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To improve the accuracy of tracking the trunk center-of-mass (CoM) trajectory and foot-end trajectory in a fully electrically driven quadruped robot, an efficient and practical new trajectory tracking control method is designed. The proposed trajectory tracking method is mainly divided into trunk balance controller (TBC) and swing leg controller (SLC). In TBC, a quadruped robot dynamics model is developed to find the optimal foot-end force that follows the trunk CoM trajectory based on the model predictive control (MPC) principle. In SLC, the Bessel curve is planned as the desired trajectory at the foot-end, while the desired trajectory is tracked by a virtual spring-damping element driving the foot-end, meanwhile, the radial basis function neural network (RBFNN) is applied for supervisory control to improve the control performance for the system. The experimental results show that the control method can modify the robot’s foot-end trajectory tracking effect, so that the stability error can be eliminated and the robustness of the controller can be improved, meanwhile, the linear and circular trajectory for CoM can be tracked accurately and quickly.

In this work, a predictive supervisory controller is presented that optimizes the interaction between a diesel engine and its aftertreatment system (ATS). The fuel consumption is minimized while respecting an upper bound on the emitted tailpipe NOx mass. This is achieved by optimally balancing the fuel consumption, the engine-out NOx emissions, and the ATS heating. The proposed predictive supervisory controller employs a two-layer model predictive control structure and solves the optimal control problem using a direct method. Through experimental validation, the resulting controller was shown to reduce the fuel consumption by 1.1% at equivalent tailpipe NOx emissions for the nonroad transient cycle when compared to the operation with a fixed engine calibration. Further, the controller’s robustness to different missions, initial ATS temperatures, NOx limits, and mispredictions was demonstrated.

The paper discusses the development of a supervisory control system enabling automatic stabilization of output parameters of multi-pass heater at a crude distillation unit. The system develops setpoints for base-level flow controllers to optimize the process. System structure is offered, its implementation on the basis of smart controllers with fuzzy logic functionality is described, results of MATLAB simulation are included. [ABSTRACT FROM AUTHOR]

This paper presents models and optimization algorithms to compute the fuel-optimal energy management strategies for a parallel hybrid electric powertrain on a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal and the gear-shift commands. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies including the optimal gear-shift trajectory. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear program and with the one resulting from the implementation of the optimal strategies in a high-fidelity nonlinear simulator. We showcase the effectiveness of the presented algorithm by assessing the impact of different powertrain configurations and electric motor size on the achievable fuel consumption. Our numerical results show that the proposed algorithm can assess fuel-optimal control strategies with low computational burden, and that powertrain design choices significantly affect the achievable fuel consumption of the vehicle. [ABSTRACT FROM AUTHOR]

In this article, the distributed synchronization control problem is investigated for a class of uncertain nonlinear leader-following systems on directed graph, where unknown backlash-like hysteresis exists in the actuator of each follower and the topology of the graph is fixed. Based on basic graph theory, the principle of variable structure control and Lyapunov theory, by using the unknown smooth function approximation capability of type-2 fuzzy logic systems, a new distributed adaptive supervisory control design method is proposed such that all followers asymptotically synchronize to the leader and each synchronizing error is bounded. In the control method, with the help of supervisor control strategy, it is guaranteed that the resulting closed-loop signals including system states, respectively, belong to the corresponding compact sets, which is necessary for fuzzy approximation theory because it is on a compact set that an unknown smooth function can be approximated by fuzzy logic system. Furthermore, the adaptive compensation terms of the optimal approximation errors are adopted to reduce the effects of modeling errors. Finally, simulation results demonstrate the effectiveness of the proposed new design method. [ABSTRACT FROM AUTHOR]

Real-time scheduling strategies for safety-critical systems are primarily focused on ensuring correctness, both functional and temporal. In order to provide the desired predictability in such systems, it is often advisable that all timing requirements be guaranteed offline, before putting the system into operation. Formal approaches allow for all necessary and sufficiency conditions corresponding to a feasible schedule to be checked in a systematic manner. This enables formal approaches to act as effective mechanisms for providing timing guarantees required by safety-critical systems. In this work, we develop a scheduler synthesis framework for the optimal work-conserving scheduling of dynamically arriving, sporadic tasks using a formal approach known as "supervisory control of timed discrete-event systems" (SCTDES). The synthesis process starts with the construction of a resource-constraint-aware task execution model and a deadline-constraint-aware timing specification model, for each task in the given real-time system. The system model (i.e., composite task execution model) is then derived and transformed to guarantee work-conserving co-execution of tasks. Such a work-conserving approach enables the synthesis of schedules which avoid processor idling in the presence of ready-to-execute tasks. Next, we use the (transformed) system and specification models to obtain a supervisor which can be used to construct an optimal scheduler for the given real-time system. Finally, the applicability of the proposed scheme for real-world scenarios is shown by presenting a case study on an instrument control system (ICS). [ABSTRACT FROM AUTHOR]

The advancement in sensing technologies and infrastructure allows real‐time condition monitoring on wind turbines (WTs), which helps improve the power generation efficiency, lower the maintenance costs of wind farms (WFs). Practically, the real‐time measurements could be unavailable at the Supervisory Control and Data Acquisition end due to unintended events such as sensor faults and communication loss, which significantly depreciates the condition monitoring and fault detection performance. Aiming to mitigate the missing data impact on data‐driven WF applications, this study develops a robust anomaly detection approach for WT fault detection using a denoising variational autoencoder. In presence of missing measurements, the proposed approach can not only sustain high fault detection performance but also recover the missing data as an auxiliary function. The proposed approach is tested on a realistic offshore WF and compared with other autoencoder variants and traditional anomaly detection methods. The testing results verify the outstanding robustness of the proposed approach against missing data events and demonstrate its great potential in missing data recovery. [ABSTRACT FROM AUTHOR]

In this paper, we shall formulate and address a problem of covert actuator attacker synthesis for cyber-physical systems that are modeled by discrete-event systems. We assume the actuator attacker partially observes the execution of the closed-loop system and is able to modify each control command issued by the supervisor on a specified attackable subset of controllable events. We provide straightforward but in general exponential-time reductions, due to the use of subset construction procedure, from the covert actuator attacker synthesis problems to the Ramadge-Wonham supervisor synthesis problems. It then follows that it is possible to use the many techniques and tools already developed for solving the supervisor synthesis problem to solve the covert actuator attacker synthesis problem for free. In particular, we show that, if the attacker cannot attack unobservable events to the supervisor, then the reductions can be carried out in polynomial time. We also provide a brief discussion on some other conditions under which the exponential blowup in state size can be avoided. Finally, we show how the reduction based synthesis procedure can be extended for the synthesis of successful covert actuator attackers that also eavesdrop the control commands issued by the supervisor. [ABSTRACT FROM AUTHOR]

This study presents a multidisciplinary end-to-end design, build, and test drive experience of a Formula Society of Automatic Engineers (FSAE) electric vehicle. The design team members’ background include computer engineering, electrical engineering, and mechanical engineering expertise. Each team member worked on a sub-team namely mechanical, drivetrain, supervisory control and data acquisition (SCADA), and battery management system (BMS). The mechanical team modified/redesigned a gasoline FSAE car frame to accommodate the battery pack, cooling systems, electric drive train, and SCADA hardware. The drivetrain team designed a combinational logic-based tractive system control board to enhance the safety and reliability of the drivetrain. The SCADA team used a model-view-controller software architecture and agile development practices to design and implement a custom electric vehicle operating system and the main brain hardware. The BMS team designed the sensing network and communication protocol to provide an update on the state of the health of the energy source. A thorough description of each sub-system is provided herein. System integration and field test results are also included.

In general, machines degrade with use. But, for some manufacturing processes machine ageing can be reversed by processing alternative types of jobs. In the latter case, machines can run longer without breakdowns if job types are balanced and scheduling is optimised. However, when job arrivals are stochastic, even short-term fluctuations in job mixes can increase the risk of breakdown. This paper presents a proof of concept study on job-mix pull control to exploit the age-reversing, healing effect. Because adding pull control will impact the architecture of factory scheduling, three issues are addressed in the proof. First, it is shown that the new architecture would have a better performance than the existing dispatching approach. Second, a method of pulling jobs from upstream to reduce the probability of machine breakdown is developed. The condition of workload imbalance in which job mix control (JMC) should or should not be activated is analysed. Finally, the benefit of JMC is evaluated by using simulation to demonstrate potential improvements that can be achieved. Besides the proof of concept, this study produces an important finding. A lingering cumulative influence of the self-healing effect is discovered, pointing out a new direction for future maintenance scheduling research. [ABSTRACT FROM PUBLISHER]

This paper handles the optimal supervisory control problem of Petri nets (PNs) via two PN structures, namely, weighted and data inhibitor arcs. It is a two-stage method. In the first stage, for each transition that may lead to illegal markings, a set of observer places with weighted inhibitor arcs is used to optimally control the maximal number of marking/transition separation instances (MTSIs) through the proposed integer linear program. Then, the controlled MTSIs are removed from the set of MTSIs. In the second stage, at each iteration, for an MTSI that cannot be controlled at the first control stage, we design an optimal observer place with a data inhibitor arc. This process terminates after all the MTSIs are controlled. The first-stage can sharply lower the computational burden compared with the method by using data inhibitor arcs alone. Finally, a typical example is presented to shed light on this technique. The proposed control strategy can definitely yield an optimal supervisor for any bounded PN on the premise that such a supervisor exists.

In this paper, we propose a method to design an on-line event feedback supervisor (EFS) for a class of Petri nets whose augmented unobservable subnets are acyclic forward synchronization and backward conflict-free (FSBCF) nets. In more detail, an FSBCF net is an ordinary Petri net in which each place has at most one output transition, and each transition has at most one input place. The designed EFS is able to compute a set of transitions that need to be forbidden based on the current observation of the system. In particular, the EFS is maximally permissive, i.e., it ensures that the controlled system never enters into illegal markings while minimally restricting its behavior. Finally, we use an example to illustrate the effectiveness of the proposed method.

Supervisory control of swarms is essential to their deployment in real-world scenarios to both monitor their operation and provide guidance. We explore mechanisms by which humans can provide supervisory control to swarms to improve their performance. Rather than have humans guess the correct form of supervisory control, we use artificial evolution to learn effective human-readable strategies. Behaviour trees are applied to represent human-readable decision strategies which are produced through evolution. These strategies can be thoroughly tested and can provide knowledge to be used in the future in a variety of scenarios. A simulated set of scenarios are investigated where a swarm of robots have to explore varying environments and reach sets of objectives. Effective supervisory control strategies are evolved to explore each environment using different local swarm behaviours. The evolved behaviour trees are examined in detail alongside swarm simulations to enable clear understanding of the supervisory strategies. We conclude by identifying the strengths in accelerated testing and the benefits of this approach for scenario exploration and training of human operators. [ABSTRACT FROM AUTHOR]

Deadlock resolution has been an important research topic in the field of automated manufacturing systems (AMSs). Researchers generally assume that AMS resources never break down whereas only a few resolve the issues of resource failures in the discrete-event supervision of AMSs. In fact, an AMS consists of a number of numerically controlled machines interacting with each other. The failure of resources happens unexpectedly. In this article, we allow parallel routes to use unreliable resources. Because of their powerful modeling capabilities, Petri nets are used to model the considered AMSs. By using a look-ahead control strategy, a robust supervisory control policy is developed for AMSs with assembly operations allowing resource failures. Our objective is to advance parts requiring failed resources in their remaining routes into a special position so as to release shared resources in case some unreliable resources fail. Consequently, those parts not necessarily requiring any failed resource can keep progressing all the time. The conventional methods are on the basis of monolithic and structure-oriented control specifications with centralized supervisors. Our policy can be implemented in a distributed, online, and local way. Several examples are given to elucidate our control policy clearly. Note to Practitioners—In automated manufacturing systems (AMSs), resources such as machines and tools with higher reliablity are always expensive. Sometimes, when it is not cost-effective to use resources with higher reliability, manufacturers may choose some resources with possible failures. These resources are thus considered as unreliable ones in our article. Normally, unreliable resources may fail unexpectedly. Their occurrences can lead a system to stagnation, causing unnecessary downtime, and bringing economic loss to enterprises. To resolve such stagnation issues, we develop a robust supervisory control policy to synthesize a robust liveness-enforcing supervisor for AMSs with assembly operations and unreliable resources. The supervisor can guarantee that a controlled system continues to progress without deadlock and blocking states even if some unreliable resources fail to work. [ABSTRACT FROM AUTHOR]

This article proposes a novel back-to-back competitive learning mechanism (BCLM) for a fuzzy logic (FL) supervisory control system of hybrid electric vehicles (HEVs). This mechanism allows continuous competition between two fuzzy logic controllers during real-world driving. The leading controller will have the regulatory function of the supervisory control system. First, the configuration of the HEV model and its FL-based control system are analyzed. Second, the algorithm of chaos-enhanced accelerated particle swarm optimization (CAPSO) is developed for back-to-back learning of the membership function. Third, based on fuel-prioritized cost functions, the regulation of competitive assessment is designed to select a controller with a better fuel economy. Finally, the competitive performance of using the CAPSO algorithm is contrasted with other swarm-based methods and the BCLM-driven control system is validated by a hardware-in-the-loop test. The results demonstrate that the BCLM control system significantly reduces fuel consumption, at least 9% from charge sustaining and charge depleting based, and at least 7% from conventional FL-based systems. [ABSTRACT FROM AUTHOR]

It has been an active research area to develop robust supervisory control policies for production systems with unreliable resources. So far, most methods for robust deadlock resolution apply only to systems without flexible routes, where each processing step of any part type requires a unique prespecified resource. In this paper, we address deadlock avoidance control problem in production systems with both failure-prone resources and flexible routings, which allow that a part has options when deciding the resource acquisition at each step. This paper presents properties that a controller with robustness must satisfy. Specifically, at any system reachable state, neither the failed resources nor part instances trapped in these resources should have too much detrimental effect on the other portions of the system. Thus, the full range of part types’ production could be assured at all time. After defining the notions of reduced system and reduced state with respect to unreliable resources, we identify and prove conditions for determining whether or not the state resulting from the occurrence of an event is feasible. Subsequently, we develop a method for robust deadlock avoidance, which uses the solutions to state safety checking problem for the reduced production system with only reliable resources. An illustrative example shows the effectiveness of this method. Finally, we conduct a comparison investigation of some representative approaches in the literature about robust supervisory control for deadlock resolution in resource allocation systems with routing flexibility. [ABSTRACT FROM AUTHOR]

This study presents a multidisciplinary end-to-end design, build, and test drive experience of a Formula Society of Automatic Engineers (FSAE) electric vehicle. The design team members' background include computer engineering, electrical engineering, and mechanical engineering expertise. Each team member worked on a sub-team namely mechanical, drivetrain, supervisory control and data acquisition (SCADA), and battery management system (BMS). The mechanical team modified/redesigned a gasoline FSAE car frame to accommodate the battery pack, cooling systems, electric drive train, and SCADA hardware. The drivetrain team designed a combinational logic-based tractive system control board to enhance the safety and reliability of the drivetrain. The SCADA team used a model-view-controller software architecture and agile development practices to design and implement a custom electric vehicle operating system and the main brain hardware. The BMS team designed the sensing network and communication protocol to provide an update on the state of the health of the energy source. A thorough description of each sub-system is provided herein. System integration and field test results are also included. [ABSTRACT FROM AUTHOR]

In the present paper, a hybrid approach that combines modular modelling and Timed Petri Nets model is proposed, the main interest of this combination is to ensure optimal supervisory control of urban traffic flows in two phases intersections of medium-sized cities. Each intersection is considered as a multi-agent system, composed of all agents who represent all physical spaces. The behaviours of interconnected agents are supervised by the Timed Petri Nets model that controls the traffic light cycles according to an optimal timing plan defined by the Continuous Genetic Algorithm. A real two phases intersection in Sidi Bel-Abbes City is presented as an example. The proposed timing plan is compared with the current one using SUMO software for the most important objective functions. Also, the proposed model's behaviour is compared with TSIS-CORSIM software's model, and all obtained experiments results show the effectiveness of the approach and its possibility to be extended and applied to a many-phases intersection. [ABSTRACT FROM AUTHOR]

We present majority-based progressive control in which local supervisors have not only their own private specifications as primary goals, but an additional global specification representing a desirable behavior of the overall system. The control policy follows the majority rule, and the ultimate goal is to achieve a progressive closed-loop behavior whereby the number of local supervisors meeting private specifications increases as the controlled system evolves. For this purpose, we present the notion of majority-controllability of a global specification, and show that it is a crucial condition for the existence of local supervisors achieving a progressive global specification. Using the presented control theory, we analyze how the federal estate tax in the Unites States was repealed in 2010. [ABSTRACT FROM AUTHOR]

A common approach to controller synthesis for hybrid systems is to first establish a discrete-event abstraction and then to use methods from supervisory control theory to synthesise a controller. In this paper, we consider behavioural abstractions of hybrid systems with a prescribed discrete-event input/output interface. We discuss a family of abstractions based on so called experiments which consist of samples from the external behaviour of the hybrid system. The special feature of our setting is that the accuracy of the abstraction can be carefully adapted to suit the particular control problem at hand. Technically, this is implemented as an iteration in which we alternate trial control synthesis with abstraction refinement. While localising refinement to where it is intuitively needed, we can still formally establish that the overall iteration will solve the control problem, provided that an abstraction-based solution exists at all. [ABSTRACT FROM AUTHOR]

Rapid developments of wind industry arise the issue of heavy monitoring tasks. The residual monitoring based on normal behaviour modelling is a highly recommended method when fault record information is missing. However, it is difficult to achieve efficient normal behaviour modelling and dynamic residual monitoring simultaneously. To this end, a novel adaptive fault detection scheme, which merges random forest (RF) with adaptive cumulative sum (CUSUM), is proposed. The authors exploit RF to explore the non-linear mechanism between features and the target variable robustly, and obtain the residuals quickly. Then, they design the adaptive CUSUM control chart of time-varying shift to sensitively detect the changes of residuals. For illustration, they apply the proposed scheme to the supervisory control and data acquisition data acquired from a wind farm in China. The empirical results demonstrate that the proposed scheme is superior to several competing methods in capturing faults and reducing false alarms. Meanwhile, the authors find it can detect anomaly quickly, automatically and robustly under different signal-to-noise ratios. These provide operators sufficient time to adopt an effective maintenance strategy. [ABSTRACT FROM AUTHOR]

This paper demonstrates a real world case study of a new method for robust simultaneous estimation of two parameters using multiple data sources. The method is used to simultaneously estimate vehicle mass and road gradient. No additional sensors are required beyond those that would normally be found on a vehicle controller network. The estimation algorithm combines components driven by observer state error and also directly by the parameter error using a sliding-mode inspired regressor structure. The algorithm incorporates a novel information fusion method that is integral to the regressor structure and a supervised data-rejection system to limit estimation activity in periods of recognised error-promoting activity. The estimation method has been demonstrated in real time on a modified production passenger car platform. It has been shown to be effective at robustly predicting road gradient and offering more reliable and stable prediction of vehicle mass than existing estimation methods employed in the same multi-parameter estimation context. The estimator allows prediction of vehicle mass whose limiting factor is the bandwidth and accuracy of the available driveline torque information, not the algorithm itself, allowing the identification of a 150 kg change in mass on a 2000 kg vehicle in this case study. [ABSTRACT FROM AUTHOR]

This study proposes a novel flexible hybrid state estimation (SE) algorithm when a realistic communication system with its irregularities is taken into account. This system is modelled by the Network Simulator 2 software tool, which is also used to calculate communication delays and packet drop probabilities. Within this setup, the system observability can be predicted, and the proposed SE can decide between using the static SE (SSE) or the discrete Kalman filter plus SSE‐based measurements and time alignment (Forecasting‐aided SE). Flexible hybrid SE (FHSE) incorporates both phasor measurement units and supervisory control and data acquisition‐based measurements, with different time stamps. The proposed FHSE with detailed modelling of the communication system is motivated by: (i) well‐known issues in SSE (time alignment of the measurements, frequent un‐observability for fixed SE time stamps etc.); and (ii) the need to model a realistic communication system (calculated communication delays and packet drop probabilities are a part of the proposed FHSE). Application of the proposed algorithm is illustrated for examples with time‐varying bus load/generation on two IEEE test cases: 14‐bus and 300‐bus. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to test the predictions of the routine-failure trade-off (or lumberjack) model in a full-scope simulator study with expert operators performing realistic control tasks.Background: A meta-study of degree of automation (DOA) studies concluded that DOA predicts task performance under both routine and automation failure conditions, workload, and situation awareness. Empirical support for this conclusion appears to be weak for complex work situations.Method: A full-scope nuclear power plant simulator experiment was conducted in which licensed operating crews completed realistic procedure execution tasks. Dependent measures selected from the lumberjack model were collected and analyzed for systematic effects.Results: Situation awareness increased with increasing DOA, which contradicts the lumberjack model. Anticipated workload and failure task performance effects were not observed.Conclusion: The experimental results add further evidence challenging the applicability of the lumberjack model to complex work situations.Application: Practitioners should use caution when extending the predictions of the lumberjack model based on data from simple work situations to complex work situations. Researchers should invest more resources in testing the predictive power of the lumberjack model in complex work situations. [ABSTRACT FROM AUTHOR]

This paper describes a new maximum power point tracking (MPPT) based on a variable structure "Automatic Systems approach" in order to modify and improve the conventional method perturb and observe. Therefore, the main contribution of this work is related to the developments in advanced control and management system applied to hybrid wind turbines (WT). The objective is to strengthen the extraction capacity of wind power in order to obtain an extended operational range "operating areas II and III." In other terms, the multi-objective optimization on hybrid WT based on sophisticate control system and robust management reliable to hybrid WTs is performed to design a perfect control system. This system requires a very complicated adaptation between the second-order sliding mode control (SOSMC)-based speed control, a novel MPPT technique modified enhanced perturb and observe "MEPO," system limitation and system energy storage. In addition, to improve the quality of the electrical energy introduced into the grid, a new efficient and robust strategy control based on direct power control-space vector modulation (DPC-SVM-SOSMC) implemented in the network side of the converter is proposed. The feasibility and effectiveness of the proposed control system and the new switching algorithm that coordinates the control (the multi-objective optimization) are validated under entire operating range (II and III). The results have showed that, compared with the conventional control system, the proposed scheme provides higher power conversion efficiency, offers better grid power quality and has better dynamic performances as well as a very robust adaptation against the uncertainties of the parameters and external disturbances. [ABSTRACT FROM AUTHOR]

Symmetric discrete-event systems (DESs) are composed of groups of identical components (machines) and buffers. As every component in each group has the same structure, they can be relabeled to a prototype machine. With respect to buffer specifications (prohibiting overflow and underflow) it is shown that optimal supervisory control of the original DES (with many machines) can be reduced to control of the much smaller collection of prototype machines. With buffer sizes fixed, the result is a small invariant supervisor which is independent of the total number of original machines. We analyze the underlying reason for this invariance property and apply the result of the invariant reduced supervisor to efficient reconfiguration triggered by the addition or removal of machines and increase or decrease of the buffer capacity. [ABSTRACT FROM AUTHOR]

With the successful introduction of advanced driver assistance systems, vehicles with driving automation technologies have begun to be released onto the market. Because the role of human drivers during automated driving may be different from the role of drivers with assistance systems, it is important to determine how general users consider such new technologies. The current study has attempted to consider driver trust, which plays a critical role in forming users' technology acceptance. In a driving simulator experiment, the demographic information of 56 drivers (50% female, 64% student, and 53% daily driver) was analyzed with respect to Lee and Moray's three dimensions of trust: purpose, process, and performance. The statistical results revealed that female drivers were more likely to rate higher levels of trust than males, and non-student drivers exhibited higher levels of trust than student drivers. However, no driving frequency-related difference was observed. The driver ratings of each trust dimension were neutral to moderate, but purpose-related trust was lower than process- and performance-related trust. Additionally, student drivers exhibited a tendency to distrust automation compared to non-student drivers. The findings present a potential perspective of driver acceptability of current automated vehicles. [ABSTRACT FROM AUTHOR]

This article presents a supervisory model predictive control system to track the desired speed profile and simultaneously prevent the wheels from slipping in acceleration mode of electrical trains. The proposed control strategy employs field-oriented control (FOC) to control the angular speed of the wheel. Model predictive control (MPC) is used to control the longitudinal velocity of the train to track the desired speed profile and prevent the wheels from slipping by generating the desired angular velocity for the FOC. Since, it is not possible to control the longitudinal velocity and slip ratio independently, a fuzzy supervisor system is proposed to control the train dynamics at the appropriate operating point. A method is presented to estimate train longitudinal velocity and the adhesion coefficient between the wheels and rail surface. These components are vital to implement the proposed method in a real train control system. The closed loop stability of the control system has been studied. Simulations were run under different friction coefficients corresponding to real train parameters to verify the effectiveness of the proposed re-adhesion control system. The simulation results have been compared with the results of other researches to show the feasibility and validity of the presented approach. [ABSTRACT FROM AUTHOR]

State-tree structure (STS) is a powerful tool for the modeling and control of large-scale discrete-event systems (DES) whose structure is organized in a top–down hierarchy. The notorious state-explosion problem can be managed effectively in the framework of STS. Priority is an important concept and exists in a wide range of DES, such as manufacturing systems, traffic systems, and logistic (service) systems. In this article, a conditional-preemption matrix is used to describe the preemption relations among events, which can represent the priority intuitively. In order to augment STS with priority, we propose a novel STS framework: STS with conditional-preemption matrices (STSM), which aims to solve different kinds of issues related to priority. The optimal nonblocking supervisory control is deployed in the framework of STSM. Symbolic synthesis algorithms are implemented and integrated in a software tool, STSLib, which exploits binary decision diagrams as a basis for efficient computation. The developed approach is demonstrated by (a) manufacturing systems using automatic guided vehicles and (b) real-time systems. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm that combines modular synthesis for extended finite-state machines (EFSM) with abstraction of variables by symbolic manipulation, in order to compute least restrictive controllable supervisors. Given a modular EFSM system consisting of several components, the proposed algorithm synthesises a separate supervisor for each specification component. To synthesise each supervisor, the algorithm iteratively selects components (plants and variables) from a synchronous composition until a least restrictive controllable solution is obtained. This improves on previous results of the authors where abstraction is only performed by the selection of components and not variables. The paper explains the theory of EFSM synthesis and abstraction and its algorithms. An example of a flexible manufacturing system illustrates how the proposed algorithm works to compute a modular supervisor. [ABSTRACT FROM AUTHOR]

Integrated continuous manufacturing is entering the biopharmaceutical industry. The main drivers range from improved economics, manufacturing flexibility, and more consistent product quality. However, studies on fully integrated production platforms have been limited due to the higher degree of system complexity, limited process information, disturbance, and drift sensitivity, as well as difficulties in digital process integration. In this study, we present an automated end‐to‐end integrated process consisting of a perfusion bioreactor, CaptureSMB, virus inactivation (VI), and two polishing steps to produce an antibody from an instable cell line. A supervisory control and data acquisition (SCADA) system was developed, which digitally integrates unit operations and analyzers, collects and centrally stores all process data, and allows process‐wide monitoring and control. The integrated system consisting of bioreactor and capture step was operated initially for 4 days, after which the full end‐to‐end integrated run with no interruption lasted for 10 days. In response to decreasing cell‐specific productivity, the supervisory control adjusted the loading duration of the capture step to obtain high capacity utilization without yield loss and constant antibody quantity for subsequent operations. Moreover, the SCADA system coordinated VI neutralization and discharge to enable constant loading conditions on the polishing unit. Lastly, the polishing was sufficiently robust to cope with significantly increased aggregate levels induced on purpose during virus inactivation. It is demonstrated that despite significant process disturbances and drifts, a robust process design and the supervisory control enabled constant (optimum) process performance and consistent product quality. [ABSTRACT FROM AUTHOR]

PCC voltage control is an important key to deal with upcoming challenges of large-scale wind power integration into electric grid. In this paper, based on the derived transfer functions of the plant and controllers, the dynamic performance of three voltage control strategies (centralized, decentralized, and distributed control) is evaluated in terms of robustness to variation of short-circuit ratio (SCR), disturbance rejection (DR), and impact of communication delay. After showing shortcomings of each strategy, an improved voltage control strategy with lower sensitivity to SCR variation and higher capability on grid voltage DR as well as on reactive current dispatch is proposed. The theoretical analysis is validated by simulation results in MATLAB. Finally, a small wind farm is modeled and simulated in RTDS to demonstrate the possible control interaction and reactive current sharing performance of the studied control schemes. [ABSTRACT FROM AUTHOR]

This paper presents an optimal supervisory control technique of urban traffic flows, through a hybrid modeling approach of two phases intersections in medium-sized cities. These last are considered as a multi-agent system, this system is made up of all agents who represent all physical spaces of the urban traffic network. The behaviors of interconnected agents are supervised by Timed Petri Nets that control the traffic light cycles according to an optimal timing plan defined by the Continuous Genetic Algorithm and it can be used to estimate the delay of vehicles. A real intersection in Sidi Bel-Abbes City is presented as an example. The proposed timing plan is compared with the current one using SUMO software for the most important objective functions. Also, the proposed model's behavior is compared with TSIS-CORSIM software's model, and all obtained experiments results show the effectiveness of the approach and its possibility to be extended and applied to a manyphases intersection. [ABSTRACT FROM AUTHOR]

Recently we developed partial-observation supervisor localisation, a top-down approach to distributed control of discrete-event systems (DES) under partial observation. Its essence is the decomposition of the partial-observation monolithic supervisor into partial-observation local controllers for individual controllable events. In this paper, we extend the partial-observation supervisor localisation to large-scale DES, for which the monolithic supervisor may be incomputable. Specifically, we first employ an efficient heterarchical supervisor synthesis procedure to compute a heterarchical array of partial-observation decentralised supervisors and partial-observation coordinators. Then, we localise each of these supervisors/coordinators into partial-observation local controllers. This procedure suggests a systematic approach to the distributed control of large-scale DES under partial observation. The results are illustrated by a system of automatic guided vehicles serving a manufacturing workcell. [ABSTRACT FROM AUTHOR]

We present an algorithm to compute the unique maximally permissive state-based supervisor for any deterministic finite labeled transition system subject to a specification with combined invariance and reachability requirements. The specifications that we consider are expressed in computation tree logic and include specifications with multiple reachability requirements, each of which should always be satisfied. The form of the controller (a state-based supervisor) is purely memoryless, so the control decisions can be made by directly sampling the state of the system that is being controlled, without recording any past event or transition history. The algorithm has been implemented in SynthSMV, an extension of the well-known model-checking solver NuSMV, which uses NuSMV’s efficient implementation of symbolic model checking (based on binary decision diagrams). A case study that involves coordinating the operation of a set of reactors in a chemical plant shows how the methods that we develop apply in practice. [ABSTRACT FROM AUTHOR]

Reactive synthesis and supervisory control theory both provide a design methodology for the automatic and algorithmic design of digital systems from declarative specifications. The reactive synthesis approach originates in computer science, and seeks to synthesise a system that interacts with its environment over time and that, doing so, satisfies a prescribed specification. Here, the distinguishing feature when compared to other synthesis problems in computer science is that the interaction is temporal in that it explicitly refers to a sequence of computation cycles. Supervisory control originates in control theory and seeks to synthesise a controller that – in closed-loop configuration with a plant – enforces a prescribed specification over time. The distinguishing feature compared to other branches of control is that all dynamics are driven by discrete events as opposed to continuous signals. While both methods apparently are closely related, the technical details differ significantly. We provide a formal comparison which allows us to identify conditions under which one can solve one synthesis problem using methods from the other one; we also discuss how the resulting solutions compare. To facilitate this comparison, we give a unified introduction to reactive synthesis and supervisory control and derive formal problem statements and a characterisation of their solutions in terms of ω-languages. Recent contributions to the two fields address different aspects of the respective problem, and we expect the formal relationship identified in this paper to be useful in that it allows the application of algorithmic techniques from one field in the other. [ABSTRACT FROM AUTHOR]