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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.

An L-150 pilot Jameson flotation cell was instrumented and a distributed control system was developed. The parameters of a metallurgic phenomenological model were estimated from industrial data. A steady state simulator was built based on this nonlinear model. This hybrid system combines on-line measured operating variables with virtual variables, characterizing the feed. All these variables are fed on-line to a simulator to predict the characteristics of the concentrate and tailings. The expert system modifies the set points of the distributed control system, including two routines: expert feedback and feed forward control. Several cases for different feed conditions are discussed.

International audience; In recent years, the growth of renewable energy production has encouraged the development of new technologies, such as High-Voltage Direct Current (HVDC) networks, that enhance the integration of such energy sources to power transmission grids. However, this type of technology introduces new challenges in the way power transmission systems are controlled and operated, as faster and more complex control strategies will be needed in a domain which nowadays relies heavily on human decisions. In this context, Discrete Event Systems (DES) modeling and Supervisory Control Theory (SCT) are powerful tools for the development of a supervisory control to be deployed in the grid. This paper presents an application of the SCT to HVDC grids and proposes an implementation method for the resulting supervisors. The proposed method is capable of integrating decentralized and discrete-event controllers that interact with the continuous-time physical system. The language chosen for the implementation is C code, as it can be easily incorporated in power system simulation software, such as EMTP-RV. The method is validated by the simulation of the start-up of a point-to-point link in the EMTP-RV software.

In this paper we present an online supervisory control approach, based on limited lookahead policy, that is amenable for the control of multi-agent discrete-event systems. We then apply this online control scheme to model and control a warehouse automation system served by multiple mobile robots; the effectiveness of this scheme is demonstrated through a case study. Moreover, we build an experiment testbed for testing the validity of our proposed method with implementation on real robots.

Diesel engines continue to be the prime movers in heavy-duty and non-road applications. Optimising and controlling the engine and its exhaust-gas aftertreatment system (ATS) for low fuel consumption and low pollutant emissions remains a challenging task. Typically, the engine and ATS are calibrated separately and based on engineering experience. In order to exploit the full potential of the overall system, a so-called supervisory control strategy can be used. In this paper, we develop such a strategy based on Pontryagin's minimum principle and show that its performance is close to the globally optimal solution. Compared to similar approaches, our concept does not require the optimal control problem to be solved online. Instead, we derive a simple, causal, and near-optimal control law that is easy to implement and tune. We present experimental results from the engine test-bench to demonstrate the practical feasibility and effectiveness of the proposed approach. Furthermore, we show how the strategy can be adapted online using sensor feedback in order to meet a given emission target during arbitrary and unknown engine operation., Control Engineering Practice, 119, ISSN:0967-0661, ISSN:1873-6939

This paper proposes a design method of the Q-learning based intelligent supervisory control system (ISCS) for optimal operation of the three step kiln process and a new practical method for improvement of Q-learning convergence. First, the Q-learning based intelligent supervisory control system with two layer-structures is designed to find the on-line optimal set-points of control loops for the kiln process. Next, C4.5 is used to extract automatically the operational experience rules of the human operator from the historical data in the lower layer (i.e., process control layer) and the Q-function value is initialized by using the extracted rules in order to determine the optimal initial point of Q-learning in the higher layer (i.e., supervisory control layer). Hence, the convergence rate of Q-learning is extremely accelerated, so that the hierarchical ISCS can replace the human operator in the kiln process in which trial-and-error operation is not allowed. Through simulations and experiments, Q-learning convergence and the stability of the process operation have been evaluated sufficiently under the variable conditions of the states.

Supervisory control theory provides means to synthesize supervisors for systems with discrete-event behavior from models of the uncontrolled plant and of the control requirements. The applicability of supervisory control theory often fails due to a lack of scalability of the algorithms. The paper proposes a format for the requirements and a method to ensure that the crucial properties of controllability and nonblockingness directly hold, thus avoiding the most computationally expensive parts of synthesis. The method consists of creating a control problem dependency graph and verifying whether it is acyclic. Vertices of the graph are modular plant components, and edges are derived from the requirements. In case of a cyclic graph, potential blocking issues can be localized, so that the original control problem can be reduced to only synthesizing supervisors for smaller partial control problems. The strength of the method is illustrated on two case studies: a production line and a roadway tunnel., Comment: Submitted to Journal of Control Engineering Practice, revision 2

Integrated Emission Management (IEM) is a supervisory control strategy that minimises operational costs (consisting of fuel and AdBlue) for diesel engines with an aftertreatment system, while satisfying emission constraints imposed by legislation. In most work on IEM, a suboptimal heuristic real-time implementable solution is used, which is based on Pontryagin's Minimum Principle (PMP). In this paper, we compute the optimal solution using both PMP and Dynamic Programming (DP). As the emission legislation imposes a terminal state constraint, standard DP algorithms are sensitive to numerical errors that appear close to the boundary of the feasible sets. Therefore, we propose two extensions to existing DP methods, which use an approximation of the forward reachable sets to reduce the grid size over time and an approximation of the backward reachable sets to avoid the aforementioned numerical errors. Using a simulation study of a cold-start World Harmonised Transient Cycle for a Euro-VI engine, we show that the novel extension to the DP algorithm yields the best approximation of the optimal cost, when compared to existing DP methods. Furthermore, we show that PMP yields almost the same results as DP, and that the real-time implementable solution only deviates approximately 0.08–0.16% from the optimal solution.

This is the first of a two-part simulation study that discusses the application of a multimode combustion engine in a mild hybrid electric vehicle (HEV). The torque assist, offered by the electric motor, can be used to extend the residence time in the homogeneous charge compression ignition (HCCI) regime, before returning to spark-ignition (SI) combustion. To enable multimode operation in the HEV, the supervisory control strategy has to maintain the battery's state-of-charge while accounting for the SI/HCCI combustion mode switch. In this study four supervisors are discussed which extend the baseline equivalent consumption minimization strategy by the mode switching decision.

Diesel engine cylinder deactivation (CDA) has been demonstrated to provide significant efficiency and aftertreatment thermal management benefits, enabling fuel-efficient emissions reduction from modern diesel engines at low load engine operation. Dynamic cylinder activation (DCA), a variant of CDA where the set of deactivated cylinders varies on a cycle-by-cycle basis, has been demonstrated to enable greater control over driveline torsional vibration while maintaining the fuel efficiency and thermal management benefits shown by fixed CDA via appropriate design of firing patterns. A model-based algorithmic approach to designing firing patterns during DCA – to control driveline torsional vibration in a user-defined frequency range, given firing density, engine speed, and maximum length of firing pattern – is described in this article. The described algorithm is generalizable to any engine configuration including different piston–cylinder layouts and number of cylinders. The algorithm is extended to design firing patterns for constrained DCA operation when CDA hardware is installed on a subset of cylinders of the engine. The resulting optimal firing patterns using the algorithm, for various combinations of inputs, are presented through an experimentally-validated simulation framework and discussed. It is demonstrated that the weighted phase-angle approach can accurately predict the frequencies and relative amplitudes of the vibration content, and, if theoretically possible, the proposed algorithm determines firing patterns that meet the specified requirements. The presented algorithm can be easily extended in future work for simultaneous selection of firing density and firing pattern during online, real-time implementation during both steady-state and transient operating conditions.

This paper presents an integrated energy and emission management strategy, called Integrated Powertrain Control (IPC), for an Euro-VI diesel engine with an electrified waste heat recovery system. This strategy optimizes the CO2–NOxCO2–NOx trade-off by minimizing the operational costs associated with fuel consumption, AdBlue dosage, and active particulate filter regeneration, while satisfying the tailpipe emission constraints. For comparison purposes, the proposed control strategy is applied to different powertrain configurations: with and without waste heat recovery (WHR) system and a WHR system equipped with a battery for energy storage. The potential of each studied configuration is evaluated over the World Harmonized Transient Cycle for cold-start and hot-start conditions. In comparison to the existing Euro VI engine without WHR system, it is shown in simulations that the optimal IPC strategy with an electrified WHR system and battery provides an additional 3.5% CO2 emission reduction and 19% particulate matter reduction, while satisfying the NOxNOx emission constraint.

Supervisory control that ensures deadlock-free and nonblocking operation has been an active research area of manufacturing engineering. So far, most of deadlock control policies in the existing literature assume that allocated resources are reliable. Additionally, a large number of methods are for systems of simple sequential processes with resources (S3PRs), where a part uses only one copy of one resource at each processing step. In contrast, we investigate the automated manufacturing systems (AMSs) that can be modeled by a class of Petri nets, namely S*PUR. S*PUR is a generalization of the S*PR Petri net model, while S*PR is a superclass of S3PR. This work addresses the robust supervision for deadlock avoidance in S*PUR. Specifically, we take into account unreliable resources that may break down while working or being in idle, and the considered AMSs allow the use of multiple copies of different resources per operation stage. Our objective is to control the system so that: 1) when there are breakdowns, the system can continue producing parts of some types whose production does not need any failed resources; and 2) given the correction of all faults, it is possible to complete all the on-going part instances remaining in the system. We illustrate the characteristics of a desired supervisor through several examples, define the corresponding properties of robustness, and develop a control policy that satisfies such properties.

This paper describes the development, implementation, and experimental verification of a supervisory energy management strategy for the vehicle electrical system of a passenger car. The control strategy commands the alternator duty cycle such that vehicle fuel economy is optimized whilst the instantaneous load current demand is met and constraints on the system voltage and battery state of charge are satisfied. The work is based on a control-oriented model of the vehicle electrical system, experimentally validated against vehicle data. Then, a constrained global optimal control problem is formulated for the energy management of the electrical system, and analytically solved using the Pontryagin׳s Minimum Principle (PMP). The optimal solution obtained is evaluated for a range of different driving cycles and electrical load current profiles, leading to the formulation of an adaptive supervisory control strategy that is implemented and tested in vehicle.

International audience; A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting-charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagin's minimum principle-derived optimization tool HOT.

This paper describes a supervisory control strategy for electrical energy transfers in multisource renewable energy systems. The sources are coupled onto a DC bus through DC/DC power converters. The aim is to control the energy transfers, according to the sources power and load variations. The controller determines the operating mode of the system. Then, it calculates the power ratio provided by each source and drives the DC/DC power converters with local current and voltage loops in order to regulate the voltage on the DC bus according to a reference value. The main contributions are to use the duty cycle values of the DC/DC power converters as decision criteria to switch the power sources and drive the power ratios, and to present the complete strategy in a single hierarchical control scheme with three stages. A non linear model of the closed loop system is also detailed in order to work out sufficient conditions for asymptotic stability. Finally, the proposed control scheme is validated with an experimental device developed by GREAH Research Group for the control of energy transfers in multi-source renewable energy systems.

This paper presents supervisory control of a total molten-metal pouring system to improve the productivity of the factory, the safety of workers, and the quality of the product. Through the pouring processes model, a forward tilting control input was calculated by an adaptive feedforward control system to hold the liquid in the sprue cup at a constant level considering the change effected by the accumulating slag in the ladle. A backward tilting input was obtained by means of the hybrid shape approach applied to suppress the slosh. The supervisory control system reasonably switches from the forward tilting motion to the backward tilting motion by using model predictive control to achieve the accurate poured quantity. The validity of the proposed total control system was demonstrated through experiments.

WOS: 000270115500006, In this paper, the real-time supervisory control of an experimental manufacturing system is reported based on a recently proposed hybrid (mixed PN/automaton) approach. Assuming that an uncontrolled bounded Petri net (PN) model of a (plant) discrete event system (DES) and a set of forbidden state specifications are given, the proposed approach computes a maximally permissive and nonblocking closed-loop hybrid model. The method is straightforward logically, graphically and technologically. This paper particularly shows the applicability of a hybrid (mixed PN/automaton) approach to low-level real-time DES control. To do this, programmable logic controller (PLC) based real-time control of an experimental manufacturing system is considered. (C) 2009 Elsevier Ltd. All rights reserved., [TUBiTAK-107E125], This work was supported by the research grant of The Scientific and Technological Research Council of Turkey (TUrkiye Bilimsel ve Teknolojik Arastirma Kurumu-TUBiTAK) under the project number TUBiTAK-107E125. The authors would like to thank the Associate Editor and anonymous reviewers for their thoughtful comments and suggestions that greatly helped to improve the presentation and technical quality of this paper.

The position mooring (PM) for marine vessels contains several controllers, such as heading, damping, restoring and mean force controllers which could be activated or deactivated depending on the environmental and operational conditions. This paper proposes to integrate these controllers into a switching control system. The supervisory control is adopted to facilitate the automatic switching. The supervisory switching control involves the designs of a set of models to track the existing operational condition and the switching logic. Simulations and experiments will be provided for validation. It is shown that the switching control could extend the weather window of the PM system.

The design of a higher-layer controller using model predictive control (MPC) is considered. The higher-layer controller uses MPC to determine set-points for controllers in a lower control layer. In this paper the use of an object-oriented model of the system for making predictions is proposed. When employing such an object-oriented prediction model the MPC problem is a nonlinear, non-smooth optimization problem, with an objective function that is expensive to evaluate. Multi-start pattern search is proposed as approach to solving this problem, since it deals effectively with the local minima and the non-smoothness of the problem, and does not require expensive estimation of derivatives. Experiments in an emergency voltage control problem on a 9-bus dynamic power network show the superior performance of the proposed multi-start pattern-search approach when compared to a gradient-based approach.

The paper presents a case study on the practical implementation of a hybrid expert system for a raw materials blending process (RMBP). Based on blending mechanism and expert knowledge, a hybrid expert system for supervisory control is developed to optimize its performance. With the help of a compensation model and a prediction model, the proposed hybrid expert system can replace the human operator for most of the operations in the process. Both experiment and industrial applications show the feasibility and effectiveness of the developed system, and its bright potential in application of the control of the RMBP.

Discrete-event systems (DES) can be found as essential integrated subsystems in many complex systems, e.g. electrical power systems. Under-load tap-changing (ULTC) transformers which obviously have discrete-event behavior are widely used in transmission systems to take care of instantaneous variations in the load conditions in substations. In this paper, the voltage control problem in ULTC is solved in different modes of operation, using DES-based solutions. These solutions include: DES supervisory control, timed DES supervisory control and a hierarchical structure for the control system. It is shown that the specifications are controllable and the closed-loop control system is non-blocking.

This paper describes a technique that automates the synthesis of supervisory controllers for plants that have a mixture of discrete and continuous dynamics, that is, plants with hybrid dynamics. The control technique utilizes embedded simulations to predict the future system trajectories of a system and then discrete control decisions are made in order to preserve the system's safety. This approach is rooted in the principles of discrete event supervisory control theory, in which the legal behaviour of the controlled system is specified abstractly by a regular language specification, as generated, for example, by a finite state machine.

This paper presents an application of the recently developed theory of language-measure-based discrete event supervisory (DES) control to aircraft propulsion systems. A two-layer hierarchical architecture is proposed to coordinate the operations of a twin-engine propulsion system. The two engines are individually controlled to achieve enhanced performance and reliability, as necessary for fulfilling the mission objectives. Each engine, together with its continuously varying control system, is operated at the lower level under the supervision of a local discrete-event controller for condition monitoring and life extension; the gain-scheduled feedback controller that is used to maintain the specified performance of the turbofan engine is kept unaltered. A global DES controller at the upper level coordinates the local DES controllers for load balancing and health management of the propulsion system.

Due to the state-space explosion, many synthesis and verification problems for discrete event systems cannot be solved using traditional state-space traversal algorithms. This work presents efficient methods for reachability search based on symbolic computations using Binary Decision Diagrams. In addition, simple guidelines and quantities for separating hard and easy reachability problems are presented. Furthermore, the performance of the presented algorithms and heuristics is demonstrated on a set of industrial benchmark examples. It is also shown that such examples are much more challenging than some well-known handmade benchmark models. This is also indicated by the presented hardness quantities.

Increasing level of automation of production systems has lead to reduced numbers of operators and at the same time increase in the number of process information each operator has to supervise and control. This article presents a concept for the design of graphical user interfaces with pictorial elements in the domain of industrial automation. The concept uses techniques known from the three-dimensional computer graphics and information structure based on the operators task to present the production systems adapted to the human information processing. Experiments tested and evaluated the efficiency of this concept.

This paper describes the different steps followed in implementing a temperature controller and a supervisory controller in a Supervisory Control And Data Acquisition (SCADA) system to control a 60 and 160 l reactor in a pharmaceutical factory. These reactors are used to produce the initial quantities of drugs needed to perform clinical tests. The chemical reactions involved are often changed (several times a week). The different steps of the project will be presented: • design of a new heating–cooling system, • design of the predictive and Supervisory Control Algorithm (SCA), • connection of the SCA to the SCADA system, • experimental validation according to Good Manufacturing Practice (GMP). This SCA has been in daily use as the standard control system for more than 2 years.