A Low Energy FPGA Platform for Real-Time Event-Based Control

We present a wireless sensor node suitable for event-based real-time control networks. The node achieves low-power operation thanks to tight clock synchronisation with the network master (at present we refer to a star network but extensions are envisaged). Also, the node does not employ any programmable device but rather an FPGA, thus being inherently immune to attacks based on code tampering. Experimental results on a simple laboratory apparatus are presented

Model predictive control with dynamic move blocking

Model Predictive Control (MPC) has proven to be a powerful tool for the control of systems with constraints. Nonetheless, in many applications, a major challenge arises, that is finding the optimal solution within a single sampling instant to apply a receding-horizon policy. In such cases, many suboptimal solutions have been proposed, among which the possibility of "blocking" some moves a-priori. In this paper, we propose a dynamic approach to move blocking, to exploit the solution already available at the previous iteration, and we show not only that such an approach preserves asymptotic stability, but also that the decrease of performance with respect to the ideal solution can be theoretically bounded.Comment: 7 page

Event-Based Control Enters the Real-Time World: Perspectives and Pitfalls

In the last years, event-based control techniques have been gaining a steadily increasing importance owing to the advantages they bring, such as reduced network traffic, low actuator wear, reduced energy consumption of the involved devices. Applying the event-based paradigm in the context of real-time control opens up new opportunities, but introduces new challenges as well. In this paper we provide an overview of both opportunities and challenges, outlining the major problems to be tackled and as a consequence future research directions

Efficient abstraction of clock synchronization at the operating system level

Distributed embedded systems are emerging and gaining importance in various domains, including industrial control applications where time determinism – hence network clock synchronization – is fundamental. In modern applications, moreover, this core functionality is required by many different software components, from OS kernel and radio stack up to applications. An abstraction layer devoted to handling time needs therefore introducing, and to encapsulate time corrections at the lowest possible level, the said layer should take the form of a timer device driver offering a Virtual Clock to the entire system. In this paper we show that doing so introduces a nonlinearity in the dynamics of the clock, and we design a controller based on feedback linearization to handle the issue. To put the idea to work, we extend the Miosix RTOS with a generic interface allowing to implement virtual clocks, including the newly designed controller that we call FLOPSYNC-3 after its ancestor. Also, we introduce the resulting virtual clock in the TDMH [20] real-time wireless mesh protocol

Day-ahead PV power forecast by hybrid ANN compared to the five parameters model estimated by particle filter algorithm

A comparison between the hybrid method (PHANN – Physical Hybrid Artificial Neural Network) and the 5 parameter Physical model, which have been determined by the particle filter algorithm, is presented here. These methods have been employed to perform the dayahead forecast of the output power of a photovoltaic plant. The aim of this work is to assess the forecast accuracy of the two methods

Human performance in manufacturing tasks: Optimization and assessment of required workload and capabilities

This paper discusses some examples where human performance and or human error prediction was achieved by using a modified version of the Rasch model(1980), where the probability of a specified outcome is modelled as a logistic function of the difference between the person capacity and item difficulty. The model needs to be modified to take into account an outcome that may not be dichotomous and o take into account the interaction between two macro factors: (a) Task complexity: that summarises all factors contributing to physical and mental workload requirements for execution of a given operative task & (b) Human capability: that considered the skills, training and experience of the people facing the tasks, representing a synthesis of their physical and cognitive abilities to verify whether or not they are matching the task requirements. Task complexity can be evaluated as a mathematical construct considering the compound effects of Mental Workload Demands and Physical Workload Demands associated to an operator task. Similarly, operator capability can be estimated on the basis of the operators\u27 set of cognitive capabilities and physical conditions. The examples chosen for the application of the model were quite different: one is a set of assembly workstation in large computer manufacturing company and the other a set of workstation in the automotive sector. This paper presents and discusses the modelling hypothesis, the interim field data collection, results and possible future direction of the studies

SEEC: A Framework for Self-aware Management of Multicore Resources

This paper presents SEEC, a self-aware programming model, designed to reduce programming effort in modern multicore systems. In the SEEC model, application programmers specify application goals and progress, while systems programmers separately specify actions system software and hardware can take to affect an application (e.g. resource allocation). The SEEC runtime monitors applications and dynamically selects actions to meet application goals optimally (e.g. meeting performance while minimizing power consumption). The SEEC runtime optimizes system behavior for the application rather than requiring the application programmer to optimize for the system. This paper presents a detailed discussion of the SEEC model and runtime as well as several case studies demonstrating their benefits. SEEC is shown to optimize performance per Watt for a video encoder, find optimal resource allocation for an application with complex resource usage, and maintain the goals of multiple applications in the face of environmental fluctuations

A Comparison of Autonomic Decision Making Techniques

Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This paper proposes a comparison of some state of the art approaches for decision making, applied to a self-optimizing autonomic system that allocates resources to a software application, which provides direct performance feedback at runtime. The Application Heartbeats framework is used to provide the sensor data (feedback), and a variety of decision mechanisms, from heuristics to control-theory and machine learning, are investigated. The results obtained with these solutions are compared by means of case studies using standard benchmarks