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In this paper, we investigate the enforcement of opacity via supervisory control in the context of discrete-event systems. A system is said to be opaque if the intruder, which is modeled as a passive observer, can never infer confidentially that the system is at a secret state. The design objective is to synthesize a supervisor such that the closed-loop system is opaque even when the control policy is publicly known. In this paper, we propose a new approach for enforcing opacity using non-deterministic supervisors. A non-deterministic supervisor is a decision mechanism that provides a set of control decisions at each instant, and randomly picks a specific control decision from the decision set to actually control the plant. Compared with the standard deterministic control mechanism, such a non-deterministic control mechanism can enhance the plausible deniability of the controlled system as the online control decision is a random realization and cannot be implicitly inferred from the control policy. We provide a sound and complete algorithm for synthesizing a non-deterministic opacity-enforcing supervisor. Furthermore, we show that non-deterministic supervisors

Product rate and grade transitions are two significant operations undertaken by process plants to operate in uncertain environments. Rate transitions, typically dictated by economic considerations, are often implemented in open loop via a throughput manipulator (TPM). The absence of feedback of product rate to manipulate the TPM results in an offset during rate/grade transitions as well as in presence of feed-side disturbances. In this work, we explore use of a simple PI controller based supervisory control layer that guides the plantwide regulatory layer to achieve product rate targets during grade transition or in the presence of feed grade disturbances. While such a layer cannot incorporate optimal operation and constraint handling explicity as in model predictive control, it obviates the need for advanced process control elements that medium-to-small scale industries can ill-afford The PI based supervisory-regulatory control structure is illustrated using simulated case studies on a prototypical process for diacetone alcohol production comprising of a reactor followed by two distillation columns and a recycle.

In this paper we study a security problem of protecting secrets in discrete-event systems modeled by deterministic finite automata. In the system some states are defined as secrets, each of which is associated with a security level. The problem is to design an event-protecting policy such that any event sequence from the initial state that reaches a secret state contains a number of protected events no less than the required level of security. To solve this secret securing problem, we first develop a layered structure called the security automaton. Then we show that the problem is transformed to a supervisory control problem in the security automaton. We consider two criteria of optimality on protecting policies: (1) disruptiveness, i.e., protecting policies with a minimum degree of disturbance to legal users' normal operations; (2) cost, i.e., protecting policies with a minimal cost. For the optimality on disruptiveness, we prove that a minimally disruptive protecting policy is obtained by using the classical supervisory control theory in the security automaton. For the optimality on cost, we develop a method to obtain a protecting policy with minimal cost by finding a min-cut in the security automaton.

We study the security of control systems in the context of the supervisory control layer of stochastic discrete-event systems. Control systems heavily rely on correct communication between the plant and the controller. In this work, we consider that such communication is partially compromised by a malicious attacker. The attacker has the ability to modify a subset of the sensor readings and mislead the supervisor, with the goal of inducing the system into an unsafe state. We consider this problem from the attacker's viewpoint and investigate the synthesis of an attack strategy for systems modeled as probabilistic automata. Specifically, we investigate the synthesis of attack functions constrained by multiple objectives. First, we study the problem of synthesizing attack functions with the maximum likelihood of successfully reaching an unsafe state. Second, we consider the problem of synthesizing attack functions that have the maximum likelihood of successfully reaching an unsafe state while minimizing a cost function. Our solution methodology is based on mapping these problems to optimal control problems for Markov decision processes

The purpose of this paper is the elimination of interruptions of electrical energy produced by a multi-source production system, so we have assured the feed of DFIG rotor in a wind system by different energy sources (photovoltaic generator, battery group) and switchover between them by auxiliary contacts controlled by a supervisor via a power control algorithm. For the photovoltaic generator, a controller for tracking the maximum power point is designed using direct search approach perturb observe method. For the battery group, the installation of a number of the capacitor battery elements in series and in parallel will be kept the power necessary to supply the DFIG rotor. For the wind system, it contains the power generated by DFIG is controlled by sliding mode system. With regard to switching between electrical energy sources, our proposed system relies on an algorithm to compare the energies produced by each generator with the energy required to feed the DFIG rotor and the energies consumed in the system, where the energy source with high production energy and available is exploited to feed the system and charge the batteries. Simulations were performed to confirm the reliability and efficacy of the system proposed.

This work investigates quantitative supervisory control with local mean payoff objectives on discrete event systems modeled as weighted automata. Weight flows are generated as new events occur, which are required to satisfy some quantitative conditions. We focus on the mean weight over a finite number of events, which serves as a measure to reflect the stability or robustness of the weight flows. The range of events to evaluate the mean payoff is termed the window, which slides as new events occur. Qualitative requirements such as safety and liveness are also necessary along with quantitative requirements. Supervisory control is employed to manipulate the operation of the system so that the requirements are satisfied. We consider two different scenarios based on whether the window size is fixed or not. Correspondingly, we formulate two supervisory control problems, both of which are solved sequentially by first tackling the qualitative issues and then the quantitative ones. The automaton model of the system is then transformed to a two-player game between the supervisor and the environment, where safety and liveness are enforced. Based on the intermediate results, several quantitative objectives are then defined to formulate two games, which correspond to the two proposed supervisory control problems. Finally we synthesize provably correct supervisors by solving the games and completely resolve both problems.

We investigate state estimation and safe controller synthesis for networked discrete-event systems (DES), where supervisors send control decisions to plants via communication channels subject to communication delays. Previous works on state estimation of networked DES are based on the open-loop system without utilizing the knowledge of the control policy. In this paper, we propose a new approach for online estimation and control of networked DES with control delays. We first propose a new state estimation algorithm for the closed-loop system utilizing the information of control decision history. The proposed state estimation algorithm can be implemented recursively upon the occurrence of each new observable event. Then we investigate how to predict the effect of control delays in order to calculate a control decision online at each instant. We show that the proposed online supervisor can be updated effectively and the resulting closed-loop behavior is safe. Furthermore, we compare the proposed online supervisor with the predictive supervisor proposed in the literature and show that our proposed online supervisor is more permissive than predictive supervisor in

In cyber-physical systems (CPSs), it is of great importance to handle network attack issues. In this article, we consider the supervisory control layer of CPSs, focusing on closed-loop control systems vulnerable to sensor-reading modification attacks (SM-attacks), which may disguise the occurrence of an event as a different event by modifying appropriately sensor readings in sensor communication channels. In particular, we consider the plant modeled as a bounded Petri net and the control specification consisting in liveness enforcing. Based on repeatedly computing a more restrictive liveness-enforcing supervisor under no attack and constructing a so-called basic supervisor, a method that synthesizes a liveness-enforcing supervisor tolerant to an SM-attack is proposed.

This work addresses the robust control problem of discrete event systems assuming that replacement attacks may occur, thus making it appear that an event that has occurred looks like another event. In particular, we assume that this is done by tampering with the sensor-readings in the sensor communication channel. Specifically, we use Petri nets as the reference formalism to model the plant and assume a control specification in terms of a Generalized Mutual Exclusion Constraint. We propose three different methods to derive a control policy that is robust to the possible replacement attacks. The first two methods lead to an optimal (i.e., maximally permissive) policy but are computationally inefficient when applied to large-size systems. On the contrary, the third method computes a policy more efficiently, and reveals more easily implementable in practice. However, this is done at the expense of optimality.

A key challenge in the synthesis and subsequent analysis of supervisory controllers is the impact of state-space explosion caused by concurrency. The main bottleneck is often the memory needed to store the composition of plant and requirement automata and the resulting supervisor. Partial-order reduction (POR) is a well-established technique that alleviates this issue in the field of model checking. It does so by exploiting redundancy in the model with respect to the properties of interest. For controller synthesis, the functional properties of interest are nonblockingness, controllability, and least-restrictiveness, but also performance properties, such as throughput and latency are of interest. We propose an on-the-fly POR on the input model that preserves both functional and performance properties in the synthesized supervisory controller. This improves the scalability of the synthesis (and any subsequent performance analysis). Synthesis experiments show the effectiveness of the POR on a set of realistic manufacturing system models.

In industry the performance has to be optimized to allow its competitiveness. The goal then is to use the resources at their maximal capacity, reduce the production time and be flexible to adapt to the customers requests. The closed-loop behavior of a system under the Supervisory Control Theory (SCT) guarantees nonblockingness and safety requirements and, thus, it can be used as the search universe for a planning problem. SCT suffers with the "curse of dimensionality" when systems become bigger and more complex. This paper presents a set of sufficient conditions that allow to work with abstractions of the closed-loop behavior (supremal controllable sublanguage), instead of the closed-loop behavior itself, as the search universe to solve a planning problem. Such abstraction is the natural projection of the supervisor into the set of controllable events satisfying the observer property. This process leads to a reduction of the search space.

International audience; We consider feedback control systems where sensor readings and actuator commands may be compromised by an attacker intending to damage the system. We study this problem at the supervisory layer of the control system, using discrete event systems techniques. The attacker can edit the outputs from the sensors of the system before they reach the supervisory controller as well as it can edit actuator commands before they reach the system. In this context, we formulate the problem of synthesizing a supervisor that is robust against a large class of edit attacks on the sensor readings and actuator commands. Intuitively, we search for a supervisor that guarantees the safety of the system even when sensor readings and actuator commands are compromised. Given the similarities of the investigated problem to the standard supervisory control problem, our solution methodology reduces the problem of synthesizing a robust supervisor against deception attacks to a supervisory control problem. This new and intuitive solution methodology improves upon prior work on this topic.

This article provides a detailed description of a supervisory control problem for a heavy-duty vehicle equipped with a diesel engine and an exhaust aftertreatment system (EATS). The control problem considers the coupling between the engine and EATS and assumes the availability of predictive driving information for a long horizon. The article also proposes a computationally efficient supervisory control algorithm, which is implemented in an existing engine electronic control unit. The algorithm is based on distributed economic nonlinear MPC. It minimizes total fuel consumption (by the diesel engine and EATS) and fulfils Euro VI real-driving and world harmonized transient cycle based NOx emission legislation for the heavy-duty diesel vehicle. The performance of the algorithm is evaluated using a high-fidelity simulation platform, which includes a validated GTPOWER model of a 13 L turbo compound diesel engine and the first-principles model of an EATS. Simulation results show that the supervisory controller has 1.7% less total fuel consumption, and 88.4% less NH3 slip, compared to a baseline controller, to achieve the same real-driving NOx emission.

In our previous paper, we have considered the supervisory control of the probabilistic discrete event systems (PDESs) under the centralized framework. In this paper, we continue to investigate the decentralized probabilistic supervisory control issue of PDESs. The intersection-based control architecture is adopted, and the notion of the probabilistic intersection-based co-observability and its verification algorithm are presented. It is shown that this observability and the probabilistic controllability act as the necessary and sufficient conditions for the existence of the intersection-based decentralized probabilistic supervisors. Moreover, a working example is provided to illustrate the proposed concepts, the obtained results, and the potential application of the proposed control approaches.

Real-time control applications are highly parallelizable and can be used to effectively harness the capacity of a given computing platform when appropriately scheduled. Given a multicore platform for executing a set of parallelizable applications, it is necessary to ensure proper functioning of the system even in the presence of transient processor faults. However, most existing scheduling approaches for parallel applications have been heuristic schemes which are often based only on the satisfaction of a set of sufficiency conditions and cannot take into consideration of all necessary schedulability requirements. Consequently, such schemes lead to suboptimal usage of resources resulting in higher design costs. Formal model-based safe design methodologies such as supervisory control are often desirable in the design of correct-by-construction fault-tolerant schedulers. This article proposes a supervisory control-based fault-tolerant scheduler synthesis scheme for real-time tasks modeled as precedence-constrained task graphs , executing on multicores. Further, we devise search strategies to obtain schedules that 1) maximize fault-tolerance and 2) minimize peak-power (MPP) dissipation. Conducted experiments using real-world benchmarks reveal the efficacy of our scheme.

This article investigates the enforcement of generalized mutual exclusion constraints (GMECs) and deadlock-freeness on a time Petri net (TPN) system with uncontrollable transitions, motivated by the fact that the existing methods enforcing GMECs may degrade the performance of a closed-loop system and lead to deadlock states. A supervisor enforcing a set of GMECs and deadlock-freeness on an underlying untimed Petri net system is assumed to be available. By exploiting timing information and mathematical programming, a control function is designed to restrict the firing intervals of transitions such that a TPN system can avoid entering forbidden states. The key idea behind the proposed approach is the online computation of a graph, called reduced modified state class graph (RMSCG), that is an extension of the partial modified state class graph recently introduced by the authors. Based on the RMSCG, an online control synthesis procedure is developed, which can enforce the originally given GMECs and deadlock-freeness in a maximally permissive way.

In microgrids with droop-controlled inverters, the supervisory controller must the tune the droop gains in real time to maintain stability as the system topology or the number of inverters change. Stability could be compromised due to delays in the dispatch of supervisory control inputs or due to communication link failures. This article proposes a filter deballasting control (FDC) method for maintaining stability in the interim period until the supervisory inputs become available. FDC employs a quasi-open-loop control structure to leverage the inductance of the LC filter as a stability ballast, which under normal operation does not contribute to the dynamic behavior of the system. To further improve the stability, a lead-lag compensator is implemented to compensate the lagging dynamics of the deballasted system. The proposed method does not affect the steady-state active and reactive power share of the inverters, enabling a smooth transition between the conventional and FDC operating modes. The effectiveness of the proposed control strategy is verified through theoretical and experimental means.

This paper studies the linear-time temporal logic (LTL) control of a class of fair discrete-event systems (DESs). It is motivated by the curious extent in which the use of LTL can be strengthened and differentiated in control theory development. Over a fair DES model, a marker-progressive supervisory control problem is formulated in LTL. The problem formulation admits a more flexible specification of multiple markers to distinguish different DES tasks, and seeks to find a supervisor—a passive control function by convention for specified temporal safety—such that a fair DES under its control is guaranteed to make constant progress to these markers. The problem is studied in terms of DES marker-controllability—a new controllability concept formulation of temporal safety for constant marker progress. This new formulation sheds light on how event fairness in DESs coachieves such marker progress with supervision that exists. It is shown that a solution supervisor may be found by canonical LTL verification. Three examples are provided for illustration.

A supervisory control for neutron scattering superconducting magnet is proposed in this paper, which adopts hierarchical control strategy. In the field layer, programmable logic controller (PLC) based on monitoring control and data acquisition system is used for automatic control of various working conditions. PLC and upper monitoring computer are independent of each other in the equipment control layer, and independent operation is not dependent on the upper computer, which ensures the independence and safety of the process. The running parameters of superconducting magnet can be detected in real-time with the coverage of local area network (LAN), and the significant equipment of neutron scattering superconducting magnet can be controlled by using real-time control method in the mobile environment. In case of abnormal situation, the backfeed control will be used to realize remote alarm and other functions, which ensures the normal running of neutron scattering superconducting magnet safely and reliably.

The energy management of the fast-charging stations (FCSs) in power systems, while maintaining the power quality and the grid codes, can be particularly challenging, given the highly dynamic load curve, the heterogeneous PEV charging, and the inconvenience to the FCS customers. In this article, a consecutive horizon-based energy management (CHEM) process is proposed for the optimal techno-economic operation of the FCSs integrated with battery energy storage systems (ESS). The proposed CHEM process employs a central supervisory control and a resilient distributed control, for the energy management of FCSs and the active and reactive power control to maintain the grid codes. As such, the grid operator can realize and direct the economic and technical operation of the FCSs with low computational burden, without the need for real-time collaboration with the FCSs. Moreover, the FCSs can autonomously cope with the volatility of PEV charging with the use of the ESS. Furthermore, a novel Volt/VAr control is proposed to confine the unpredicted voltage deviations, including voltage sags, within an acceptable limit. The heterogeneous PEV charging is modeled using the queuing theory, while the inherent characteristics of the lithium-ion batteries are considered. Through multiple case studies, it is shown that the developed CHEM process can provide lower costs and a leveled active power profile for the FCSs, while ensuring that the voltage deviations are well confined and the convenience of PEV owners can still be met.

We consider feedback control systems where sensor readings may be compromised by a malicious attacker intending on causing damage to the system. We study this problem at the supervisory layer of the control system, using discrete event systems techniques. We assume that the attacker can edit the outputs from the sensors of the system before they reach the supervisory controller. In this context, we formulate the problem of synthesizing a supervisor that is robust against the class of edit attacks on the sensor readings and present a solution methodology for this problem. This methodology blends techniques from games on automata with imperfect information with results from supervisory control theory of partially-observed discrete event systems. Necessary and sufficient conditions are provided for the investigated problem., Comment: Full version of preprint accepted at IEEE Transactions on Automatic Control

Hierarchy is a tool that has been applied to improve the scalability of solving planning problems modeled using Supervisory Control Theory. In the work of Hill and Lafortune (2016), the notion of cost equivalence was employed to generate an abstraction of the supervisor that, with additional conditions, guarantees that an optimal plan generated on the abstraction is also optimal when applied to the full supervisor. Their work is able to improve their abstraction by artificially giving transitions zero cost based on the sequentially-dependent ordering of events. Here, we relax the requirement on a specific ordering of the dependent events, while maintaining the optimal relationship between upper and lower levels of the hierarchy. This present paper also extends the authors’ work (Vilela and Hill 2020) where we developed a new notion of equivalence based on cost equivalence and weak bisimulation that we term priced-observation equivalence. This equivalence allows the supervisor abstraction to be generated compositionally. This helps to avoid the explosion of the state space that arises from having to first synthesize the full supervisor before the abstraction can be applied. Here, we also show that models with artificial zero-cost transitions can be created compositionally employing the new relaxed sequential dependence definition. An example cooperative robot control application is used to demonstrate the improvements achieved by the compositional approach to abstraction proposed by this paper.

The modeling and control of a stand-alone solar photovoltaic with battery backup-based hybrid system is implemented in this paper. Normally, a hybrid PV system needs a complex control scheme to handle different modes of operations. Mostly, a supervisory control is necessary to supervise the change in controller arrangement depending on the applied mode. The proposed system consists of a dc–dc boost converter, a single-phase inverter and a bidirectional dc–dc converter. The PV array is interfaced through a dc–dc boost converter and is controlled with the help of an MPPT controller to extract the maximum power. The battery backup unit is integrated with the PV system through a common dc bus for the power management within the system as well as to maintain a constant dc bus voltage. The power exchange between the battery and dc bus can be controlled by a bidirectional buck–boost converter. The system can be operated with or without battery units, having no alteration of the control configuration, as a result use of complex supervisory controller can be avoided. For simple circuit architecture to be realizable, a control scheme based on sliding mode control is designed for the full-bridge inverter. Its purpose is to maintain a sinusoidal output voltage with small steady-state error, lower total harmonic distortion (THD) and excellent dynamic response under step change in load. Finally, the simulation results from a two-stage 2 kW, 110 V, single-phase stand-alone PV system are provided to confirm the theoretical analysis, effectiveness and feasibility of the proposed control scheme.

Electric transport systems and renewable energy sources (RESs) have recently attracted significant interest, because of the limitations and drawbacks of fossil fuels and the growing demand for utilization of clean energy. Suitable paradigms are needed to manage the atypical and heterogeneous load of electric vehicles (EVs), the intermittent nature of RESs, and the changeability of electrical loads in power networks. The technical and commercial operation of an integrated system comprising an electric transportation system with a battery-powered bus [electric bus (eBus)] charging station and an EV parking lot, integrated with solar photovoltaic (PV) generation, and combined with a battery storage system (BSS), as a virtual energy hub (VEH) is proposed. Moreover, a cooperative decision-making (CDM) strategy is proposed for the VEH, where the active and reactive power flows and the economic operation of the VEH are scheduled using a novel three-stage cooperative control system. A supervisory control system is developed using agents to realize the scheduled CDM of the VEH and to assign the control parameters for the provision of ancillary services. The VEH agent can operate autonomously during the real-time operation to cope with the volatility of the power system based on the optimal predetermined set points from the grid agent. The effectiveness of the CDM methodology is studied and evaluated through different realistic scenarios. The results show that the proposed CDM can organize the effective energy management (EM) with a minimum operational cost to the VEH. Furthermore, because of the designed supervisory control and the provision of ancillary services using the VEH, the grid requirements can be effectively managed.

This article presents a novel framework for the modeling and scheduling of real-time systems (RTS) processing both sporadic and (multiperiod) periodic tasks, based on nonblocking supervisory control of state-tree structures. Instead of assigning static priorities, a real-time scheduling mechanism, namely priority-free conditionally preemptive scheduling, is used to describe the preemption relation among the tasks processed in an RTS. As a dynamic priority scheduling mechanism, partially preemptive or nonpreemptive earliest-deadline first scheduling is also addressed in this article. Finally, the scheduling strategies are illustrated by real-world examples.

This paper presents and evaluates a discrete-event control-based Supervisory Controller (SC) for a grid-connected, battery-enabled, DC Fast-Charging (DCFC) station for Electric Vehicles’ (EVs). The SC enhances operational resiliency of the DCFC station and its host grid by decoupling the dynamics and limiting the steady-state power between the station and its host grid. The charging station includes multiple DCFC units, a Battery Energy Storage System (BESS), a solar-PV unit, and a grid-interface Voltage-Sourced Converter (VSC). The SC (i) limits the station-imported power from the grid based on the grid-imposed requirements, (ii) dynamically dispatches power flow among the grid, PV unit, DCFC units, and BESS to meet charging needs of EVs and when applicable charges BESS or exports power to the grid, (iii) protects the BESS by enforcing its state of charge upper and lower limits and the maximum charge/discharge rates, and (iv) enables the VSC to provide grid ancillary services, if required. Simulation results in PLECS software platform are given to assess the dynamic performance of the proposed SC in response to various discrete events/transitions. The SC algorithm is implemented on a Programmable Logic Controller (PLC) and verified based on real-time Hardware-In-the-Loop approach using an OPAL-RT platform.

A maximally permissive (or optimal) supervisory control of an automated manufacturing system (AMS) modeled by Petri nets (PNs) can be usually implemented by imposing constraints in the form of a set of linear inequalities. To find such a set of linear constraints, in the existing work, an integer linear programming (ILP) problem is generally formulated and solved for some dominant markings obtained by reachability analysis and vector covering theory, which is computationally inefficient due to the combinatorial nature of solving ILPs to decide the coefficients of optimal linear constraints. This paper addresses the deadlock prevention problem for AMSs by developing efficient methods to reduce the computational overhead through the establishment of conditions on deciding the maximal permissiveness of linear constraints imposed on a system. By taking the advantage of structural properties of a PN model under consideration, we identify the most part of minimal covered illegal markings that can be optimally controlled via policies obtained by specific linear inequality constraints. Also, algorithms are developed to implement the proposed policies. The proposed approach can verify the optimality of a linear constraint efficiently without solving ILP problems. A linear programming method is developed to deal with the markings that cannot be processed by the proposed structural analysis. It is shown that for the considered class of PNs, called system of simple sequential processes with resources (S3PR), no mixed ILP problem needs to solve and the computational burden is dramatically reduced. Two examples are employed to demonstrate the efficiency of the developed method.

This paper presents predictive energy management of hybrid electric vehicles (HEVs) via computationally efficient multi-layer control. First, we formulate an optimization problem by considering driveability and a penalty for using service brakes in the objective function to optimize gear, engine on/off, engine clutch state, and power-split decisions subject to constraints on the battery state of charge (SOC) and charge sustenance. Then, we split it into two control layers, including a supervisory control in a higher layer and a local power-split control in a lower layer. In the supervisory layer, a gear and powertrain mode manager (PM) is designed, and optimal gear, engine on/off and clutch states are obtained by using a combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP). Moreover, a real-time iteration Secant method is proposed to calculate optimal battery costate such that the constraint on charge sustenance is satisfied. In the local controller layer, a linear quadratic tracking method (LQT) is used to optimally split power between the engine and the electric machine and keep battery SOC within its bounds.

In practical systems, due to reasons such as sensor limitations, sensor faults, and packet losses in networks, the observation of events becomes nondeterministic. In this article, we extend strong detectability and weak detectability to the case of nondeterministic observations and denote them as A-( $k_{1}$ , $k_{2}$ )-detectability and O-( $k_{1}$ , $k_{2}$ )-detectability, respectively. A-( $k_{1}$ , $k_{2}$ )-detectability says that, for any string, we can distinguish state pairs in the specification for all possible observations of the string. O-( $k_{1}$ , $k_{2}$ )-detectability says that, for at least one string, we can distinguish state pairs in the specification for all possible observations of the string. For A-( $k_{1}$ , $k_{2}$ )-detectability, we construct a transformed automaton and then translate the A-( $k_{1}$ , $k_{2}$ )-detectability problem into the traditional detectability problem that has been solved. We show that A-( $k_{1}$ , $k_{2}$ )-detectability can be used to solve the deterministic supervisory control problem. For O-( $k_{1}$ , $k_{2}$ )-detectability, we construct an augmented automaton that includes all the information of the given automaton and its state estimates. Based on the augmented automaton, we propose a depth-first search (DFS)-based algorithm to check O-( $k_{1}$ , $k_{2}$ )-detectability. Note to Practitioners —Nowadays, practical engineering systems become more and more complex. In these systems, the observation of events often becomes nondeterministic due to reasons such as sensor limitations, sensor faults, and packet losses in networks. Consider a mobile robot as an example. The availability of a sensor output may depend on the current location of the mobile robot. If the mobile robot is in an area where the wireless network is unreliable, the sensor output may not be received by the supervisor. In this article, we investigate the state estimation problem for practical engineering systems under nondeterministic observations within a discrete-event system framework. The results in this article provide not only insights for engineers in the automatic control field to understand nondeterministic observations in practical systems but also the methodology to estimate the current discrete state that is always an important issue. Therefore, we believe that the engineers in the automatic control field should be interested in this article and can benefit from it.

In this article, we consider the problem of synthesizing a nondeterministic supervisor for the plant and the specification modeled as nondeterministic automata. A similarity control problem requires us to synthesize a supervisor such that the supervised plant is simulated by the specification. Ideally, the synthesized supervisor should be as permissive as possible. We develop a method for synthesizing a maximally permissive supervisor that solves the similarity control problem.

Due to the complexity of a multimode plug-in hybrid electric vehicle (PHEV) powertrain, the energy management strategy of the said powertrain is a prime candidate for the application of optimal control methods. This article presents a predictive control strategy for optimal mode selection and powertrain control for a multimode PHEV capable of real-time control. This method utilizes predictions of future vehicle behavior in order to plan an optimal path of vehicle powertrain modes that minimizes energy consumption. This article also presents the integration of the developed optimal mode control strategy with an optimal powersplit strategy using nonlinear model predictive control to create a real-time integrated predictive powertrain controller (IPPC) responsible for all aspects of multimode PHEV powertrain supervisory control. The IPPC provides a real-time optimal solution to address the major challenge of a multimode HEV powertrain control: an integrated discrete and continuous optimization. Testing in simulation has shown the IPPC to be capable of reducing PHEV energy consumption by 4%–10% across real-world and standard drive cycles. In addition, the presented IPPC was deployed onto a rapid prototyping embedded controller where on-road, real-time testing has shown the IPPC to be capable of providing an energy reduction of 5%, thus confirming the energy savings observed in simulation.

The dual active bridge (DAB) is one of the useful interfaces for the bidirectional power exchange between two buses with galvanic isolation. In this article, a design framework is given in the analog domain that completely fulfills the operational features of a DAB under phase angle modulation. This is a cost-effective solution with configurable control parameters and easy to implement using well-established semiconductor integrated circuit (IC). Moreover, it provides an efficient way to study the behavior of power converters. A closed-loop control for bidirectional power exchange is established. For that, a control-to-output current transfer function is derived analytically considering the dc and first-order harmonic terms and verified experimentally. A small scale laboratory prototype of DAB is set up for the validation of the concept. It is shown that the design architecture has the facility for supervisory control that is useful for battery charging/discharging, microgrid application, etc.

Reinforcement learning (RL) is gradually being implemented in the hybrid electric vehicle (HEV) supervisory control. Even though RL exhibits significant fuel consumption saving, the long learning time makes it hardly applicable in real-world vehicles. This study aims to reduce the learning iterations of Q-learning in HEV application utilizing warm-start methods. Different from previous studies, which initiated Q-learning with zero or random Q values, this study initiates the Q-learning with different supervisory controls, and the detailed analysis is given. The results show that the proposed warm-start Q-learning requires 68.8% fewer iterations than cold-start Q-learning and improves 10%–16% MPG compared with equivalent consumption minimization strategy control. The results of this study can be used to facilitate the deployment of RL in vehicle applications.

In low-voltage dc distribution networks (DDNs), the load terminals typically employ voltage-source inverters to provide ac supply to the consumers. These load terminals can potentially experience poor voltage regulation especially in the presence of intermittent photovoltaic sources and uncoordinated charging patterns of plugged-in electric vehicles. Variations in power absorption and generation at different points can potentially create significant voltage drop/rise at the load terminals and may reduce the power quality at the ac side of the loads. In order to mitigate this issue, this article suggests a real-time coordinated supervisory control strategy that estimates the present state of the network and generates a set of references for the controllable sources such that the load voltage deviations are minimized. This article also proposes a power-electronic-converter-based soft open point (SOP) that provides a seamless connection of the remote end and gives an additional degree of freedom to control the circulating current among the feeders. Detailed description and control strategy of the SOP is presented. Steady-state analysis carried out on a three-feeder DDN is presented with and without the proposed SOP. Real-time simulation with an Ethernet-based transmission control protocol/Internet protocol communication system along with experimental results is presented to validate the implementation and effectiveness of the control strategy.

A novel framework is introduced for the supervisory control (SC) of timed discrete event systems, based on Time Petri nets. The method encompasses both logical (markings to reach or avoid) and temporal specifications (arrival and departure times in specific markings). It relies on the construction of a partial forward reachability graph, of the Modified State Class Graph type, and the formulation of integer linear programming problems to establish suitable firing time intervals (FTIs) for the controllable transitions. The SC algorithm provides for each enabled controllable transition the largest FTI that guarantees that the specifications are met, irrespectively of the firing times of the uncontrollable transitions.

In this study, a systematic solution for mobile manipulation with the application of autonomous trolley collection in the airport is presented. The developed system features a novel integrated hardware and software system, incorporating a specially designed manipulator capable of tightly catching the trolley. Particularly, we innovatively present a supervisory control guided coarse-to-fine visual trolley catching strategy in large-scale airport environments. At the coarsest level, an image-based visual servoing method is utilized to approach the trolley from far to near, where a learning-based object detection method is adopted to identify trolleys in the environment in real-time. At the finest level, we present an accurate point cloud registration based object pose estimation method to promote the object manipulation with a position-based visual servoing controller. The tightly integrated system and the subsystems are evaluated through conducting experiments in the trolley collection task and comparing the key module with the state of the art. Extensive experiments demonstrate that the proposed framework can find and fetch the trolley robustly. Notably, the proposed object detection and pose estimation methods exhibit higher accuracy, providing credible perceptual approaches for mobile manipulation. Moreover, the proposed framework can be easily extended to other mobile manipulation tasks for object search and retrieval.

In this article, we study the supervisory control problem of networked discrete event systems with timing structure assuming bounded communication delays and intermittent loss of observations. The communication between the plant and the supervisor is through a network that can have several channels, and so communication delays may change the order of event observations by the supervisor. We assume a priori knowledge of the minimal activation time of the plant transitions and the maximum communication delays, and propose an equivalent untimed model that takes into account all possible observation delays and also loss of observations. Based on this model, we formulate a networked supervisory control problem and show that it can be converted into an equivalent range control problem. We also address implementation, and, to this end, we present a representation for networked supervisors that is able to deal with possible loss of synchronization between event occurrence and its observation.

A set of non-negative integral vectors is said to be right-closed if the presence of a vector in the set implies all term-wise larger vectors also belong to the set. A set of markings is control invariant with respect to a Petri Net (PN) structure if the firing of any uncontrollable transition at any marking in this set results in a new marking that is also in the set. Every right-closed set of markings has a unique supremal control invariant subset, which is the largest subset that is control invariant with respect to the PN structure. This subset is not necessarily right-closed. In this paper, we present an algorithm that computes the supremal right-closed control invariant subset of a right-closed of markings with respect to an arbitrary PN structure. This set plays a critical role in the synthesis of Liveness Enforcing Supervisory Policies (LESPs) for a class of PN structures, and consequently, the proposed algorithm plays a key role in the synthesis of LESPs for this class of PN structures.

This article presents a new droop-based supervisory-level controller for nanogrids (NGs) in the islanded mode of operation. The proposed control system is able to significantly improve the dynamics of power electronic interfaces in NGs. Unlike the existing techniques, the proposed controller takes into account the input-side (i.e., source-side) and the output-side (i.e., load-side) parameters as well as the dc-bus voltage dynamics. Theoretical analysis, simulation results, and experimental results confirm the feasibility of the proposed control system and demonstrate its superior performance.

An ever-increasing number of robotic applications is being created based on the functionalities and the communication facilities offered by the Robotic Operating System (ROS). When applications involve contact with humans, such as in some transportation tasks, a robot supervisory controller shall guarantee that in every possible circumstance safety and task requirements are satisfied. There is no framework provided by ROS to specify supervisory controllers with formal guarantees about the satisfaction of requirements. This is problematic when the complexity of the controller increases, and it is not viable to test every possible scenario during the validation stage of the robotic application. This paper presents novel work that aims at enabling supervisory controller synthesis for ROS-based applications by (a) proposing a novel mapping between concepts from supervisory control theory and ROS, (b) demonstrating that a supervisory control modeling language exists that can be used to describe plant models and to specify requirements and (c) presenting generic finite-state automata models of ROS components, regarded as plant models, which are input for the synthesis procedure. The approach is demonstrated by synthesizing the controller to coordinate state-of-the-art mobile robot navigation modules. Simulations and real-life experiments show that the generated supervisory controller guarantees the correctness of the model with respect to the requirements and that the execution time of a control loop, including the binding code between the supervisory controller and ROS, is on average short enough to be deployed in high-level control loops. The paper is accompanied by a public code repository that contains the implementation of all methods presented in the paper including simulation environments to ease the replication of the results presented.

A basic notion of transparency in automated systems design is the need to support user tracking and understanding of system states. Many usability principles for complex systems design implicitly target the concept of transparency. In this study, we made comparison of a “baseline” control interface mimicking an existing available UAV ground control station with an “enhanced” interface designed with improved functional transparency and usability, and a “degraded” interface which removed important design features. Each participant was extensively trained in the use of one of the interfaces and all simulated UAV control tasks. Each participant was tested in four trials of a typical military concept of UAV operation with different mission maps and vehicle speeds. Results revealed participants using the enhanced interface to produce significantly faster task completion times and greater accuracy across all UAV control tasks. The enhanced features were also found to promote operator understanding of the system and mitigate workload. By defining and setting automation transparency as an overarching design objective and identifying specific transparency and usability issues within existing GCS designs, we weress able to design and prototype an enhanced interface that more effectively supported human-automation interaction. Automation transparency as a high-level design objective may be useful for expert designers; whereas, usability design guidelines, as “building blocks” to transparency, may be a useful tool for new system designers.

In this article, we propose a two-layer-structure called hierarchical basis reachability graph that is useful for marking estimation in Petri nets that contain unobservable transitions. On the basis of a hierarchical partition of the set of observable transitions, a hierarchical basis reachability graph consists of a primary and a secondary structures. Simulation shows that the time-consumption to compute a hierarchical basis reachability graph is in general much less than that of computing the corresponding basis reachability graph. A marking estimation algorithm is proposed by the structural analysis of the corresponding hierarchical basis reachability graph.

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.

The hybrid ac–dc microgrid, consisting of ac subgrid and dc subgrid, is a promising structure to interconnect ac and dc systems to provide high quality and reliable electric energy. However, in single-phase microgrid or unbalanced three-phase microgrid, second-order ripples of the dc subgrid can lead to significant power quality issues. The ripple mitigation function can be added to existing bidirectional dc–dc converters (BDCs) or dc active power filters. Considering these ripple sources can be distributed in the dc subgrid, it is essential to develop ripple mitigation control schemes for distributed BDCs. In this article, a virtual resistor based ripple mitigation strategy is proposed. Autonomous accurate ripple current sharing can be achieved for BDCs installed closely, enabling flexible parallel of ripple mitigation converters. A comprehensive parameter design method is developed to ensure the accurate sharing for closely installed BDCs. For dc subgrid with significant line impedance, the control scheme provides the opportunity for supervisory control to further enhance ripple current sharing and mitigation performance. Both simulation and experimental results validate that the control method and the parameter design method are effective.

We address the problem of flight control in the presence of actuator anomalies. A supervisory control architecture that includes the actions of both a human pilot and an autopilot is proposed to ensure resilient tracking performance in the presence of anomalies. The pilot is tasked with supervisory, higher level decision-making tasks, such as anomaly detection, estimation, and command regulation. The autopilot is assigned a lower level task of accurate command following based on an adaptive control design. The main innovations in the proposed architecture are the use of human pilot in utilizing the concepts of capacity for maneuver (CfM) and graceful command degradation (GCD), both of which originate in cognitive sciences and a judicious combination of the pilot inputs and the autopilot control action. Together, they provide guidelines for a system to be resilient, which corresponds to the system’s readiness to respond to unforeseen events. The supervisory control architecture is shown to be capable of achieving maximum CfM while allowing minimal GCD, as well as satisfactory command following postanomaly, resulting in resilient flight capabilities. The proposed controller is analyzed in a simulation study of a nonlinear F-16 aircraft model under actuator anomalies. It is shown through numerical studies that under suitable inputs from the pilot, the overall controller is able to deliver resilient flight.

In this article, we investigate the synthesis of a (possibly nondeterministic) supervisor, with partial information, such that the controlled nondeterministic plant is bisimulation equivalent to a nondeterministic specification. We derive a new necessary and sufficient condition for the existence of a supervisor and provide an algorithm for the supervisor construction when one exists. The algorithm is exponential in the size of the plant and specification, a substantial improvement over the double exponential cost from exhaustive search. We show that the cases of complete information and deterministic control are special cases of the presented theory.

Neither the existence nor the nonexistence of a liveness enforcing supervisory policy (LESP) for an arbitrary Petri net (PN) is semidecidable. In an attempt to identify decidable instances, we explore the decidability of certain properties of the set of initial markings for which an LESP exists, and the decidability of the existence of a specific class of LESPs. We first prove that for an arbitrary PN structure, determining if there is an initial marking, or there are no initial markings, for which there is an LESP, is not semidecidable. Then, we characterize the class of PN structures for which the set of all initial markings for which an LESP exists is right-closed . We show that testing membership, or nonmembership, of an arbitrary PN in this class of PNs is not semidecidable. We then consider a restricted class of LESPs, called marking monotone LESPs (MM-LESPs). We show that the existence of an MM-LESP for an arbitrary PN is decidable.