Search

Process and energy industries have been recognised as adopters of high levels of automation compared to other sectors. Nonetheless, human cognitive input still plays a critical role in the operation of process plants and replication of these cognitive capabilities remains a key challenge for advancing automation levels. In this paper, we provide an analysis of process and energy industries based on a scenario of reduced availability of skilled labour and increased demands for safety, sustainability, and resilience. We consider the different mechanical, sensing, situational awareness, and decision-making tasks involved in the operation of plants and map them to possible realisations of unmanned and autonomous systems. We discuss the implications of current technology capabilities and future technology development perspectives, the factors influencing the complexity of operation in process plants, and the importance of human-machine collaboration. As part of autonomous system capabilities, we consider adaptation as a key capability and we make a connection to adaptation of model-based solutions. We argue that reaching higher and wider levels of autonomy requires a rethink of the design processes for both the physical plants as well as the way automation, control, and safety solutions are conceptualised., IFAC-PapersOnLine, 55 (7), ISSN:2405-8963

A third of the Dutch highways is monitored and controlled by roadside units. These RSUs are safety-critical systems, as they safeguard roadworks and accidents. Therefore, it is essential to have guarantees on the correctness of the roadside unit controller. Using supervisory control theory, a correct-by-construction supervisor is obtained from a model of the system and its requirements. From the supervisor model, a controller implementation can be obtained automatically. There are many roadside units sharing the same basic components, but in different numbers and configurations. To take advantage of this, parametric models are used. Subsequently, these models can be used to create different models for different configurations of an roadside unit. The purpose of this paper is to show the applicability of supervisory control theory to roadside units. To this end, parametrized models of the components of an roadside unit are presented. The models are used to synthesize a supervisor for a specific roadside unit, which is validated using simulation. After validation, a controller implementation is generated from the supervisor model. The implementation is then validated using hardware-in-the-loop simulation.

When mobile robots are employed in transportation tasks involving contact with humans, their control software shall guarantee that in every possible circumstance safety and, in general, task requirements are guaranteed. When control models are manually translated into an executable implementation, it becomes cumbersome to provide such guarantees. Model-driven engineering approaches provide an answer to such a problem. Domain specific models are automatically translated into an executable implementation. Some model-driven engineering approaches exist that are specific to robotics. However, formal guarantees on correctness of the model and the generated implementation with respect to the requirements are, often, not provided. This paper investigates how a general purpose modelling language for supervisory controller synthesis can be used to formally model plants and requirements for a robotic navigation task and can generate an executable implementation that can be integrated into a leading middleware for robotic applications. The starting point is the modelling of the interface provided by existing navigation components available in the targeted middleware. We demonstrate, with simulations and real-life experiments, that the generated supervisory controller is suitable for real-time deployment and guarantees correctness of the model with respect to the requirements of the navigation task at hand. Results on the reaction time of the supervisory controller show that such reaction time is about twenty times smaller than the one of the same supervisory controller implemented with a conventional framework.

We consider the influence that the variable order of Binary Decision Diagrams (BDDs) has on the computational effort that is required for symbolic supervisor synthesis. In recent research it has been shown that improving the variable order can result in a substantial decrease of synthesis effort. We propose the combined use of the Dependency Structure Matrices (DSMs) and the matrix reordering heuristics Cuthill-McKee and Sloan to minimize the Weighted Event Span (WES) of the variable order. This is done by placing variables that often appear together in transition relations near each other. By performing benchmark experiments, we measure the reduction in synthesis effort by utilizing a variable order with minimized WES. The experiments show that our approach is competitive in reducing computational effort compared to FORCE, a state of practice variable ordering heuristic. Moreover, the best improvements in effort reduction are shown for the most computationally demanding models tested.

Supervisory control synthesis is a model-based engineering method to design supervisory controllers for high-tech and cyber-physical systems. Recent advances in synthesis techniques and modelling formalisms allow for synthesis of supervisors for large-scale industrial applications. Yet, the synthesis results depends on the quality and validity of the models used as input. Other model-based techniques such as simulation, testing, and verification provide complementary support in the design process to increase the quality and validity of the models. In this paper, we propose, in addition to the other supporting techniques, eleven modeling aspects to assess the model quality in the context of supervisory control synthesis. Examples of modeling aspects are the interdependency between component models, whether independent subsystems are modeled, and whether the model is annotated with comments. For each modeling aspect, we discuss its importance and describe how it can be quantified. We report on an experiment where 21 models of automated guided vehicles, created by students during a course on Supervisory Control Theory, are evaluated with the proposed modeling aspects. This experiment demonstrates the applicability of the modeling aspects.

In this paper, we exploit the supervisory control theory to synthesize a responsive control logic that assures non-blocking, safety, and time constraints in a manufacturing system subject to uncertain processing times arising from direct human intervention and flexible operations. A modular approach is used to compute a set of timed supervisors and a reduced coordinator that forces machine operations or inhibits human interventions in a minimally restrictive way. A new architecture for structured implementation of timed supervisors in programmable logic controllers is presented and applied to a real-world test-bench.

Supervisory control synthesis is a method to automatically generate a correct-by-construction supervisory controller. Validation of the synthesized controller is an important step to guarantee correct and safe system behavior. Especially requirement validation for systems with numerous components can be a difficult and time-consuming task. This paper proposes a method that reduces the required validation time and effort of systems through symmetry reduction, and is based on the concept of isomorphism. Isomorphism of component models and requirement models means that these models are equivalent in behavior, and therefore only part of the system needs to be validated. This method is used in an industrial case study, in which a supervisory controller is synthesized for a road tunnel (the Koning Willem-Alexandertunnel, the Netherlands). In this case study, the modeling of the plant and the requirements, supervisor synthesis, simulation, and validation are described.

Multi-agent systems have been an effective choice for designing control systems that are flexible and agile. However, few attention has been given to the evaluation of the architectures of such systems. This becomes critical with the emerging requirements in complex domains such as digital network infrastructures. In this paper, we propose an approach for the evaluation of agent-based control architectures and introduce three multi-agent based architectures for the supervisory control of network service operations of the next generation of digital infrastructures. With the proposed approach, we evaluated the architectures and the implemented control systems prototypes under a realistic network infrastructure environment. Our approach has been effective to evaluate the candidate architectures. The results of communication overhead and reaction time, have shown that agent-based hierarchical and heterarchical-ring architectures have outperformed the heterarchical-complete network architecture.

Automated systems require controllers which guarantee machine safety and specified functionality even in case of occurring defects. In literature, several methods can be found for formally deriving a supervisor providing such guarantees, including the existence of failure recovery. In this paper, an extension is proposed so that the derived supervisor not only guarantees the existence of failure recovery, but also enforces a shortest path for it. To this end, a two-step procedure is defined for supervisor derivation, in which two algorithms are involved.

Industrial manufacturing systems are becoming more complex by inserting properties aligned with the precepts of Industry 4.0. In this context, this paper seeks to define a reference model for the Mecatrime platform based on hierarchical interface-based supervisory control (HISC) with a parallel architecture, in order to validate the existing control logic and impose a hierarchy that limits complexity, adds flexibility and facilitates future improvements to the system.

With a structured state space, state-tree structures (STS) are a powerful framework to model hierarchical finite state machines (HFSM). The boundary consistency property of STS endows them a compact and neatly representation. In this study, by naturally decomposing an STS into a set of STS nests in a top-down nested approach and finding a supervisor for each, the boundary consistency property is extended to the supervisory control of STS. As a consequence, the state spaces for both the system model and optimal supervisor are significantly reduced. Two examples are provided, in which the state space of a large scale HFSM example is reduced from 1024 to 2 × 1018.

A survey was conducted to establish the status-quo of industrial flotation control. The survey focussed on the measurements and actuators generally available in industry, the reliability and accuracy of measurements, and how important process variables are controlled. It is evident from the survey that regulatory control is well established with reliable and relatively accurate measurements available throughout a plant. The introduction of froth image analysers seems to gain good traction and enables improved control of mass pull to achieve consistent concentrate grade. Although supervisory control may soon be the new standard for flotation plants, on-line grade optimisation requires further work.

This paper investigates the synthesis of successful sensor deception attack functions in supervisory control using abstraction methods to reduce computational complexity. In sensor deception attacks, an attacker hijacks a subset of the sensors of the plant and feeds incorrect information to the supervisor with the intent on causing damage to the supervised system. The attacker is successful if its attack causes damage to the system and it is not identified by an intrusion detection module. The existence test and the synthesis method of successful sensor deception attack functions are computationally expensive, specifically in partially observed systems. For this reason, we leverage results on abstraction methods to reduce the computational effort in solving these problems. Namely, we introduce an equivalence relation called restricted observation equivalence, that is used to abstract the original system before calculating attack functions. Based on this equivalence relation we prove that the existence of successful attack functions in the abstracted supervised system guarantees the existence of successful attack functions in the unabstracted supervised system and vice versa. Moreover, successful attack functions synthesized from the abstracted system can be exactly mapped to successful attack functions on the unabstracted system, thereby providing a complete solution to the attack synthesis problem.

In control systems, sensor deception is a class of attacks where an attacker manipulates sensor readings to cause damage to the system. Our work investigates quantitative measurements to detect this class of attacks in the context of stochastic supervisory control. We introduce the notion of ϵ-safe systems, which is a first step to generalize qualitative intrusion detection conditions to quantitative intrusion detection conditions. We provide sufficient and necessary conditions to verify if a system is ϵ-safe. Moreover, we provide an algorithm that verifies these conditions, which implies that the problem is decidable.

This paper studies supervisory control of periodic event-triggered control (PETC) systems based on the construction of symbolic abstractions. To this end, we first construct symbolic abstractions for PETC systems, and establish feedback refinement relation from the PETC system to its symbolic models. Here, the constructed symbolic models are represented by the form of discrete event systems (DESs), including extended finite state machines, finite state machines, and classic DESs. With the constructed symbolic models, we study the supervisory control of PETC systems to achieve the desired specification. Since the constructed symbolic models are nondeterministic, we first transfer the symbolic models into deterministic versions, and then verify the existence of the supervisor. Finally, the obtained results are illustrated via a numerical example.

Real-valued clocks make the state space of timed automata (TA) infinite. Conventional supervisory control synthesis techniques are only applicable to finite automata (FA). Therefore, to synthesize a supervisor for TA using conventional techniques, an abstraction of TA to FA is required. For many applications, the abstraction of real-time values results in an explosion in the finite state space. This paper presents a supervisory control synthesis technique applied directly to TA without any abstraction. The plant is given as a TA with a set of uncontrollable events and a set of forcible events that may preempt the passage of time. To obtain a non-blocking controlled system, a synthesis algorithm is proposed that delivers a supervisor (also as a TA) avoiding the blocking states. The algorithm works by (iteratively) strengthening the guards of edges labeled by controllable events and invariants of locations where time progress can be preempted by forcible events.

In this paper we present a heuristic supervisory control scheme for jointly controlling the engine and the after-treatment system (EATS) in heavy-duty vehicles. The proposed controller aims at fulfilling emission legislation constraints without penalizing the fluid consumption and the delivered torque. Compared to existing methods, the proposed control scheme is computationally efficient since it does not require the online execution of iterative algorithms as it is typically done for this class of problems. Moreover, it does not require an accurate model identification of the system nor it requires highly skilled personnel for calibrating its parameters, which are two aspects very appealing in an industrial setting. The effectiveness of the proposed approach is evaluated through simulations where a comparison with existing methods is also performed.

In this work, we investigate the problem of covert attacker synthesis for supervisory control of discrete-event systems. In particular, we consider attackers that exercise both actuator attacks and sensor attacks, where the (partial-observation) attackers may or may not eavesdrop the control commands issued by the supervisor. We develop a reduction from the covert attacker synthesis problem to the Ramadge-Wonham supervisor synthesis problem, which generalizes our previous work on a reduction based approach for covert actuator attacker synthesis. Ministry of Education (MOE) National Research Foundation (NRF) Published version This work is financially supported by Singapore Ministry of Education Tier 1 Academic Research Grant (2018-T1-001-245 (RG91/18)) and the National Research Foundation of Singapore Delta-NTU Corporate Lab Program with the project reference of DELTA-NTU CORP-SMA-RP2, which is gratefully acknowledged.

The fundamental problem in hierarchical supervisory control under partial observation is to find conditions preserving observability between the original (low-level) and the abstracted (high-level) plants. Two conditions for observable specifications were identified in the literature - observation consistency (OC) and local observation consistency (LOC). However, the decidability of OC and LOC were left open. We show that both OC and LOC are decidable for regular systems. We further show that these conditions do not guarantee that supremal (normal or relatively observable) sublanguages computed on the low level and on the high level always coincide. To solve the issue, we suggest a new condition -modified observation consistency - and show that under this condition, the supremal normal sublanguages are preserved between the levels, while the supremal relatively observable high-level sublanguage is at least as good as the supremal relatively observable low-level sublanguage, i.e., the high-level solution may be even better than the low-level solution.

Autonomous surface vehicles (ASVs) are seeing a significant development over the last decade. In recent years, their commercial applications are attracting the attention of many companies. One of the promising subjects is to develop autonomous tugs for ship berthing. This paper focuses on the cooperative control of autonomous tugs for ship towing in a berthing scenario. We propose a multi-layer optimal control strategy for a two-tug towing system to guarantee a ship reaching a desired position with a desired heading and velocity. In the higher layer supervisory control, an optimal control method is used to allocate towing forces and determine towing angles. With the help of these results and geometry relationships, the reference trajectories of the two autonomous tugs can be calculated online. Based on the reference trajectories, the trajectory tracking, which is in the lower layer, is addressed. Simulation results indicate that two autonomous tugs can cooperatively tow the unpowered ship to a desired position with a desired heading and velocity.

This paper exploits the Supervisory Control Theory for the synthesis of minimally restrictive and nonblocking discrete-event supervisors that ensure safety specifications for both the normal and non-routine operations of a PID-controlled industrial process. The discrete-event dynamics of the components of a tank level control system is modeled in such a way that supervisors may indirectly avoid undesired level thresholds by deciding on the occurrence of controllable events of a valve, pump and operator interface. A modular control and coordination scheme is used to avoid state-space growth in synthesis. The reduced supervisors are implemented in PLC that turns the pump on and off and saturates the outputs of a PID-controller on a Foundation Fieldbus only when necessary. The experimental results on a real plant shows that supervisory control may effectively assure a safe behavior during startup, steady state and shutdown with minimal intervention on the process.

In this paper we present a solution to a particular problem from the more general field of construction robotics. The problem we deal with consists of employing one or more mobile robots to autonomously build a user-defined 3-D structure composed of bricks of the same size. Our approach is based on Supervisory Control Theory for extended finite-state machines. The main point of our paper is to communicate our first findings on how the solution to the one-robot scenario could be modified to yield a solution to the multiple-robot scenario. Along the way we examine the scalability, completeness and non-blockingness of our proposed approach and discuss future perspectives.

In this paper, we investigate the supervisory control problem for timed discrete-event systems (TDES) under partial observation. In the timed setting, the system consists of both standard logical events and time event, where the former can be disabled directly by the supervisor if it is controllable while the latter can only be preempted by forcing the occurrences of forcible events. We consider a general control mechanism where the supervisor can choose which events to force dynamically online at each instant. The design objective is to synthesize a maximally-permissive supervisor to restrict the behavior of the system such that the closed-loop language is within a safe specification language. Effective procedure is presented to synthesize such a supervisor. To our knowledge, how to synthesize a maximally-permissive partial-observation supervisor has not been solved for timed DES. We provide a solution to this problem under a general control mechanism.

In this paper, we introduce a new notion of switched supervisory control in the context of discrete event systems. We assume a single event-driven system (plant) controlled by a set of supervisors, where each pair plant/supervisor generates a different event-driven system. A switching mechanism coordinates which plant/supervisor is active at a given time. We investigate the problem of synthesizing a set of supervisors and a switching mechanism such that safety, liveness and maximal permissiveness specifications are satisfied. Sufficient and necessary conditions to solve this problem are presented. Second, a direct application of this new switched supervisory control theory is provided in the context of cyber-security. Namely, this new framework is used as basis for a Moving Target Defense paradigm. Again, sufficient and necessary conditions to solve this problem are presented.

We consider a supervisory control problem of a discrete event system (DES) under partial observation, where a control specification is given by a fragment of linear temporal logic. We design an on-line supervisor that dynamically computes its control action with the complete information of the product automaton of the DES and an acceptor for the specification. The concepts of controllability and observability are defined by means of a ranking function defined on the product automaton, which decreases its value if an accepting state of the product automaton is being approached. The proposed on-line control scheme leverages the ranking function and a permissiveness function, which represents a time-varying permissiveness level. As a result, the on-line supervisor achieves the specification, being aware of the tradeoff between its permissiveness and acceptance of the specification, if the product automaton is controllable and observable., Comment: 7 pages, 2 figures; Accepted for the 21st IFAC World Congress. arXiv admin note: text overlap with arXiv:2003.11808

This paper establishes a connection between supervisory control theory (SCT) and reactive synthesis (RS) in the situation where both the plant and the specification are modeled by *-languages, i.e., formal languages over finite words. In particular, we show that the deterministic finite automaton G typically used in SCT to construct a maximally permissive supervisor f for a plant language L w.r.t. a specification language E, can be interpreted as a two-player game which allows to solve the considered synthesis problem by a two-nested fixed-point algorithm in the µ-calculus over G. The resulting game turns out to be a cooperative Buchi-type game which allows for a maximally permissive solution in the particular context of SCT. This is surprising, as classical Buchi games do not have this property.

Supervisory control synthesis for discrete-event systems can help in overcoming the growing complexity in the process of designing supervisors for cyber-physical systems. This is especially the case when fault-tolerance needs to be taken into account. The aim of this paper is to present a structured way of working that can be used for this purpose. Special attention is given to partitioning plant models and requirement models according to nominal and post-fault behavior. Extended finite-state automata and state-based requirement models are used as the modeling formalism. A case study involving a movable bridge (state space size: 8.4 × 1025) is presented to illustrate the proposed method.

The paper extends the hybrid control for the continuous radiopharmaceuticals production processes to supervisory control in order to add new functionalities and behavioural modes: optimal offline planning of the production process, adjusting on line the parameters of the process in current execution, and conditioning the process execution by the evolution of the environment context. The present research demonstrates the possibility of applying directly the holonic control paradigm to hybrid supervised control of continuous processes, exemplified by the production of radiopharmaceuticals. The design of an intelligent control solution for this type of processes uses the holonic hybrid supervised control architecture with optimally planned demand sequence which groups demands into commands and orders which are derived from a master product recipe. Implementing solutions are provided.

Conventional supervisory control synthesis techniques are not adequate anymore when a network between the plant and the supervisor introduces communication delays. This paper presents a method to synthesize a networked supervisor handling delays in both observation and control channels. To deal with the problem of delayed observations, we propose an automaton modeling the behaviour of the plant observed by a supervisor through a network, called observed plant. In this automaton, events observed by a supervisor are delayed from those occurring in the plant. Moreover, since observation channels are considered not to have the first in first out (FIFO) characteristic, events may not be necessarily observed in the same order as they occurred within the plant. A safe, observable, controllable and nonblocking supervisor is synthesized for the observed plant by means of an adapted synthesis algorithm for timed discrete-event systems (TDES). By enabling the achieved supervisor to predict the effects of control delays, it will be further transformed to a networked supervisor. The networked supervisor makes decisions ahead of time to ensure that the commands will be applied on the right (plant) state.

Distinguishers have been used in Supervisory Control Theory (SCT) of Discrete Event Systems, as a way to simplify modeling tasks. Approximations complement this approach with an alternative to also reduce synthesis effort. Despite the advantages, supervisors obtained from approximated DES plants can be suboptimal, so that the modeling structure has to be reconstructed empirically, until the proper supervisor is found. In this paper we show how to construct suitable-approximations that always lead to least restrictive controllable synthesis. A manufacturing system example is provided to illustrate the approach.

Reactive synthesis (RS) and supervisory control theory (SCT) both provide a design methodology for digital systems. RS takes a computer science perspective and seeks to synthesise a system that interacts with its environment in computation cycles and which, doing so, satisfies a prescribed specification. SCT takes a control theoretic perspective and seeks to synthesise a controller that - in closed-loop configuration with a plant - enforces a prescribed specification, where all dynamics are driven by discrete events. While both synthesis techniques seem superficially very similar, their technical details differ significantly. We provide a formal comparison allowing us to identify conditions under which one can solve one synthesis problem via the other one and we discuss how the resulting solutions compare. To facilitate this comparison, we give a unified introduction to RS and SCT and derive formal problem statements and a characterisation of their solutions in terms of ω-languages. As recent contributions to the two fields focus on different aspects of the respective problem, our translational theorems can be used to guide the application of algorithmic techniques from one field in the other.

Cyber-physical systems (CPSs) integrate computing and communication capabilities to monitor and control physical processes. In order to do so, communication networks are commonly used to connect sensors, actuators, and controllers, implemented, in general, on Programmable Logic Controllers (PLC). The use of communication networks increases the vulnerability of the CPS to attacks that can drive the system to unsafe states. We propose in this paper a defense strategy that prevents damages caused by cyber attacks in the sensor and/or actuator communication channels of supervisory control systems, without changing the behavior of the closed-loop system when it is not attacked. We introduce the definitions of DNA-and UNA-Security, and present necessary and sufficient conditions to verify these properties.

The manual design of monolithic controllers for flexible manufacturing systems is no longer feasible due to the sheer size of the problem. A well-known approach to tackle this scalability problem is to create a set of smaller controllers and orchestrate their interaction in an architecture. Another approach is to use synthesis techniques to generate a controller model from models of the uncontrolled system and the formalized requirements. In this paper we describe a pragmatic approach that combines the complementary advantages of these two approaches, where we decompose the design problem of the controller into a number of sub-controllers by introducing intermediate interfaces and use supervisory controller synthesis to synthesize the sub-controllers. We have evaluated this approach on an industrial case study, where we examined a large controller in a lithography machine. We found that the approach can successfully be used to generate a large portion of the needed sub-controllers.

l-complete approximation is a discrete abstraction method for a specific class of hybrid control problems involving purely discrete specifications. It allows for global refinement if the subsequent synthesis of supervisory control should fail for the currently selected abstraction level. In this paper, we present a methodology of local refinement for l-complete approximations. If the strongest l-complete approximation of a given hybrid system does not guarantee the existence of a suitable supervisor for a given specification, the proposed scheme refines the abstract model only in a local set of states that violate a controllability condition. Compared to the standard unfocused and global refinement procedure, this may significantly reduce the computational burden both in the abstraction step and the subsequent controller synthesis step.

In this paper we propose a method to compute a monitor-based supervisor that enforces a GMEC in labelled Petri nets. Due to the presence of indistinguishable transitions, an observable GMEC may not be enforceable by a monitor place in general. To solve this problem we introduce the notion dependency of transitions that can be used to judge whether an event is generated by an input/output transition or its spurious ones, and we present an algorithm to design a monitor-based supervisor based on the notion of Hilbert basis. This approach is purely structural with low online computational cost. Moreover, our supervisor is robust to some change of initial markings or irrelevant part of the plant net.

This paper presents an economic nonlinear MPC based strategy for integrated control of the engine and the exhaust aftertreatment system (EATS) of a heavy-duty powertrain. The objectives of this control strategy are to improve the fuel economy while fulfilling real-driving emission legislation based on work-based-window, as stipulated in the Euro VI emission legislation for heavy-duty vehicles. The strategy is evaluated using a high-fidelity simulation platform including a validated GT-POWER model of a 13 L turbo compound diesel engine and a first principles model of an EATS. Simulation results show that the strategy always fulfills the real-driving emission legislation.

In this paper we study multi-agent discrete-event systems where the agents can be divided into several groups, and within each group the agents have similar or identical state transition structures. We employ a relabeling map to generate a "template structure" for each group, and synthesize a scalable supervisor whose state size and computational process are independent of the number of agents. This scalability allows the supervisor to remain invariant (no recomputation or reconfiguration needed) if and when there are agents removed due to failure or added for increasing productivity. The constant computational effort for synthesizing the scalable supervisor also makes our method promising for handling large-scale multi-agent systems. Moreover, based on the scalable supervisor we design scalable local controllers, one for each component agent, to establish a purely distributed control architecture. Three examples are provided to illustrate our proposed scalable supervisory synthesis and the resulting scalable supervisors as well as local controllers., 14 pages, 12 figures

With recent developments in autonomous cars, route based optimisation is closer to reality. Penetration of such connected cars technology provides potential for optimisation of fuel consumption. In this paper, look ahead prediction is used along with lumped parameter based models to develop a supervisory controller for a light duty diesel engine. A supervisory interface proposed in earlier works for a light duty diesel engine with LNT-SCR aftertreatment system is used. The supervisory controller is designed with the objective of non-interference of local controllers. However, the ability to influence the subsystem with a system objective is maintained. The look ahead prediction comprises of vehicle speed and load trajectory. A set of discrete control actions are evaluated for the complete powertrain to determine the optimal action with respect to equivalent fuel consumption. The use of the simple models along with discrete control actions has low computational effort. After a full factorial simulation of the discrete actions carried out on-line, the optimal supervisory control action is chosen by selecting the action with least fuel consumption. The simulation results utilising the proposed controller is analysed for fuel equivalent consumption saving potential.

In this paper, we outline a method for carrying out efficient (max, +) matrix multiplication when using the heaps of pieces framework. We present an algorithm for multiplying an arbitrary m by r matrix X by a r by r heaps of pieces matrix M, making it possible to calculate the resulting matrix in worst case time complexity O(mr), rather than O(mr2) which is required when using the matrix multiplication definition. We also give an algorithm for multiplying M by an arbitrary r by n matrix X with worst case time complexity O(nr). Finally, we consider a variant of the standard heaps of pieces model, and present an improved matrix multiplication algorithm for this variant as well. Published version

This paper demonstrates the use of model checking to solve the problem of optimal task scheduling in a flexible manufacturing system. The system is modelled as a discrete event system, for which the least restrictive safe behaviour is synthesised according to supervisory control theory. Then timing constraints are added to the model in the form of extended finite-state machines, and time-optimal schedules are computed using the discrete event systems and model checking tool Supremica. In the case study considered in this paper, which previously was only solved heuristically, the method successfully produces optimal schedules to manufacture up to 30 products of two different types. The method is furthermore used to find an optimal cycle, solving the scheduling problem of the case study for an arbitrary number of products in optimal or asymptotically close to optimal time.

In our previous work (Atampore et al., 2016b), we developed a novel supervisory control framework for automated composition of Web services. In the proposed framework, we modeled services that exchange messages and exhibit nondeterministic (runtime-dependent) behaviours based on runtime input. The objective is to synthesize a supervisor that interacts with a given set of Web services through messages to guarantee that a given specification is satisfied. The framework employs Labelled Transition Systems (LTSs) equipped with guards and data variables to model Web services and provides a technique to synthesize a controller. We modeled the interactions of services asynchronously and we used the guards and data variables to express certain preconditions which are then propagated from the system requirements through the overall composite service. In this paper, first, we present a prototype implementation of our automated web service composition framework proposed in our previous paper into a tool support: The implementation involves the following: (i) the translation of industrial Web service standard such as WS-BPEL in a formal language suitable for control synthesis; (ii) the implementation of all the proposed control synthesis algorithms in our previous work; (iii) the incorporation of runtime information into the supervisory control synthesis; and (iv) the translation of the formal language used in the synthesis back to its WS-BPEL equivalent. Second, we present the application and a preliminary evaluation of the proposed composition approach in terms of (i) its effectiveness for the generation of controllers for a composition problem and (ii) its applicability using two well-known small case studies.

Current production systems are becoming more complex: manufactured products are increasingly technical, production components are more specific, and control specifications are rapidly changing. Thus, formal methods and tools are becoming essential to support the automated development of control systems. We propose to develop a tool for the synthesis and implementation of modular/distributed supervisory control for Automated Manufacturing Systems (AMS). To reduce the computational complexity, we divide the control problem into local and global controls. Local Controllers (LCs) are designed for the individual subsystems, then global dependencies are added to the LCs to cooperatively execute the control actions. The tool provides a distributed control, interpreted as Grafcet (standard IEC 60848), that can be lately converted into any suitable IEC 61131-3 standard programming language for PLC programming purposes. It is based on Model-to-Model (M2M) transformations implemented in an Eclipse Modeling Framework (EMF) environment.

In this work, a metallurgical simulator, developed in a previous project, was implemented to complete the operation of a L-150 pilot Jameson flotation cell, available at the Process Control Laboratory. The cell was instrumented and its distributed control system includes the pulp to air ratio, the froth depth and the wash water flow rate controllers. A fraction of the tailings can be recycled in order to keep a constant flow rate to the downcomer, independently of the fresh feed flow rate. This hybrid system combines the real operating variables of the pilot plant (feed flow rate, air flow rate, froth depth, wash water flow rate, downcomer pressure and gas hold up) with virtual variables, characterizing the feed (solids per cent, particle size distribution by classes, grades of mineralogical species by classes). All these variables are fed to a simulator to predict the characteristics of the concentrate and tailings (mass flow rate, grades, particle size distribution). The expert control strategy developed is based on the maximization of the cell Cu recovery subject to the technical specifications of the produced concentrate are met. To achieve this, the quality of the concentrate and tailings, predicted by the metallurgical simulator, are monitored on line. The cell recovery is estimated when an on line steady state test is passed. The expert system modifies the set points of the distributed control system. The supervisory control includes two main routines: expert feedback control, acting whenever a steady state is reached, and expert feed forward control, to compensate for measured disturbances on the characteristics of the feed. Several cases for different feed conditions are discussed and evaluated.

The joint position constraints of two cooperative robotic manipulators is a critical issue, especially when both manipulators grasp and move the same object. When two robots cooperate to move a grasped object from one point to another one, following a desired path, the general solution adopted in the literature is to use the kinematic redundant motions possessed by a single manipulator, in order to avoid joint constraints. However, this method requires a number of redundant motions at least equals to the number of joint constraints to satisfy. The study proposed in this paper is focused on developing a joint position limits avoidance strategy, which is able to satisfy all joint limits even when the number of redundant motions are no longer sufficient to ensure them with a classical approach. This is achieved by means of a supervisory control system, which allows to locally and temporary change the desired end-effectors motion, when the redundant motions are not available. The supervisory control sacrifices the path following task in order to ensure joint position limits avoidance, while preserving at the same time the relative end-effector motion, such that the stability of the grasped object is still ensured. The proposed strategy has been implemented on a commercial robot, namely Baxter, which possesses two anthropomorphic arms having one degree of redundancy. The obtained results prove the effectiveness of the proposed supervisor controller, which can be easily extended and applied in manufacturing scenario. Whereas path following can be temporary sacrificed (e.g., the grasped object can be moved from one point to another in different ways), the joint constraints must be strictly preserved for safety reasons.

Complex mineral raw materials processing is an essential operation in modern industry. Innovative technologies help in constant improving of the efficiency and performance. The paper presents a design of electromagnetic mill, its technological structure and control system. In comparison to conventional solutions, the electromagnetic mill is a fully automated system. It operates in more effective way. The proper selection of the physical and process parameters of the system is crucial for efficiency of the process. The device stands out because of its low energy consumption. The air stream ratio model has been presented for the dry grinding and classification unit with the electromagnetic mill. The concept of the grinding system is described along with indirect measurement methodology. Supervisory control algorithm based on the model structure is derived.

For the shift of the energy grid towards a smarter decentralised system flexible microscale trigeneration systems will play an important role due to their ability to support the demand side management in buildings. However to harness their potential modern control methods like model predictive control must be implemented for their optimal scheduling and control. To implement such supervisory control methods, first, simple analytical models representing the behaviour of the components need to be developed. At the Institute of Energy System Technologies in Offenburg we have built a real-life microscale trigeneration plant and present in this paper the models based on experimental data. These models are qualitatively validated and their application in the future for the optimal scheduling problem is briefly motivated.

Commercial refrigeration systems consume a substantial amount of electrical energy, resulting in high indirect global warming impact due to greenhouse gases emissions. The multitude of different system configurations, system complexity, component wear, and changing operating conditions make efficient operation of this kind of refrigeration systems a difficult task. This paper presents an investigation of machine learning for supervisory control of a supermarket refrigeration system. In particular, a reinforcement learning algorithm for a CO2 booster refrigeration system is designed by exploiting the Matlab-based “SRSim” simulation tool. The reinforcement learning controller learns to operate the refrigeration system based on the interaction with the environment. The analysis shows that learning control is a feasible model-free technique to find a suitable control strategy for demand-side management in a smart grid scenario.

This brief history summarizes the ‘supervisory control of discrete-event systems’ as it has evolved in the period 1980-2015. Overall, the trend has been from centralized or ‘monolithic’ control to more structured architectures, and from ‘naive’ to symbolic computation. Like any ‘history’ this one represents the perspective of the authors; inevitably some important contributions will have been overlooked or short-changed.

We work with the case study of an industrial glass bonding system for truck cabins that presents interesting aspects of timing to be investigated. Specifically, the coordination of the different equipments must start and wait for the ending of several processes, and a maximal processing time must be respected. Monolithic and local modular supervisors are synthesized using the timed discrete event systems control approach of Brandin and Wonham (1994). By using these tools, we’ve found a minimal time for production in steady state that complies by far the maximal production time.