The papers in this special section provide a timely discussion on technical trends and challenges of some classical and emerging issues, such as networked control and filtering, fault detection and tolerant control, event-triggered control, security control, distributed control, sensor networks, and real-time network protocol design, over networks with resource constraints for industrial systems.

One of the main concerns in the design of networked control systems (NCS's) is the inherent delays induced by the networking infrastructure. Rather than considering delays as a threat, delays may be considered as an opportunity for novel controller design procedures, where delays become a design parameter. The benefits may range from easier and cheaper implementations to improved performance. To enable procedures where the explicit specification of a delay may be profitable, alternative models for a discrete-time LTI system with an arbitrary delay used for controller design must be investigated. This paper presents a modeling effort different than the common approach of extending the state-space model with new state variables. The new model is also capable of representing the same system with a delay but using a state-space modeling structure that keeps the original system order. The focus of the paper is to show that both models are equivalent. A direct consequence is that it will be possible to design a controller for a system without delay in such a way that the closed loop system behaves like having a delay. In other words, a relation between desired control performance (via e.g. closed-loop system poles), controller, and delay is established, which provides a new design criterion for NCS.

This research aims to contribute to the analysis of control performance assessment in extractive metallurgy. Productivity-based indices are proposed in addition to current measuring techniques. Such criteria are employed to compare conventional PID and fuzzy-based controllers in copper smelting. This process is mathematically modeled in order to be simulated. The comparison confirms a better performance of the fuzzy controller in dealing with the molten bath temperature within an Isasmelt furnace. In normal operating conditions (online tests), the proposed controller achieves a consistent mean square relative error reduction of 72% between measured values and the temperature setpoint and standard deviation of approximately 60% (from 27.8 °C to 11.1 °C). The productivity criteria establish a lower consumption of raw materials (13%) and energy supply (29%).

The majority of existing Networked Control Systems (NCS) models are discrete-time formulations based on the exact discretization of the continuous-time linear plant over a sample interval. In the state-space formalism, standard control textbooks suggest that a simple approach to model discrete-time LTI systems with time delay shorter or equal than the sampling period is to extend the state vector with a new state variable representing the last control signal, which implies increasing the order of the model by one. In this paper an alternative state-space model for discrete-time LTI systems with time delay is introduced that does not require increasing the order of the model. The relation between both the standard model and the new model is established, and the potential benefits of the new model with respect to the standard one are discussed. Numerical examples illustrate the concepts discussed throughout the paper.

The successful operation of Networked Control Systems (NCS) requires employing appropriate approaches for dealing with network induced time delays, i.e. time intervals elapsed from consecutive sampling and actuation operations. Effective approaches often require to impose periodic execution for the sampling and/or actuation operations, enforcing synchronized constant time delays. And considering that sampling and actuation is performed in different nodes, global time between nodes achieved by clock synchronization is the standard assumption or strategy that enables such synchronized operations. This paper presents a technique that permits to implement these synchronized operations in NCS in the absence of global time. Experimental results corroborate the effectiveness of the presented approach.

This paper presents the schedulability analysis for control messages when networked control loops, built on top of the controller area network (CAN), are dynamically allocating bandwidth in terms of their controlled plants' dynamics. The bandwidth allocation policy is theoretically described by an optimization problem and practically solved by the distributed bitwise arbitration of CAN messages when message identifiers, i.e., priorities, reflect control applications demands. This poses the problem of assessing whether the set of real-time messages will meet their deadlines regardless of run-time priority changes. This is solved by a schedulability analysis based on recent results on worst-case response time techniques for real-time CAN applications. The analysis ends up with the schedulability test for this type of applications.

In many application areas, including control systems, careful management of system resources is key to providing the best application performance. Traditional control systems with multiple control loops statically allocate a fixed portion of the system resources to each controller based on their average or worst-case resource requirements. However, controllers' resource needs vary depending on the jobs they perform and the state of the systems they control. A controller of a plant operating close to its equilibrium requires fewer resources than a controller of a plant operating far from its equilibrium point. The Draco dynamic rate control system exploits this fact by dynamically allocating resources to control systems based on system state. Our research demonstrates that Draco provides significantly better overall control performance with much less resources than static controllers. Our experimental evaluation shows that in the control scenarios we examined Draco provides up to 25% better control performance with 30% less resources.

In recent years, approaches to control performance and resource optimization for embedded control systems have been receiving increased attention. Most of them focus on theory, whereas practical aspects are omitted. Theoretical advances demand flexible real-time kernel support for multitasking and preemption, thus requiring more sophisticated and expensive software/hardware solutions. On the other hand, embedded control systems often have cost constraints related with mass production and strong industrial competition, thus demanding low-cost solutions. In this paper, it is shown that these conflicting demands can be softened and that a compromise solution can be reached. We advocate that recent research results on optimal resource management for control tasks can be implemented on simple multitasking preemptive real-time kernels targeting low-cost microprocessors, which can be easily built in-house and tailored to actual application needs. The experimental evaluation shows that significant control performance improvement can be achieved without increasing hardware costs.

Today there is a significant body of results of resource management in real-time systems. However, most of them are based on "open-loop" strategies and techniques that do not take into account application demands to dynamically adjust their behavior. To overcome this problem by focusing on control applications, we show a novel scheduling technique: large error first (LEF). This scheduling algorithm uses feedback information from each controlled plant by the application in order to assign computing resources to control tasks. The LEF is used in simulation. By comparing the performance of LEF versus classical open-loop scheduling techniques, encouraging simulation results have been obtained. There are still many issues to be addressed with regard to LEF algorithm. This paper also shows these issues, with possible approaches that should be investigated further

In many application areas, including control systems, careful management of system resources is key to providing the best application performance. Most traditional resource management techniques for real-time systems with multiple control loops are based on open-loop strategies that statically allocate a constant CPU share to each controller, independent of their current resource needs. This provides average control performance with minimal overhead but in general fails to provide the best performance possible within the available resources. We show that by using feedback to dynamically allocate resources to controllers as a function of the current state of their controlled systems, control performance can be significantly improved. We present an optimal resource allocation policy that maximizes control performance within the available resources and provide experimental results showing that the optimal policy 1) significantly increases control performance compared to traditional control system implementations (by more than 20% in our experiments), 2) maximizes control performance over other feedback-based policies, 3) saves resources when perturbations occur infrequently, and 4) incurs negligible overhead.

The increasing use of internet in the automation field has brought new challenges in the remote control, supervision and management of industrial processes. Specifically, process data information has to be available anywhere/anytime on the internet. This brings together two main problems: process data abstraction and process data accessibility. In this paper, we present a specification for an environment aimed to virtually model industrial plants, for their remote control, supervision and management. In this environment, we model real industrial plants by means of communication objects called virtual industrial devices (VID). A VID is a software component, which may contain other VIDs and represents, with different degrees of abstraction, an entity of an industrial plant. These objects communicate and are accessible through a peer-to-peer industrial communication protocol that allo15 messaging interchange between virtual industrial devices (and graphic user interfaces) regardless the communication technology that supports the actual messaging.

Closed-loop control systems are dynamic systems subject to perturbations. One of the main concerns of the control is to design controllers to correct or limit the deviation that transient perturbations cause in the controlled system response. The smaller and shorter the deviation, the better the achieved performance. However, such controllers have been traditionally implemented using fixed timing constraints (periods and deadlines). This precludes controllers to execute dynamically, accordingly to the system dynamics, which may lead to sub-optimal implementations: although higher execution rates may be preferable when reacting to perturbations in order to minimize the response deviations, they imply wastage of resources when the system is in equilibrium. In this paper we argue and demonstrate that the responsibility of maximizing the performance of closed-loop systems relies on both the controller designer and the scheduler. We show that the dynamic optimization of the quality of the controlled system response calls for (a) flexible control task timing constraints that deliver effective control performance; flexible constraints allow us to achieve faster reaction by adaptively choosing the controller sampling rate and completion time upon transient perturbations, (b) a quality-of-control (QoC) metric; it associates with each control task timing a quantitative value expressing control performance (in terms of the closed-loop system error), and (c) new scheduling approaches; their goal is to quickly react to perturbations by dynamically scheduling tasks based on the chosen control task execution parameters to maximize the QoC. This combination offers the possibility of taking scheduling decisions based on the control information for each control task invocation, rather than using fixed timing constraints with constant periods and deadlines.

Deals with the use of object oriented technologies in the modeling of industrial plants that have to be remotely monitored and/or controlled. The main goal is to propose a design methodology for obtaining the model of the so-called Virtual Plant, that a remote and certified user will use to access the real plant for performing command actions and/or monitoring. The type of applications and systems targeted must have autonomous behavior and be interconnected and accessible. Reliability, maintainability and flexibility are also required goals, as well as a high rate of reusability of the applications. The methodology has been applied to the remote process monitoring and control of manufacturing processes. The Virtual Plant Model is, in fact, the part of the plant that can be accessed by a certified remote client and thus it can be constituted by a set of sub-models, as many as different types of clients. The Virtual Plant resides inside the Application Server who is responsible of communicating with the cell controller and sends the image of the actual plant state (the Virtual Plant) as required by the remote client (monitoring) or sends the client commands to the cell controller.

The increasing application of flexible and powerful real-time distributed control systems is presently characterizing the industrial automation field. Such systems involve three main disciplines: control systems, real-time systems, and communication systems. Control systems, due their stringent timing constraints, demand real-time computing technology. In addition, communication systems are needed for the data messaging between field devices. We propose an integrated approach to the design and implementation of such systems. We show that by a separate control design and its posterior distributed implementation, the system performance may suffer degradation. That is, when control loops are closed over communication networks, timing problems, as communication induced varying delays, can appear, decreasing the control system performance, and even leading the system to instability. However, we show that by an adequate integrated approach, that takes advantage of control theory, real-time communication properties, an adequate timing analysis, and an appropriate distribution of the control functions, the system performance increases dramatically.

In this paper, we first identify the potential violations of control assumptions inherent in standard real-time scheduling approaches (because of the presence of jitters) that causes, degradation in control performance and may even lead to instability. We then develop practical approaches founded on control theory to deal with these violations. Our approach is based on the notion of compensations wherein controller parameters are adjusted at runtime for the presence of jitters. Through time and memory overhead analysis, and by elaborating on the implementation details, we characterize when offline and on-line compensations are feasible. Our experimental results confirm that our approach does compensate for the degraded control performance when EDF and FPS algorithms are used for scheduling the control tasks. Our compensation approach provides us another advantage that leads to better schedulability of control tasks. This derives from the potential to derive more flexible timing constraints, beyond periods and deadlines necessary to apply EDF and FPS. Overall, our approach provides guarantees offline that the control system will be stable at runtime-if temporal requirements are met at runtime-even when actual execution patterns are not known beforehand. With our approach, we can address the problems due to (a) sampling jitters, (b) varying delays between sampling and actuation, or (c) both-not addressable using traditional EDF and FPS based scheduling, or by previous real-time and control integration approaches.