Computer Controlled Systems: Theory and Design

The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence

Industrial information integration engineering (IIIE) is a set of foundational concepts and techniques that facilitate the industrial information integration process. In recent years, many applicat...

A Survey on Industrial Information Integration 2016–2019

Machine vision technologies hold the promise of enabling rapid and accurate fruit crop yield predictions in the field. The key to fulfilling this promise is accurate segmentation and detection of fruit in images of tree canopies. This paper proposes two new methods for automated counting of fruit in images of mango tree canopies, one using texture-based dense segmentation and one using shape-based fruit detection, and compares the use of these methods relative to existing techniques:—(i) a method based on K-nearest neighbour pixel classification and contour segmentation, and (ii) a method based on super-pixel over-segmentation and classification using support vector machines. The robustness of each algorithm was tested on multiple sets of images of mango trees acquired over a period of 3 years. These image sets were acquired under varying conditions (light and exposure), distance to the tree, average number of fruit on the tree, orchard and season. For images collected under the same conditions as the calibration images, estimated fruit numbers were within 16 % of actual fruit numbers, and the F1 measure of detection performance was above 0.68 for these methods. Results were poorer when models were used for estimating fruit numbers in trees of different canopy shape and when different imaging conditions were used. For fruit-background segmentation, K-nearest neighbour pixel classification based on colour and smoothness or pixel classification based on super-pixel over-segmentation, clustering of dense scale invariant feature transform features into visual words and bag-of-visual-word super-pixel classification using support vector machines was more effective than simple contrast and colour based segmentation. Pixel classification was best followed by fruit detection using an elliptical shape model or blob detection using colour filtering and morphological image processing techniques. Method results were also compared using precision–recall plots. Imaging at night under artificial illumination with careful attention to maintaining constant illumination conditions is highly recommended.

Machine vision for counting fruit on mango tree canopies

MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard

Recently, research on control and architecture co- design has been drawing increasingly more attention. This is because these techniques integrate the design of the controllers and the architecture and explore the characteristics on both sides to achieve more efficient design of embedded control systems. However, there still exist several challenges like the large design space and inadequate trade-off opportunities for different objectives like control performance and resource utilization. In this paper, we propose a co-optimization approach for FlexRay-based distributed control systems, that synthesizes both the controllers and the task and communication schedules. This approach exploits some FlexRay protocol specific characteristics to reduce the complexity of the whole optimization problem. This is done by employing a customized control design and a nested two-layered optimization technique. Therefore, compared to existing methods, the proposed approach is more scalable. It also allows multi-objective optimization taking into account both the overall control performance and the bus resource utilization. This approach generates a Pareto front representing the trade-offs between these two, which allows the engineers to make suitable design choices.

Multi-Objective Co-Optimization of FlexRay-Based Distributed Control Systems

Software-based control of physical systems is common in domains such as automotive, avionics, and industrial automation. Safety of such systems is determined by control-theoretic properties such as stability, settling time, and peak overshoot. These properties strongly depend on the software code generated from high-level controller models, and the implementation of such code on an embedded platform. To ensure safety, the semantics of the system model considered for controller design must be faithfully preserved in the platform implementation. However, traditionally, controller design and implementation platform design are carried out in isolation, followed by their integration, which often relies on simulations to estimate the behavior of the controllers. Thus, safety properties that were proven at the model level using control-theoretic tools can no longer be established in an actual implementation. This makes the design of embedded control systems costly, error prone, and hinders certification. In this paper, we review recent efforts in control-platform cosynthesis techniques toward addressing this problem. Here, the control and the embedded systems communities have come together to adopt a cyber–physical system (CPS)-oriented design paradigm. This cosynthesis paradigm integrates the design of control algorithms and platform parameters within a holistic optimization framework and accounts for relevant details from both sides. We survey the evolution of design approaches for such cosynthesis and show how–the originally disjoint–controller and the platform design methods are gradually converging.

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Semantics-Preserving Cosynthesis of Cyber-Physical Systems

Automotive platforms today run hundreds of millions of lines of software code implementing a large number of different control applications spanning across safety-critical functionality to driver assistance and comfort-related functions. While such control software today is largely designed following model-based approaches, the underlying models do not take into account the details of the implementation platforms, on which the software would eventually run. Following the state-of-the-art in control theory, the focus in such design is restricted to ensuring the stability of the designed controllers and meeting control performance objectives, such as settling time or peak overshoot. However, automotive platforms are highly cost-sensitive and the issue of designing "resource-efficient" controllers has largely been ignored so far and is addressed using very ad hoc techniques. In this paper, we will illustrate how, following traditional embedded systems design oriented thinking, computation, communication and memory issues can be incorporated in the controller design stage, thereby resulting in control software not only satisfying the usual control performance metrics but also making efficient utilization of the resources on distributed automotive architectures.

Model-based design of resource-efficient automotive control software

For many embedded systems, such as automotive electronic systems, security has become a pressing challenge. Limited resources and tight timing constraints often make it difficult to apply even lightweight authentication and intrusion detection schemes, especially when retrofitting existing designs. Moreover, traditional hard deadline assumption is insufficient to describe control tasks that have certain degrees of robustness and can tolerate some deadline misses while satisfying functional properties such as stability. In this work, we explore feasible weakly-hard constraints on control tasks, and then leverage the scheduling flexibility from those allowed misses to enhance system's capability for accommodating security monitoring tasks. We develop a co-design approach that 1) sets feasible weakly-hard constraints on control tasks based on quantitative analysis, ensuring the satisfaction of control stability and performance requirements; and 2) optimizes the allocation, priority, and period assignment of security monitoring tasks, improving system security while meeting timing constraints (including the weakly-hard constraints on control tasks). Experimental results on an industrial case study and a set of synthetic examples demonstrated the significant potential of leveraging weakly-hard constraints to improve security and the effectiveness of our approach in exploring the design space to fully realize such potential.

https://par.nsf.gov/servlets/purl/10182763

Security-Driven Codesign with Weakly-Hard Constraints for Real-Time Embedded Systems

One emerging research direction to address the design of Cyber-Physical Systems (CPS) is the co-design of the architecture and the controllers. The co-design techniques integrate the design of control and architecture in an early phase and the parameters on both sides can be synthesized according to certain design objectives. This explores the characteristics on both sides to achieve more efficient design of such systems. In this paper, we give an overview of the automated synthesis of CPS from joint controller/architecture specifications by explaining the background and motivation for such methods and illustrating this design paradigm with a concrete example of a FlexRay-based embedded control system. Furthermore, we provide the future outlook in this direction by explaining possible extensions and the related challenges.

Debayan Roy

Papers

Multi-Objective Co-Optimization of FlexRay-Based Distributed Control Systems

Semantics-Preserving Cosynthesis of Cyber-Physical Systems

Model-based design of resource-efficient automotive control software

Security-Driven Codesign with Weakly-Hard Constraints for Real-Time Embedded Systems

Automated synthesis of cyber-physical systems from joint controller/architecture specifications