Control Loops & Invariants

In spite of the significant body of research on traditional source code metrics, there has been a general failure to produce conclusive evidence as to their effectiveness for measuring software quality. We describe and recommend a potentially much more powerful and sensitive quality assessment alternative, software quality guidelines. Software quality guidelines are presented as “designer metrics”, that is, user-defined rules or constraints relating to measurable features of a program's structure, semantics, and syntax that affect its quality. To provide a methodology for designing, applying, and validating software quality guidelines, we recommend and briefly summarize IEEE standard 1061. This standard gives a process for constructing and implementing a software quality metrics framework that can be tailor-made to meet quality requirements for a particular project and/or organization. Our paper then demonstrates how software quality guidelines fit within the IEEE framework and gives an example illustrating how user-defined guidelines can be applied to evaluate or assess the quality of an Ada source unit. This guideline-based assessment of quality is then compared with an analysis based on traditional McCabe and Halstead metrics. Finally, we introduce a tool being developed by SAIC, called AdaReVu, as an effective mechanism for implementing and applying user-defined quality guidelines for Ada source code.

Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.

A language independent proof of the soundness and completeness of generalized Hoare logic

We present an extension of Logic Programming (under stable models semantics) that, not only allows concluding whether a true atom is a cause of another atom, but alsoderiving new conclusionsfrom these causal-effect relations. This is expressive enough to capture informal rules like “if some agent's actionshave beennecessaryto cause an eventEthen conclude atomcaused(,E),” something that, to the best of our knowledge, had not been formalised in the literature. To this aim, we start from a first attempt that proposed extending the syntax of logic programs with so-calledcausal literals. These causal literals are expressions that can be used in rule bodies and allow inspecting the derivation of some atomAin the program with respect to some query function ψ. Depending on how these query functions are defined, we can model different types of causal relations such as sufficient, necessary or contributory causes, for instance. The initial approach was specifically focused on monotonic query functions. This was enough to cover sufficient cause-effect relations but, unfortunately, necessary and contributory are essentiallynon-monotonic. In this work, we define a semantics for non-monotonic causal literals showing that, not only extends the stable model semantics for normal logic programs, but also preserves many of its usual desirable properties for the extended syntax. Using this new semantics, we provide precise definitions ofnecessaryandcontributorycausal relations and briefly explain their behaviour on a pair of typical examples from the Knowledge Representation literature.

Concurrent programs are commonly used in real-time system software. Verifying the correctness of concurrent programs is an important job to ensure the reliability of real-time system software. The causes of concurrency in a real-time system program include task scheduling and interrupt mechanism, where multi-interruption is an effective means of real-time response to asynchronous events. This paper studies the modeling approach for multi-interruption concurrent programs based on TMSVL (Timed Modeling, Simulation and Verification Language), so as to verify temporal properties of multi-interruption concurrent programs, such as safety and liveness properties. The formal syntax and semantics of multi-interruption concurrent programs is established based on TMSVL, and the correctness and practicability of our approach is demonstrated with a case study.

Decentralized self-adaptive systems consist of multiple control loops that adapt some local and system-level global goals of each locally managed system or component in a decentralized setting. As each component works together in a decentralized environment, a control loop cannot take adaptation decisions independently. Therefore, all the control loops need to exchange their adaptation decisions to infer a global knowledge about the system. Decentralized self-adaptation approaches in the literature uses the global knowledge to take decisions that optimize both local and global goals. However, coordinating in such an unbounded manner impairs scalability. This paper proposes a decentralized self-adaptation technique using reinforcement learning that incorporates partial knowledge in order to reduce coordination overhead. The Q-learning algorithm based on Interaction Driven Markov Games is utilized to take adaptation decisions as it enables coordination only when it is beneficial. Rather than using unbounded number of peers, the adaptation control loop coordinates with a single peer control loop. The proposed approach was evaluated on a service-based Tele Assistance System. It was compared to random, independent and multiagent learners that assume global knowledge. It was observed that, in all cases, the proposed approach conformed to both local and global goals while maintaining comparatively lower coordination overhead.

This article presents a comprehensive review of control loop performance assessments in the context of building HVAC controls. Few studies are available for assessing HVAC control loop performance using a single control quality factor. A control quality factor should be an objective and quantitative metric with simple-to-interpret criteria and should only use data available from the actual control system, such as the control output. The authors systematically reviewed 34 indices and the associated methods of evaluating control loop performance and cataloged the drawbacks and merits of the different indices. Most of these performance assessment indices are currently used in process control industry applications. There were 14 of the 34 indices selected for further review, due to their particular suitability for implementation in HVAC control loop performance assessment. Finally, the selected 14 indices are implemented for assessments of three regulatory control loops with proportional-integral controllers: a heating coil outlet air temperature control loop and variable air volume room air temperature control loop using simulated data from a dynamic Modelica model, and variable air volume room air temperature control loop in a heating mode from real field data. Based on the review and preliminary results, the Normalized Harris Index and exponentially weighted moving averages based index are proposed as potential candidates for control quality factor, and further investigation of the use of them in HVAC control loop performance assessment is recommended.

Is there an axiomatic semantics for standard pure prolog?

M.W.HarroldR.M.ZavodBasic Concepts in Medicinal Chemistry.American Society of Health-System Pharmacists440pp2013$49 (softcover), ISBN 978-1-58528-266-1

Autonomic networks are an emerging technology which is promising to reduce the complexity of human-driven network management processes and enable a variety of so-called self-* features such as self-configuration, self-optimization, etc, inside the network devices and the network as a whole. Autonomic behaviors are widely understood as a control loop implemented by an autonomic entity that automates management processes and controls diverse aspects of a set of resources. Though automation is necessary and achievable, autonomic decision-making-elements of the network can not fully perform decisions on every task of the network without requiring some degree of “a human-in-the-loop” in some of the decisions. From the operator's perspective, controllability of the control loop and decision notification from the autonomic network is a vital issue that needs to be addressed. In this paper we present our considerations on how an Autonomic Fault-Management control loop (“detect an incident” - “find the root cause behind it” - “remove the root cause”) can be controlled by the network operation personnel.

Safe-visor architecture for sandboxing (AI-based) unverified controllers in stochastic cyber–physical systems

5 - Stability and control modes of closed loops

The ultimate goal of our study is to obtain suggestions for the development of devices capable of the automatic modeling of natural phenomena. It is therefore of fundamental importance to develop a theoretical basis for the description of their optimal performance. In the previous chapters it was stated that an empirical modeling of natural phenomena includes three main tasks: Estimation, storage, and application of a probability distribution. Each of these tasks can be optimized by using the methods described in the previous chapter. The aim of this section is to present the problems and the solutions related with an optimal storage of empirical information about a continuous probability distribution in a system comprised of a finite number of discrete memory units. Such a system can be considered as a basic building block of an automatic modeler of natural phenomena. For example, we can imagine the brain of a biological organism or the digital memory of a computer that continually obtains signals from its surroundings and it optimally stores the corresponding empirical information. The first problem is the estimation of the probability density function of a continuous variable from the empirical data. We have already seen that this can be solved using Parzen’s window. [2] The second problem is the storage of the continuous probability density in a discrete system. This task is generally related with a loss of information and thus the question arises, how one can minimize this loss.KeywordsWindow FunctionExcited NeuronReference VectorDiscrete Random VariableMemory UnitThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In the past decade we have seen an active research community proposing attacks and defenses to Cyber-Physical Systems (CPS). Most of these attacks and defenses have been heuristic in nature, limiting the attacker to a set of predefined operations, and proposing defenses with unclear security guarantees. In this paper, we propose a generic adversary model that can capture any type of attack (our attacker is not constrained to follow specific attacks such as replay, delay, or bias) and use it to design security mechanisms with provable security guarantees. In particular, we propose a new secure design paradigm we call DARIA: Designing Actuators to Resist arbItrary Attacks. The main idea behind DARIA is the design of physical limits to actuators in order to prevent attackers from arbitrarily manipulating the system, irrespective of their point of attack (sensors or actuators) or the specific attack algorithm (bias, replay, delays, etc.). As far as we are aware, we are the first research team to propose the design of physical limits to actuators in a control loop in order to keep the system secure against attacks. We demonstrate the generality of our proposal on simulations of vehicular platooning and industrial processes.

In this paper, the authors implemented a technique in controlling a batch distillation column system by utilizing data driven control. The authors used the pre-existing mini batch distillation column which has been integrated into a Cyber-Physical System by the previous research. Then, some minor modification in the network structure were added to enable the implementation of data driven control. The data driven control itself consists of a machine learning with Recurrent Neural Network model to mimic the behavior of the physical system and a Mataheuristics-based PID tuner such as Genetic Algorithm and Particle Swarm Optimization to adjust the plant's controller over time. The performance of the system was then compared with the conventional PID control scheme and also human in the loop control.