PALS-Based Analysis of an Airplane Multirate Control System in Real-Time Maude

Distributed cyber-physical systems (DCPS) are pervasive in areas such as aeronautics and ground transportation systems, including the case of distributed hybrid systems. DCPS design and verification is quite challenging because of asynchronous communication, network delays, and clock skews. Furthermore, their model checking verification typically becomes unfeasible due to the huge state space explosion caused by the system's concurrency. The PALS ("physically asynchronous, logically synchronous") methodology has been proposed to reduce the design and verification of a DCPS to the much simpler task of designing and verifying its underlying synchronous version. The original PALS methodology assumes a single logical period, but Multirate PALS extends it to deal with multirate DCPS in which components may operate with different logical periods. This paper shows how Multirate PALS can be applied to formally verify a nontrivial multirate DCPS. We use Real-Time Maude to formally specify a multirate distributed hybrid system consisting of an airplane maneuvered by a pilot who turns the airplane according to a specified angle through a distributed control system. Our formal analysis revealed that the original design was ineffective in achieving a smooth turning maneuver, and led to a redesign of the system that satisfies the desired correctness properties. This shows that the Multirate PALS methodology is not only effective for formal DCPS verification, but can also be used effectively in the DCPS design process, even before properties are verified.Comment: In Proceedings FTSCS 2012, arXiv:1212.657

Architecture-Driven Semantic Analysis of Embedded Systems (Eds) Dagstuhl Seminar 12272

Architectural modeling of complex embedded systems is gaining prominence in recent years, both in academia and in industry. An architectural model represents components in a distributed system as boxes with well-defined interfaces, connections between ports on component interfaces, and specifies component properties that can be used in analytical reasoning about the model. Models are hierarchically organized, so that each box can contain another system inside, with its own set of boxes and connections between them. The goal of Dagstuhl Seminar 12272 “Architecture-Driven Semantic Analysis of Embedded Systems” is to bring together researchers who are interested in defining precise semantics of an architecture description language and using it for building tools that generate analytical models from architectural ones, as well as generate code and configuration scripts for the system. This report documents the program and the outcomes of the presentations and working groups held during the seminar

Time in SCCharts

Synchronous languages, such as the recently proposed SCCharts language, have been designed for the rigorous specification of real-time systems. Their sound semantics, which builds on an abstraction from physical execution time, make these languages appealing, in particular for safety-critical systems. However, they traditionally lack built-in support for physical time. This makes it rather cumbersome to express things like time-outs or periodic executions within the language. We here propose several mechanisms to reconcile the synchronous paradigm with physical time. Specifically, we propose extensions to the SCCharts language to express clocks and execution periods within the model. We draw on several sources, in particular timed automata, the Clock Constraint Specification Language, and the recently proposed concept of dynamic ticks. We illustrate how these extensions can be mapped to the SCChart language core, with minimal requirements on the run-time system, and we argue that the same concepts could be applied to other synchronous languages such as Esterel, Lustre or SCADE

Aircraft adaptive learning control

The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed

Back Pressure Based Multicast Scheduling for Fair Bandwidth Allocation

We study the fair allocation of bandwidth in multicast networks with multirate capabilities. In multirate transmission, each source encodes its signal in layers. The lowest layer contains the most important information and all receivers of a session should receive it. If a receiver’s data path has additional bandwidth, it receives higher layers which leads to a better quality of reception. The bandwidth allocation objective is to distribute the layers fairly. We present a computationally simple, decentralized scheduling policy that attains the maxmin fair rates without using any knowledge of traffic statistics and layer bandwidths. This policy learns the congestion level from the queue lengths at the nodes, and adapts the packet transmissions accordingly. When the network is congested, packets are dropped from the higher layers; therefore, the more important lower layers suffer negligible packet loss. We present analytical and simulation results that guarantee the maxmin fairness of the resulting rate allocation, and upper bound the packet loss rates for different layers

Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Algorithms and architectures for the multirate additive synthesis of musical tones

In classical Additive Synthesis (AS), the output signal is the sum of a large number of independently controllable sinusoidal partials. The advantages of AS for music synthesis are well known as is the high computational cost. This thesis is concerned with the computational optimisation of AS by multirate DSP techniques. In note-based music synthesis, the expected bounds of the frequency trajectory of each partial in a finite lifecycle tone determine critical time-invariant partial-specific sample rates which are lower than the conventional rate (in excess of 40kHz) resulting in computational savings. Scheduling and interpolation (to suppress quantisation noise) for many sample rates is required, leading to the concept of Multirate Additive Synthesis (MAS) where these overheads are minimised by synthesis filterbanks which quantise the set of available sample rates. Alternative AS optimisations are also appraised. It is shown that a hierarchical interpretation of the QMF filterbank preserves AS generality and permits efficient context-specific adaptation of computation to required note dynamics. Practical QMF implementation and the modifications necessary for MAS are discussed. QMF transition widths can be logically excluded from the MAS paradigm, at a cost. Therefore a novel filterbank is evaluated where transition widths are physically excluded. Benchmarking of a hypothetical orchestral synthesis application provides a tentative quantitative analysis of the performance improvement of MAS over AS. The mapping of MAS into VLSI is opened by a review of sine computation techniques. Then the functional specification and high-level design of a conceptual MAS Coprocessor (MASC) is developed which functions with high autonomy in a loosely-coupled master- slave configuration with a Host CPU which executes filterbanks in software. Standard hardware optimisation techniques are used, such as pipelining, based upon the principle of an application-specific memory hierarchy which maximises MASC throughput

Special Issue on Formal Aspects of Component Software (Selected Papers from FACS'12)

International audienceThis issue contains extended versions of selected papers from the 9th International Symposium on Formal Aspects of Component Software (FACS'12)