Extending an industrial robot controller

Software plays a crucial role in robot control systems, and its efficient, flexible development is essential for production. Such software must be customized to specific production processes, requiring developers with specialized expertise in these areas—the high development threshold results in reduced efficiency and increased costs. Recently, significant progress has been made in automated problem-solving through societies of agents based on large language models (LLMs). To automate software development for industrial robot control systems, this paper introduces an Industrial Robot Control Software Auto-Development (IRCSAD) framework with multi-agent collaboration, and a Low-code Industrial Software Platform (LISP) for validating IRCSAD-developed software. IRCSAD automates software development and iterative optimization of prompts through the collaboration of multiple LLM-based agents. This work also proposes a software testing methodology for robot control systems based on reliability assessment. An experimental study that develops software for the robot control system for assembly, sorting, and inspection tasks is implemented. The results show that collaboration with LISP enhances IRCSAD's ability to solve complex problems in the development process, saves development costs, and improves development efficiency.

This paper presents Rapid RT, a novel statistical approach to Worst-Case Response-Time (WCRT) analysis targeting complex embedded real-time systems. The proposed algorithm combines Extreme Value Theory (EVT) and other statistical methods in order to produce a probabilistic WCRT estimate. This estimate is calculated using response time data from either Monte Carlo simulations of a detailed model of the system, or from response-time measurements of the real system. The method could be considered as a pragmatic approach intended for complex industrial systems with real-time requirements. The target systems contain tasks with many intricate dependencies in their temporal behavior, which violates the assumptions of traditional analytical methods for response time analysis and thereby makes them overly pessimistic. An evaluation is presented using two simulation models, inspired by an industrial robotic control system, and five other methods as reference.

Recently, human–robot interaction technology has been considered as a key solution for smart factories. Surface electromyography signals obtained from hand gestures are often used to enable users to control robots through hand gestures. In this paper, we propose a dynamic hand-gesture-based industrial robot control system using the edge AI platform. The proposed system can perform both robot operating-system-based control and edge AI control through an embedded board without requiring an external personal computer. Systems on a mobile edge AI platform must be lightweight, robust, and fast. In the context of a smart factory, classifying a given hand gesture is important for ensuring correct operation. In this study, we collected electromyography signal data from hand gestures and used them to train a convolutional recurrent neural network. The trained classifier model achieved 96% accuracy for 10 gestures in real time. We also verified the universality of the classifier by testing it on 11 different participants.

After investigating the conception and features of PLC (Programmable Logic Controller) and embedded system, in this paper the conception of a Quasi-PLC is proposed, which combine the advantages of PLC and embedded system. The architecture of Quasi-PLC and a Top-Down design method with collaboration design of software and hardware for Quasi-PLC embedded control system are researched then. Finally, setting the large-scale industrial valve control system as an example, the conception of Quasi-PLC and related technologies are used in industrial valve control system, and a satisfied application effect is obtained, which proves the rationality of the conception of Quasi-PLC and related technologies.

Industry 4.0 is the German vision for the future of manufacturing, where smart factories use information and communication technologies to digitise their processes to achieve improved quality, lower costs, and increased efficiency. It is likely to bring a massive change to the way control systems function today. Future distributed control systems are expected to have an increased connectivity to the Internet, in order to capitalize on new offers and research findings related to digitalization, such as cloud, big data, and machine learning. A key technology in the realization of distributed control systems is middleware, which is usually described as a reusable software layer between operating system and distributed applications. Various middleware technologies have been proposed to facilitate communication in industrial control systems and hide the heterogeneity amongst the subsystems, such as OPC UA, DDS, and RT-CORBA. These technologies can significantly simplify the system design and integration of devices despite their heterogeneity. However, each of these technologies has its own characteristics that may work better for particular applications. Selection of the best middleware for a specific application is a critical issue for system designers. In this paper, we conduct a survey on available standard middleware technologies, including OPC UA, DDS, and RT-CORBA, and show new trends for different industrial domains.

Bio-inspired algorithms for industrial robot control using deep learning methods

Computer facilities and microprocessor-based technology have been successfully used in the energy industry. For protection and equipment control, this technology has been used in SCADA, remote control and monitoring applications. Particular attention is paid to the cyber security sector for automation and control systems. Protective application of the equipment and control, SCADA, monitoring and remote control, uses the technology with microprocessor. Due to the great importance of the power supply process, there is no question of being left in a state of vulnerability and neglect. Security is not perfect and will never be. For this reason, there will always be security breaches and incidents. Also, for this reason, not only protection mechanisms are in place, but also mechanisms for rapid detection of incidents and which are able to react effectively to the isolation of problems and to ensuring security. Processes security for systems will continue to evolve in the future. By definition, there are no communication systems that are 100% safe. Attacks against critical industrial infrastructures marked an increase not only in terms of number but also of the level of complexity. The destruction of the industrial control system (ICS) and critical processes were interrupted. For many organizations, the security improvement in ICS systems is great. The extreme sensitivity to ensure the availability and performance of industrial processes has led to a more conservative and rigorous approach to how security measures are implemented. Cyber-attacks that could compromise the availability, integrity, and confidentiality of ICS systems may come from within systems or from outside ICS systems. Among the ICS system infection vectors from the perspective of the SANS Institute (2014) include: external threats (state attacks, hacking etc.), malware, exploiting tools, phishing, internal attacks, cyber security protocols, and industrial espionage. This chapter addresses the cyber security issues required for the protection, automation, control and communications systems of transformation stations as well as methods that could be used to prevent computer attacks that can have a significant impact on the availability of the system Electro-energetic effect with serious consequences on extended area interruptions.

The control instructions of industrial control systems are prone to threats such as unauthorized access and tampering during transmission and interaction, and access control is a fundamental method to protect data security. Due to the cyber-physical integration and availability constraints in industrial control systems, existing access control methods cannot be directly applied. In this paper, we propose an access control policy for control instructions based on the ciphertext policy attribute-based encryption (CP-ABE) under the availability constraints in industrial control systems. First, we analyze the abnormal behaviors of control instructions in process industrial monitoring systems, and model the attributes associated with field control business and integrate them into CP-ABE to achieve fine-grained access control and avoid non-compliant operations. Second, we adopt a trusted computing mechanism to protect the identity trustworthiness of the transmission node; the confidentiality of the transmitted control instruction is guaranteed by the negotiated symmetric key and the key authorization is realized by the CP-ABE. We further optimize the measuring frequency of the trusted measurement and the deployment policy of the access control method to guarantee business availability. Finally, we conduct formal analysis and experimental validation of the proposed method, and the results show that the proposed access control policy can prevent unauthorized access and non-compliant tampering by industrial control devices and achieve trustworthy delivery of control instructions with controlled computational complexity.

The manufacturing industry always tries to improve performance amid the trade globalization. Demand and supply uncertainty, and increasingly the intense competition, prosecute the presence of an affective leadership to integrate the company's internal and external resources in improving company performance. This research investigates the role of affective leadership in firm performance through an internally integrated system and external integration in FMCG companies. Data collection used questionnaires, designed with a five-point Liker scale, were distributed to 55 fast-moving consumer goods (FMCG) manufacturing companies. The data analysis used the PLS technique utilizing smart PLS software to assess the validity and reliability of the outer model and to examine the hypotheses developed. The results of the hypothesis testing found that affective leadership can improve internal system integration, external integration, and firm performance. Internal system integration has an impact on external integration but is not strong enough to have a direct effect on firm performance. The internally integrated system has an influence on firm performance through external integration. The company's ability to share information with external partners can improve firm performance through demand fulfillment. The study provides a managerial implication on how to enhance firm performance in the context of internal and external system integration. The finding of this research enriches the current studies in supply chain management.

The implementation of automated regulatory control has been around since the middle of the last century through analog means. It has allowed engineers to operate the plant more consistently by focusing on overall operations and settings instead of individual monitoring of local instruments (inside and outside of a control room). A similar approach is proposed for cyber security, where current border-protection designs have been inherited from information technology developments that lack consideration of the high-reliability, high consequence nature of industrial control systems. Instead of an independent development, however, an integrated approach is taken to develop a holistic understanding of performance. This performance takes shape inside a multiagent design, which provides a notional context to model highly decentralized and complex industrial process control systems, the nervous system of critical infrastructure. The resulting strategy will provide a framework for researching solutions to security and unrecognized interdependency concerns with industrial control systems.

This paper addresses the pressing challenges of building secure and resilient Industrial Control Systems (ICS) by focusing on rapid attack detection, recovery, and maintaining monotonicity in online monitoring systems. Traditional methods often fail to quickly identify threats, leaving systems vulnerable to prolonged downtime. Specifically, classical online monitoring systems struggle to preserve monotonicity, as they typically detect only isolated anomalies during operation, which can mask subsequent deviations in system behavior. This research presents an advanced approach to resilient cyber-physical systems (CPS) that utilizes Signal Temporal Logic with Global Time (STL-GT) in combination with an Autotuning mechanism for robust realtime monitoring, multi-point anomaly detection, and recovery. This innovative framework enables prompt detection of attacks and faults, along with rapid system recovery. Our STL-GT online monitoring system incorporates a syntactic separation technique based on a partition rule to address monotonicity issues and improve the system's ability to detect and respond to multiple anomalies during system execution. The autotuning mechanism, based on Internal Model Control (IMC) PID tuning rules, enables swift and adaptive system recovery in the event of disruptions. We validated our methodology using the Tennessee Eastman Plant model, a complex chemical plant simulator designed to study industrial processes and control systems. Experimental results demonstrate that our approach provides effective security and resilience of the CPS by consistently detecting anomalies and restoring the system with zero overshoot and minimal oscillations, eliminating violation masking and missed alarms. These findings highlight the effectiveness of our approach in fortifying critical systems against adversarial attacks and operational faults.

In this paper, an integrated system to control and manage a state-of-the-art industrial computer numerical control (CNC) machine (Studer S33) using a commercially available tablet (Samsung Galaxy Tablet S2) is presented as a proof of concept (PoC) for the ubiquitous control of industrial machines. As a PoC, the proposed system provides useful insights to support the further development of full-fledged systems for Industrial Internet of Things (IIoT) applications. The proposed system allows for the quasi-decentralisation of the control architecture of conventional programmable logic controller (PLC)-based industrial control systems (ICSs) through data and information exchange over the transmission control protocol and the internet protocol (TCP/IP) suite using multiple agents. Based on the TCP/IP suite, a network device (Samsung Galaxy Tablet S2) and a process field net (PROFINET) device (Siemens Simatic S7-1200) are interfaced using a single-board computer (Raspberry Pi 4). An override system mainly comprising emergency stop and acknowledge buttons is also configured using the single-board computer. The input signals from the override system are transmitted to the PROFINET device (i.e., the industrial control unit (ICU)) over TCP/IP. A fully functional working prototype is realised as a PoC for an integrated system designated for the wireless and ubiquitous control of the CNC machine. The working prototype as an entity mainly comprises a mobile (handheld) touch-sensitive human-machine interface (HMI), a shielded single-board computer, and an override system, all fitted into a compact case with physical dimensions of 300 mm by 180 mm by 175 mm. To avert potential cyber attacks or threats to a reasonable extent and to guarantee the security of the PoC, a multi-factor authentication (MFA) including an administrative password and an IP address is implemented to control the access to the web-based ubiquitous HMI proffered by the PoC.

The oil and gas industry, a vital lifeline fueling the global economy, find itself at a pivotal juncture where the convergence of operational technology (OT) and information technology (IT) has ushered in unprecedented opportunities and challenges. As digital transformation sweeps across this sector, the imperative to fortify cybersecurity defenses against ever-evolving threats has become paramount. Innovative and forward-looking oil and gas organizations across the globe are adopting the Cloud in many forms because of their digital transformation initiatives. Data lakes, edge technology, machine-to-machine communication, and machine learning (ML) algorithms have been enabling this industrial digital transformation. This transformation is also driving changes to the OT landscape, and as these environments continue to evolve, OT environments are leveraging well proven IT solution patterns to improve the productivity and efficiency of production operations. Industrial customers often start their digital transformation journey by sending OT data to the cloud for analysis and analytics without sending commands back to the industrial automation and control systems (ICAS). This process is often called "open loop" operations, since there is one-way communication from edge to cloud. Customers generally find this relatively easy to secure and manage. However, more often we are witnessing requirements to optimize operations by generating an automatic or operator-initiated response in the oil and gas production operation, rig management based on insights gained from cloud analytics. This process is often referred to as "closed loop" operations with two-way communication between edge and cloud. The security and compliance practices for closed loop operations are more rigorous because closed operations manipulate OT devices remotely. Developing these practices should be rooted in a cyber risk assessment to help businesses understand and prioritize security concerns. In this paper we propose how strengths of Cloud computing can become key enabler for oil and gas organizations in helping them enhance their overall security posture and manage risks within OT environments. We have deployed solution patterns described in this paper as the foundational pillar of several Oil and Gas organization's overall OT system architecture to unlock both "open loop" and "closed loop" operations in a secure, reliable and cost-effective manner. The specific scope items we will cover in this paper focus on a custom security uplift framework having four foundational components – 1) Cloud led architecture patterns to provide next generation network segmentation strategies in OT De-militarized Zone 2) OT asset inventory and vulnerability management 3) Centralized security monitoring and incident response with help of Artificial Intelligence and most recently providing Generative AI based virtual assistant to query security event data from OT systems. For organizations to plan their industrial digital transformation safely and securely, it is recommended that a multi-layered approach to secure the Industrial Control Systems (ICS)/OT and Cloud environments be implemented as captured in ten security golden rules in the following paper [1]. In addition to this, in the paper we propose a comprehensive architecture framework that aligns with established cybersecurity framework (CSF) such as NIST. The use case studies discussed in the paper will highlight how customer have been able to remediate critical security vulnerabilities within weeks post implementation by deploying a comprehensive asset inventory discovery and vulnerability assessment. This has help reduce mean time to identify and mitigate vulnerabilities from months to days with automated testing in pre-production environments, ensure quick detection and response towards security incidents with help of advanced security monitoring and incident response playbooks, covering 100% of the OT assets through this capability and leverage advanced data analytics, machine learning to perform log mining, data cleansing, data validation, log mining and analysis through natural language processing such as conversational AI assistants powered by large language model (LLM) in the Cloud.

In order to evaluate the network security risks and implement effective defenses in industrial control system, a risk assessment method for industrial control systems based on attack graphs is proposed. Use the concept of network security elements to translate network attacks into network state migration problems and build an industrial control network attack graph model. In view of the current subjective evaluation of expert experience, the atomic attack probability assignment method and the CVSS evaluation system were introduced to evaluate the security status of the industrial control system. Finally, taking the centralized control system of the thermal power plant as the experimental background, the case analysis is performed. The experimental results show that the method can comprehensively analyze the potential safety hazards in the industrial control system and provide basis for the safety management personnel to take effective defense measures.

The integration of Industrial Control Systems (ICS) with Information and Communication Technologies (ICT) systems has resulted in tremendous increase in the number of ICS security incidents. Current ICS incident response practices can hardly be applied into ICS directly as the two systems are fundamentally different in terms of system complexity, criticality, flexibility and dependability. This paper addresses this challenges by proposing an ICS incident response decision framework. It is based on decision-making theory and has descriptive, predictive and prescriptive models that particularly address ICS incident response decision-making. This framework is implemented using a service-oriented architecture (SOA) that provides incident response decision making as services and offers standardized interfaces to integrate with existing incident management systems.