM framework.

A multi-bus dispatching strategy is proposed for a ring-shaped road bus transport system, which allows dispatching single bus or multiple buses and incorporates volume dynamics on both buses and stations. Also, the passengers’ perceived waiting time is firstly formulated as one part of the cost function to take passengers’ anxiety into account, and thereby improving the bus quality of service of bus operations. At upstream stations, as many passengers as possible will board the bus, which leads to the less space remaining on the bus and thus the enlongated wait for passengers at downstream stations. With the aim to avoid such phenomenon, the bus boarding control is implemented in the passengers’ boarding process captured by a simultaneous loading model to provide boarding opportunities for the waiting passengers at downstream bus stations. The formulated problem is tackled in two different scenarios, i.e., either with a linear cost or with a nonlinear cost. The linear cost, incorporating the passengers’ actual waiting time and the bus utilization, is firstly converted into a Mixed Integer Linear Programming (MILP) problem, and is solved by the commercial solver Gurobi. With the computational complexity as a concern, two different evolutionary algorithms, Genetic Algorithm (GA) and Harmony Search algorithm (HS), are also adopted to solve the problem in real time. In Scenario 2, the nonlinear cost, integrating the passengers’ perceived waiting time and the bus utilization, is directly solved by both GA and HS. Finally, case studies are provided to illustrate the efficiency of our proposed strategy by comparing with the traditional bus schedule strategies, as well as analyzing the different impacts of the bus loading process when either passengers’ actual waiting time or passengers’ perceived waiting time are taken into account.

The continuing increase of the on-road private cars is contributing to a deterioration of the urban traffic system. Public transportation is widely used to tackle this issue due to its large ridership. In this paper, we propose a multi-bus dispatching strategy combined with the boarding and holding control (MBDBH) to improve bus utilization and further decrease the passenger excess delay. Dispatching adjustments and operation control are taken into account in the system. At the dispatching level, on the one hand, either a bus platoon or a single bus can be dispatched for each trip to provide adaptive bus capacity to match the highly-fluctuated stop demands, on the other hand, we adjust the bus dispatching time based on the existing timetable to minimize passenger excess waiting time to a large extent. Meanwhile, the operation level incorporates both holding strategy and boarding limit strategy to bring more flexible adjustments in improving bus service. Besides the efficiency, we also minimize the headway variation in order to maintain a high system reliability. The problem is formulated as a Mixed Integer Nonlinear Programming (MINP) problem, which is solved by the commercial solver Gurobi. With the computational complexity as a concern, we propose a distributed algorithm to implement dual decomposition based on the partial Lagrangian relaxation. Finally, numerical examples are investigated to illustrate the significant time reduction of distributed algorithm and the efficiency of our proposed strategy: The proposed MBDBH model can reduce roughly 50% and 30% of remaining passenger volumes when compared with the timetable-based fixed schedule and the optimized single-bus dispatching schedule, respectively.

This article proposes a novel dynamic fault-tolerant control model to address the optimal tracking cooperative control problem for cyber-physical systems, by considering that all systems can be endowed as a multiagent system and the admissible levels of the actuator fault can be resiliently management. Different from previous works, the feedback gain for the cooperative controller design is no longer fixed, and actuator outage behaviors can be solved by a resilient control way. By introducing a sampling manner, a robust optimal framework is first developed to determine the appropriate feedback gain under a cost constraint for the dynamic fault model. The dynamic fault-tolerant control protocol is, then, designed to achieve the cooperative behaviors. Moreover, a fault management mechanism is proposed, in which the fault parameter is reset as an initial value when the fault growth is greater than the admissible level. By this design, the tracking cooperative behaviors can be achieved in a resilient management process. Two examples are presented to illustrate the effectiveness of the proposed theories.

In order to efficiently balance the current and future air traffic demands with the system capacity, a proper Air Traffic Flow Management (ATFM) approach is required. The current focus of ATFM is generally on optimally utilizing the available airspace and airport capacities, while maintaining the required safety separation between aircraft. Yet, only a minor focus is given to the inherent uncertainty in the Air Transportation System (ATS), especially to its adverse effect on safety and day-to-day operations. To this end, we propose an ATFM framework scrutinizing the stochastic nature of ATS through a chance-constraint-based probabilistic approach. Moreover, anticipating the high volumes in air traffic in the future, we propose to split the model into two stages, in which the first stage scrutinizes the behavior of a set of flights as a flow, while the second stage transforms them into individual flight plans, enhancing scalability. The two models are formulated as an Integer Linear Programming (ILP) problem, and a Mixed Integer Linear Programming (MILP) problem at stages I and II, respectively. The NP-hard nature of the overall problem is minimized by transforming the problem into a Maximum Weighted Independent Set (MWIS) finding problem.

Conflicts between pedestrians and vehicles are one of the common safety issues at signalized intersections. Pedestrian Flashing GREEN (FG), a time interval for pedestrians on crosswalks to safely finish crossing before the next phase occurs, may fail to clear the crosswalk in the allotted time, due to significant pedestrian non-compliant behavior. In this manner, probability of pedestrian-vehicle exposures increases when non-compatible vehicle flows are released at the next immediate phase. This paper seeks to address this issue by presenting a traffic signal control strategy for urban traffic networks that aims to minimize vehicle traveling delay (increase efficiency) as well as pedestrian crossing risk (increase safety). First, a macroscopic model for pedestrian-vehicle mixed-flow networks is proposed. Considering the high-incidence rate of pedestrian violations during FG, an additional Dynamic All RED (DAR) phase is introduced at the end of each FG period, whose duration is adaptively adjusted according to the number of non-compliant pedestrians. With computational complexity being a concern for our model, an evolutionary algorithm with repairing mechanism (EARM), is proposed to solve our problem. Case studies are provided to illustrate the potential impact of the pedestrian movement to the vehicle traffic networks when pedestrian safety is considered in the system, as well as the efficacy of our traffic light control strategy for pedestrians and vehicles on risk reduction.

This article investigates the adaptive resilient event-triggered control for rear-wheel-drive autonomous (RWDA) vehicles based on an iterative single critic learning framework, which can effectively balance the frequency/changes in adjusting the vehicle's control during the running process. According to the kinematic equation of RWDA vehicles and the desired trajectory, the tracking error system during the autonomous driving process is first built, where the denial-of-service (DoS) attacking signals are injected into the networked communication and transmission. Combining the event-triggered sampling mechanism and iterative single critic learning framework, a new event-triggered condition is developed for the adaptive resilient control algorithm, and the novel utility function design is considered for driving the autonomous vehicle, where the control input can be guaranteed into an applicable saturated bound. Finally, we apply the new adaptive resilient control scheme to a case of driving the RWDA vehicles, and the simulation results illustrate the effectiveness and practicality successfully.

In this paper, we provide a tutorial introduction to a topological approach for the computation of some supremal sublanguages, often specified by language equations, that arise in the study of the supervisory control theory. As an illustration, we show that the supremal sublanguages for properties such as normality, prefix-closedness, trace-closedness, L-closedness and prefix-closed controllability are supremal open subsets of certain topologies. Thus, the supremal sublanguages correspond to the interiors and can be directly expressed.

Owing to flexibility of Unmanned Aerial Vehicles (UAVs) and high efficiency of image processing technology, the combined systems become increasingly popular and important in the smart city operations. However, the application scenarios of this technology, especially on the traffic system prediction and multi-vehicle information extraction, still need to be explored. Besides, vehicle's detailed attributes need to be considered when building models. The smart traffic system can be broadly divided into two parts, traffic facilities (e.g. traffic signals, signs and sensors) and participants (e.g. vehicles and pedestrians). Many related works are presented about traffic parameters measurements using UAVs. In this paper, the prediction and traffic signal system analysis through different categories of vehicles' dynamic characteristics extracted from UAVs is presented. The motivation and related work is introduced. A stochastic process framework is presented for multi-vehicle speed extraction and signal transition time distributions at a signalized intersection. Detection and tracking methods/algorithms are proposed. To verify the mathematical model, the experimental data is collected at one intersection, in the city of Singapore during peak hours. After data collection, aerial images are processed to extract information. The regression method and processed parameters help to fit the required dynamic functions for different types vehicles. The estimated distributions reflect the traffic signal transition time provided by ground truths nicely. Moreover, the future research is presented on enhancing the system prediction accuracy and robustness.

Remote scene classification serves a vital role in many applications. However, satellite images are often blurred and degraded due to aerosol scattering under fog, haze, and other weather conditions, reducing the image contrast and color fidelity. State-of-the-art remote sensing classification models building upon convolutional neural networks (CNNs) are mostly trained on annotated datasets of clear satellite images. When applied to blurred images, they will suffer a great degradation in performance. To address this problem, we adopt the domain adaptation algorithm TADA and propose Transferable Attention enhanced Adversarial Adaptation Network (TA3N), which utilizes annotated data in clear images by applying knowledge transferring from clear image domain to blurred image domain. Our TA3N first integrates spatial attention to focus on salient areas which are discriminative and transferable. In addition, domain discriminator and adversarial training via gradient reversal layer are used to minimize the discrepancies in extracted features from clear and degraded domains. We synthesize degraded remote scene classification dataset SSI based on FoHIS model. Experiments on degraded SSI showed that TA3N significantly outperforms baseline and other state-of-the-art domain adaptation methods.

In this work, we study the problem of supervisory synthesis for networked discrete event systems against non-FIFO communication channels with bounded delays. Both the observation and control communication channels are represented by finite state automata under the assumption that all communication delays are bounded, the resilient networked supervisor synthesis problem is then reduced to supervisor synthesis for non-deterministic automata. We firstly analyze the structure of networked discrete-event systems with delays, and then describe the message transmission process through the communication channels which connect the plant and the supervisor. The assumption of the networked discrete event systems: 1)The buffer size of the communication channels is limited; 2)There is a maximum delay boundary for each event in the communication channel; 3)The number of multiple copies of the same event in the communication channel is limited. The content of the observation and control channel will be represented by the timed finite state automata. Finally, an example will illustrate how the networked resilient supervisor will be synthesized.

This article is concerned with the optimal tracking control problem of the coupled Markov jump system (CMJS) by using the reinforcement learning (RL) technique. Based on the conventional optimal tracking architecture, an offline tracking iteration algorithm is first designed to solve the coupled algebraic Riccati equation that can hardly be solved by mathematical methods directly. To overcome the crucial requirements and existing shortcomings in the offline tracking method, a novel integral RL (IRL) tracking algorithm is first proposed for CMJS, which develops a transition-probability-free optimal tracking control scheme with a reconstructed augmented system and discounted cost function. Both the requirements of transition probability πij and system matrix Ai are avoided via the designed IRL algorithm. The stability and convergence of the novel schemes are proved by the Lyapunov theory, and the tracking objective is achieved as desired. Finally, we apply the designed algorithms in a fourth-order Markov jump control problem and the stochastic mass, spring, and damper system to track continuous sinusoidal waveforms, and the simulation results are provided to show the effectiveness and applicability.

Demand uncertainty is one critical form of uncertainty which has an adverse effect on Air traffic flow management (ATFM). This is mainly due to the deviation in departure time and aircraft speed from their scheduled values. This may lead to the arrival of aircraft to certain routes at unscheduled times, causing an unexpected demand on those routes. The uncertainty of demand creates several difficulties in air transportation systems, including higher workloads for air traffic controllers, higher delays, travel costs, as well as safety risk. In this study, we propose a robust flight routing and scheduling scheme while considering both departure and speed uncertainty present in the air traffic network. Following robust optimization, we ensure that the capacity violations are eliminated from the system. The ATFM problem is formulated as a Mixed Integer Quadratic Programming (MIQP) problem with the objective of minimizing expected total delay of the system while maintaining required in-trail separation between aircraft even under uncertainty. In addition, we use an optimal flight level assignment method and speed assignment strategy to minimize the system delay and to fully utilize the system capacity. Furthermore, a greedy strategy with parallel computation is presented with the problem decomposed into a set of maximum independent sets to reduce the computational complexity in solving large-scale ATFM problems. With the experimental results, we demonstrate the effectiveness of the model.

In wafer manufacturing, extensive research on the operations of cluster tools under the steady state has been reported. However, with the shrinking down of wafer lot size, such tools are frequently required to switch from handling one lot of wafers to another, resulting in more transient processes, including start-up and close-down ones. Also, wafer residency time constraint is critical for many wafer fabrication processes. To cope with the transient scheduling problem of time-constrained single-arm cluster tools with parallel chambers, based on a generalized backward strategy, this paper first builds timed Petri net models for these two transient processes. Then, two linear programs are derived for the first time to search a feasible schedule with a minimal makespan. Two industrial examples are given to demonstrate the effectiveness of the obtained results at last.

Scheduling a cluster tool with wafer residency time constraints is challenging and important in wafer manufacturing. With a backward strategy, the scheduling problem of such singlerobot-arm cluster tools is well-studied in the literature. It is much more challenging to schedule a more general case whose optimal scheduling strategy is not limited to the backward one. This work uses a timed Petri net (PN) to model the dynamic behavior of the system and presents a method to determine the optimal scheduling strategy for the system. Based on its PN model and the obtained strategy, it reveals that the key issue to schedule such a tool is to determine when and how long the robot should wait for. Based on this finding, this work establishes for the first time the necessary and sufficient conditions regarding the existence of an optimal and feasible one-wafer cyclic schedule for singlerobot-arm cluster tools. It then formulates a computationally efficient linear program to find it if existing, and finally gives industrial examples to show the application and power of the proposed method.

This paper addresses a traffic signal scheduling (TSS) problem in a heterogeneous traffic network with signalized and non-signalized intersections. The objective is to minimize the total network-wise delay time of all vehicles within a given finite-time window. First, a novel model is proposed to describe a heterogeneous traffic network with signalized and non-signalized intersections. Second, five meta-heuristics are implemented to solve the TSS problem. Based on the problem characteristics, three local search operators and their ensemble are proposed. Then, five meta-heuristics with such an ensemble are proposed to solve the TSS problem. Third, experiments are carried out based on the real traffic data in the Jurong area of Singapore. The performance of the ensemble of local search operators is verified. Ten algorithms, including five meta-heuristics with and without the ensemble, are evaluated by solving 18 cases with different scales. Finally, the algorithm with the best performance is compared against the currently used traffic signal control strategies. The comparisons and discussions show the competitiveness of the proposed model and meta-heuristics.

This paper addresses a bi-objective urban traffic light scheduling problem (UTLSP), which requires minimizing both the total network-wise delay time of all vehicles and total delay time of all pedestrians within a given finite-time window. First, a centralized model is employed to describe the UTLSP, where the cost functions and constraints of the two objectives are presented. A non-domination strategy-based metric is used to compare and rank solutions based on the two objectives. Second, metaheuristics, such as harmony search (HS) and artificial bee colony (ABC), are implemented to solve the UTLSP. Based on the characteristics of the UTLSP, a local search operator is utilized to improve the search performance of the developed optimization algorithms. Finally, experiments are carried out based on the real traffic data in Jurong area of Singapore. The HS, ABC, and their variants with the local search operator are evaluated in 19 case studies with different scales and time windows. To the best of our knowledge, this paper is the first of its kind to solve bi-objective traffic light scheduling problems in the literature. To demonstrate the effectiveness of the proposed algorithms in dealing with bi-objective optimization in traffic light scheduling, they are compared to the classical non-dominated sorting genetic algorithm II (NSGAII) with and without the local search operation. The comparisons indicate that our algorithms outperform the NSGAII algorithm with and without the local search operator for solving the UTLSP.

This paper presents a macroscopic model for pedestrian-vehicle mixed-flow network and a traffic signal scheduling strategy for both pedestrians and vehicles. We first propose a novel mathematical model of pedestrians crossing a junction. By combining a link-based vehicle network model, a traffic light scheduling problem is formulated with the aim to strike a good balance between pedestrians' needs and vehicle drivers' needs. The problem is first converted into a mixed-integer linear programming (MILP) problem via a novel transformation procedure, which is solvable by several existing solvers, e.g., GUROBI. Then a meta-heuristic method called discrete harmony search (DHS) algorithm is also adopted to reduce the computational complexity in MILP. Numerical simulation results are provided to illustrate the effectiveness of our real-time traffic light scheduling strategy for pedestrians and vehicles, and the potential impact of the pedestrian movement to the vehicle traffic flows.

Efficient flight routing and scheduling play an important role in air traffic flow management, which aims to maximize the utilization of airport and enroute capacities to ensure safety and efficiency of air transportation. In this paper, we first propose a novel discrete-time flow dynamic model for an air traffic network, consisting of airports, waypoints, and air links, upon which we formulate an air flow routing and scheduling problem as an integer linear programming problem. Considering the NP-hard nature of the problem, we present a novel hierarchical flow routing and scheduling approach, where the hierarchical architecture is derived naturally from the network containment relationship, and computation is carried out in a bottom-up manner, which relies on an incremental strategy. On the resulting flow routes and schedules, a heuristic algorithm is carried out to determine flight plans for individual aircrafts. The effectiveness of the proposed hierarchical approach is illustrated by air traffic data in four flight information regions in the association of Southeast Asian nations.

Occupant detection using carbon-dioxide sensors is prevalent but its accuracy is restricted by the inherent sensing delays. This paper proposes an indoor occupant detection method using real-time carbon-dioxide and Pyroelectric Infrared (PIR) sensor measurements overcoming the sensing delays. The occupancy detection problem is formulated as a classification problem wherein the classifier learns from offline carbon-dioxide data and the actual occupancy measurements of the room. While the classifier can provide realtime occupancy detection, the delays in carbon-dioxide sensors influence their accuracy. To overcome the delays, observations from PIR sensors are combined with the results of the single-layer feedforward neural network (SLFN) based classifier. The classifier works in four steps: (i) data-preprocessing, (ii) feature-selection, (iii) learning, and (iv) validation. The data is preprocessed by smoothing and several features are selected as input to the SLFN. Then, the classifier is validated with realtime experiments. Our results demonstrate that the proposed approach provides accuracy up to 99.79% and also overcomes the delays found in carbon-dioxide sensors.

In this paper, a novel centralized traffic network model is proposed to describe the urban traffic light scheduling problem (UTLSP) in a traffic network. The objective is to minimize the network-wise total delay time of all vehicles in a fixed time window. To overcome the potentially high computational complexity involved in UTLSP, an improved artificial bee colony (IABC) algorithm is proposed. A new solution generating strategy and three local search operators corresponding to different neighbourhood structures of UTLSP are proposed to improve the performance of IABC. Extensive computational experiments are carried out using sixteen instances with different problem-scales. The IABC with and without three local search operators are evaluated and compared. The comparisons and discussions show the competitiveness of IABC for solving UTLSP.

This paper proposes a four-phase mutually coupled switched reluctance machine (MCSRM) arrangement with single pulse current control strategy, targeting performance improvements of both the machine and the power converter. Previously published works on MCSRM have focused only on machine performance while neglecting the power converters. Designs and comparative studies have been carried out among conventional switched reluctance machine (CSRM), full pitch MCSRM, short pitch MCSRM and single pulse MCSRM with practical specifications. Coupled finite element analysis and circuit simulations have been employed to evaluate performance indices including torque ripple, machine iron/copper loss, power factor, winding current density, power converter losses and power densities. The machine-converter system level investigations have confirmed that in the proposed four-phase MCSRM under single pulse control, improved machine performances can be obtained with reduced power converter loss.

This paper presents a novel approximation technique based on metaheuristics and weighted residual function (WRF) for tackling singular boundary value problems (BVPs) arising in engineering and science. With the aid of certain fundamental concepts of mathematics, Fourier series expansion, and metaheuristic optimisation algorithms, singular BVPs can be approximated as an optimisation problem with boundary conditions as constraints. The target is to minimise the WRF (i.e. error function) constructed in approximation of BVPs. The scheme involves generational distance metric for quality evaluation of the approximate solutions against exact solutions (i.e. error evaluator metric). Four test problems including two linear and two non-linear singular BVPs are considered in this paper to check the efficiency and accuracy of the proposed algorithm. The optimisation task is performed using three different optimisers including the particle swarm optimisation, the water cycle algorithm, and the harmony search algorithm. Optimisation results obtained show that the suggested technique can be successfully applied for approximate solving of singular BVPs.

The Ramadge-Wonham supervisory control paradigm has been shown effective in dealing with logic control. Nevertheless, time-related performance is always one of the major concerns in industry. Current methods for synthesizing time optimal supervisors are incapable of dealing with large discrete-event systems (DESs) with massive state spaces. This paper proposes an approach for finding a time optimal accepting trace for large DESs based upon sequential language projection, and pruning. The algorithms are tested on a linear cluster tool to show their effectiveness.

Air traffic flow management (ATFM) is an important component in an air traffic control system and has significant effects on the safety and efficiency of air transportation. In this paper, we propose a distributed ATFM strategy to minimize the airport departure and arrival schedule deviations. The scheduling problem is formulated based on an en-route air traffic system model consisting of air routes, waypoints, and airports. A cell transmission flow dynamic model is adopted to describe the system dynamics under safety related constraints, such as the capacities of air routes and airports, and the aircraft speed limits. Our ATFM problem is formulated as an integer quadratic programming problem. To overcome the computational complexity associated with this problem, we first solve a relaxed quadratic programming problem by a distributed approach based on Lagrangian relaxation. Then a heuristic forward-backward propagation algorithm is proposed to obtain the final integer solution. Experimental results demonstrate the effectiveness of the proposed scheduling strategy.

Differential equations are at the heart of physics and much of chemistry. In this paper, differential equations of convective-radiative longitudinal fins with convex and exponential profiles have been solved approximately using a Fourier-based optimization approach. Using the concepts of mathematics, Fourier series expansion, and metaheuristics, ordinary differential equations (ODEs) can be modeled as an optimization problem. The optimization's target is to minimize the weighted residual function (cost function) of the ODEs. Boundary and initial conditions of ODEs are considered as constraints for the optimization model. Generational distance metric has been used for evaluation and assessment of the approximate solutions against the exact (numerical) solutions. The optimization task has been performed using two well-known optimizers including the harmony search and particle swarm optimization. Approximate solutions obtained by the applied method have been compared with numerical and approximate methods in literature. The optimization results obtained show that the applied approach can be successfully utilized for approximately solving of longitudinal fins with convex and exponential profiles.

Optimal power flow (OPF) is known as one of the most important planning and scheduling tools in electrical power systems. The OPF problem is a non-convex, NP-hard optimization problem, therefore, the applications of metaheuristic algorithms in the OPF problem have been gained more attentions in recent years. This article investigates successful application of recently developed optimizer, so called, water cycle algorithm (WCA) on the OPF. The WCA, as a metaheuristic optimization method, is inspired by water cycle process in nature. The IEEE 57-bus test system has been taken into account. The obtained optimization results show the better optimal power flow solution using the WCA compared with the other reported optimization approaches. Therefore, the applied WCA can be considered as an alternative approach for tackling OPF.

This paper studies a large-scale urban traffic signal control problem (LUTSCP). A centralized model is developed for describing the LUTSCP in a scheduling framework. The objective is to minimize the total network-wise delay in a fixed time window. We have implemented a recently developed algorithm, so called Jaya, to solve the LUTSCP. The population initialization is based on the four stages of traffic signal in Singapore. A simple new solution generation strategy is proposed to improve the performance of the Jaya. A neighborhood search operator is proposed based on the characteristics of LUTSCP to improve the search performance in local search space. Experiments are carried out using the traffic signal data from Singapore traffic network. The performance of the new strategy for generating feasible solution and the neighborhood search operator are evaluated and discussed. The optimization results obtained by standard Jaya algorithm and its variants are compared to those by existing traffic signal control system. The comparisons and discussions verify that the Jaya algorithm and its variants are superior over the existing traffic light control. In future work, we will compare the performance of Jaya algorithm to existing intelligent algorithms in literature.

This paper researches on the flexible job shop scheduling problem (FJSP) with new job insertion. FJSP with new job insertion includes two phases: initializing schedules and rescheduling after new job(s) insertion. Initializing schedules is the standard FJSP problem while rescheduling is an FJSP with different job start time and different machine start time. The objective is to minimize maximum machine workload. A recently developed algorithm, so called Jaya, is employed to solve the FJSP with new job insertion and a discrete version of Jaya is proposed. Extensive computational experiments are carried out on eight real instances from remanufacturing enterprise. The discrete Jaya is compared to several existing heuristics and ensemble of them for FJSP with new job insertion. The results and comparisons verify that the discrete Jaya algorithm is superior over the existing methods. In future work, we will future improve the performance of discrete Jaya and compare it to more existing intelligent algorithms in literature.

In this paper we present a robust controller based on Takagi-Sugeno (T-S) fuzzy model. First, we obtain the nonlinear state equation of the underactuated robot systems by linearization, then, the parallel distributed compensation scheme (PDC) is applied to design the controller. Simulation results show the robustness of the pendubot control system against the disturbance.

Online shopping has become an essential part of our life, which provides a suitable, cheap, and quick way for customers to enjoy a wide variety of products. However, due to the large number of online stores, a customer usually faces difficulties to review all available offers manually in order to find a favorite item. The Internet shopping optimization problem (ISOP) is a multiple-item multiple-shop optimization problem, which targets to minimize the total cost for a costumer to purchase a given set of products over all available offers. In this paper, the mathematical model of existing ISOP has been improved. In the improved model of ISOP different constraints and assumptions such as the maximum budget, discounts offered by internet shops have been taken into account. Several metaheuristic optimization methods such as the genetic algorithm are implemented. The obtained numerical results illustrate the effectiveness of the improved model and metaheuristics applied.

Several major technical obstacles appear when moving toward more electric aircraft (MEA) architecture. First, there has been an increasing number of power electronic components used in aircraft power systems, leading to the modelling complexity. Second, the number of variables for system modelling increase significantly, leading to high computational complexity. To overcome these difficulties, this report proposes (1) a mathematical model of hybrid AC/DC electrical power systems for MEA architecture; and (2) a distributed power allocation and scheduling strategy based on the Lagrangian relaxation to reduce the computational complexity. Simulation results show that the distributed optimization approach is able to achieve good performance whilst reducing the computation complexity when the scale of an electrical power system increases.

The Flux Switching Machine is one of the novel topologies within the hybrid machine class. It has many advantages such as flux focusing effect, compatibility with simple power converters, high fault tolerance with independent concentrated armature windings, mechanical robustness due to its simple salient pole rotor and high power density. With such attributes the machine is a promising candidate for high speed, high power density applications. It includes toothed rotor with permanent magnet excitation and segmented rotor with field coil excitation. This article reports on comparative studies into the mechanical stress, magnetic flux, back EMF, D axis, Q axis inductance and saliency ratio, loss distribution, efficiency and power density for these topologies via finite element analysis under open circuit and various load conditions. Quantitative simulation results reveal that the segmented rotor with field coil excitation topologies exhibit better electromagnetic performance, among which the 12/7 combination of stator pole and rotor segments exhibits superior EM performances than those of other combinations. Parametric analysis with respect to the aspect ratio and rotor segment arc angle are also performed on 12/7 topology to investigate their relationship with the torque, efficiency and power density.

A novel control method using H∞tracking and integral reinforcement learning is applied to a flux-switching permanent magnet (FSPM) machine in a hostile environment. The proposed controller can maintain high performance in the presence of motor parameter uncertainties and load disturbances. The conventional design procedure for an H∞controller is to solve the Hamilton-Jacobi-Isaacs (HJI) equation which requires full information of the system model. The novel control method, the integral reinforcement learning (IRL) makes use of neural networks to parametrically represent the control policy and the performance of system, and learns the solution of HJI equations online. Therefore, the FSPM machine can work optimally under parameter uncertainties due to different operating conditions. The simulation in Matlab/Simulink vividly illustrates the control performance for a 45kW, rotor speed 9000 rpm, 12/5 poles flux-switching permanent magnet machine.

Congestion in urban areas constitutes an important problem that affects people in explicit but also implicit ways. Current research literature on Urban Traffic Estimation has shown that homogeneous distribution of vehicle density along the links of urban traffic networks plays an important role in the derivation or even the existence of the so-called Urban-Scale Macroscopic Fundamental Diagram or MFD in short. This Urban-Scale MFD can provide information that facilitates the application of perimeter traffic control strategies. In this paper, we implement a partitioning of an urban road network into homogeneous regions based on historical traffic information. Using prior information, we make informed decisions about the selection of the region on which the urban road network is based on, as well as the particular time periods for which the partitioning is to be implemented. We make use of weighted k-means, k-harmonic means and normalized spectral clustering techniques to successfully partition the region into clusters defined by low link density variability, while ensuring that the resulting partitions are spatially cohesive.

This note presents a preliminary study on a high-level model based task programming framework for secure multimodal human-robot interaction in industrial context. The case study of robotic polishing tasks, in particular the task of polishing the whole surfaces of objects of interest, is considered. Our main focus is on system modeling and optimal synthesis of surface coverage trajectories in robotic polishing tasks. Two heuristics are proposed to solve the optimal synthesis problem approximately, based on different considerations and assumptions. We then briefly discuss about polishing tasks other than coverage problem that are specifiable in the language of linear temporal logic and state space reduction heuristics based on learning from expert operators. Finally, we introduce a design of the specification input device for the task specification module.

This work presents a time-weighted finite state automaton modeling formalism for an alternative formulation of multi-commodity flow network problem with some "discrete event features". We introduce a procedure to translate the "multi-commodity flow problem" into a minimum makespan controllable sublangauge synthesis problem in supervisory control theory and use an existing algorithm that computes a finite makespan controllable sublanguage as an approximated algorithm. A simple case study is used to illustrate the application of our model translation procedure.

Due to the competition for limited resources by many concurrent processes in large scale automated manufacturing systems (AMS), one has to resolve a deadlock issue in order to reach their production goal without disruption and downtime. Monolithic resolution is a conventional approach for optimal or acceptable solutions, but suffers from computational difficulty. On the other hand, some decentralized methods are more powerful in finding approximate solutions, but most are application-dependent. By modeling AMS as Petri nets, we develop an innovative distributed control approach, which can create a trajectory leading to a desired destination and are adaptable to different kinds of constraints. Control strategies are applied to processes locally such that they can concurrently proceed efficiently. Global destinations are always reachable through the local observation upon processes without knowing external and extra information. Efficient algorithms are proposed to find such distributed controllers.

In the context of automated manufacturing systems (AMS), Petri nets are widely adopted to solve the modeling, analysis, and control problems. So far, nearly all known approaches to liveness-enforcing supervisory control study AMS with flexible routes whereas little work investigates the ones with synchronization operations. Compared with flexibility, synchronization allows the disassembly and assembly operations which correspond to the splitting and merging to and from different sub-processes, respectively. Such structures bring difficulties to establish liveness condition upon the analysis of the underlying process flows. In this paper, we propose a novel class of systems, which can well deal with these features so as to facilitate the investigation of such complex systems. Using structural analysis, we show that their liveness can be attributed to deadlock-freeness, which is much easier to analyze, detect, and control by synthesizing a proper supervisory controller. Furthermore, a set of mathematical formulations are proposed to describe and extract the corresponding deadlocks. This facilitates the synthesis of liveness enforcing supervisors as it avoids the consideration of deadlock-free but non-live scenarios. The effectiveness and efficiency of this work is verified through examples.

This paper shows that language based top-down supervisor synthesis of Ramadge-Wonham supervisory control theory is in general not feasible. We show this as a direct consequence of the undecidability result of Decomposable Subset problem (and its prefix closed version), which in turn is a corollary of the undecidability result of Trace Closed Subset problem. Essentially, it is in general not possible to decide in finite amount of time whether there exists a string in a regular language such that all those strings indistinguishable from it are contained in the same language. We bring these results to the attention of control community and investigate the decidability status of some other related problems.

This paper addresses adaptive performance management of real-time computing systems. We consider a generic model-based predictive control approach that can be applied to a variety of computing applications in which the system performance must be tuned using a finite set of control inputs. The paper focuses on several key aspects affecting the application of this control technique to practical systems. In particular, we present techniques to enhance the speed of the control algorithm for real-time systems. Next we study the feasibility of the predictive control policy for a given system model and performance specification under uncertain operating conditions. The paper then introduces several measures to characterize the performance of the controller, and presents a generic tool for system modeling and automatic control synthesis. Finally, we present a case study involving a real-time computing system to demonstrate the applicability of the predictive control framework.

In this paper we propose a method to create discrete abstraction of state space behavior for continuous-time systems based on gradient analysis of the system dynamics. Then we describe how to use such a discrete model to design a supervisory controller for a given safety specification for the system. Finally we provide an entropy measure of nondeterminism, which can be used to evaluate the quality of the result discrete model as the degree of nondeterminism in that model.