How can you use AI to improve your control system?

AI can enhance control systems in robotics by enabling adaptive and predictive capabilities. Machine learning algorithms can analyze data from sensors and actuators, optimizing control parameters for improved performance. Reinforcement learning can enable robots to learn from experience, refining control strategies. Additionally, AI facilitates predictive maintenance, identifying potential issues before they occur, thus enhancing overall system reliability and efficiency.

A control system is an integrated set of components that regulates a robot's behavior to achieve a desired goal, based on feedback from the environment. It typically comprises a controller (the brain), which analyzes sensor data and calculates commands for the actuator (the muscle) to execute. The actuator, in turn, interacts with the world, changing the robot's state. Sensors (the eyes) provide feedback to the controller, enabling it to adjust its commands and ensure the robot achieves its objective. This feedback loop allows for dynamic adaptation to changing conditions, making control systems essential for robots, automated systems, and many other technologies.

Machine normal control systems are designed to realise machine purpose. A failure of normal control is at worse a nuisance perhaps resulting in downtime. Safety related control systems are designed very specifically to reduce levels of risk associated with specific machine hazards to humans. Failure of such control functions could give rise to danger/harm. Therefore, safety functions must be clearly specified, available when the hazard is present, failsafe, and dependable. Assuming a normal control could fail once per 10 years, functional safety standards (e.g. IEC 61508, IEC 62061, ISO 13849) generally require safety control functions to be, depending on the risk reduction target, 10X-1000X more dependable than normal control functions.

AI empowers control systems with adaptive learning, allowing them to optimize performance and handle complex situations beyond the capabilities of traditional methods. AI can analyze vast data sets to make informed decisions, plan complex strategies, and even interact with humans in a more natural way. This leads to enhanced efficiency, safety, and reliability in control systems, opening up new possibilities for innovation and automation in various industries. While challenges such as data requirements and ethical concerns exist, the benefits of AI in control systems are undeniable and are poised to revolutionize how we design and operate complex systems.

AI empowers control systems with intelligence and adaptability. Reinforcement learning enables machines to learn from experience, neural networks process complex data for prediction and decision-making, fuzzy logic handles uncertainty with human-like reasoning, and genetic algorithms optimize control strategies. These techniques combined create more efficient, responsive, and adaptable control systems across various fields, from robotics to autonomous vehicles to industrial processes.

Systems with self-evolving behaviour (aka AI) ensuring safety functions are classified as requiring a formal conformity assessment under the New Machinery Regulation 2023/1032, due to their characteristics such as data dependency, opacity, autonomy and connectivity, which might considerably increase the probability and severity of harm and seriously affect the safety of the machinery or related product. The biggest challenges are how to define the safety requirements, design, build, test/validate and assess conformity of AI-based safety with the Essential Health and Safety Requirements in this regulation, which recommends third party involvement whether the software is fully or partially self-evolving.

By its very nature AI can behave or fail in unpredictable ways since it is not based on hard-coded rules like PLC logic. Therefore, for AI to be considered for use in safety critical functions (whose behaviour is rule-based, deterministic and failsafe) it must be approached applying two key fundamental principles found in Functional Safety standards such as IEC 61508. Systematic design processes and statistical performance analysis are necessary to eliminate and control systematic failures, and to reduce the probability of dangerous failures. Guidance is available within ISO/IEC TR 5649:2024 "Artificial intelligence - Functional safety and AI systems" and legal requirements found in the EU New Machinery Regulation.