Increasingly, from smart homes to connected automobiles, consumers are being exposed to an environment of intelligent products and systems. The level of intelligence now embedded in our cars, homes, communication devices, consumer electronics, and even mundane items like our toasters and toothbrushes increases every day. In the very near future, not only will humans interact with a rapidly growing array of smart products, but many of these products will interact autonomously with each other and other systems to monitor and control power usage for smart grids; automatically monitor and manage our homes; diagnose, and schedule service for our vehicles; and lots more.
Moreover, factory production lines, process plants for energy and utilities, and smart cities will depend on cyber-physical systems to self-monitor; optimize; even run infrastructure, transportation, and buildings autonomously. In the future, cyber-physical systems will rely less on human control and more on the intelligence embedded in the AI-enabled core processors. These will run the devices, products, and systems that will be a pervasive part of our personal lives and the industrial world that produces them.
While manufacturers across all industrial sectors are ramping up to meet demand for this growing “smart product” market, they face major challenges developing and manufacturing these new and increasingly more complex products and systems. These cyber-physical systems require tight coordination and integration between the computational (virtual) and the physical (continuous) worlds. To meet these complexity and integration requirements, designers of cyber-physical intelligent systems are using advanced simulation platforms that cover model-based mechatronic systems engineering, embedded system design integration, and simulation models that validate product and system design in the physical world.
Cyber-physical Systems Will Run Business and Industry
What exactly is a cyber-physical system (CPS)? In basic terms, a CPS is an engineered system or mechanism that is controlled or monitored by computer-based algorithms and tightly integrated with both the inter-net and its users. In cyber-physical systems, physical and software components are deeply intertwined, each operating on different spatial and temporal scales, exhibiting multiple and distinct behavioral modalities, and interacting with each other in a lot of ways that change with context. Examples of CPS include smart grid, driver-assist and autonomous automobile systems, transportation systems, health and biomedical monitoring, manufacturing and process control systems, smart cities, robotics systems, intelligent edge devices, and new agricultural technologies.
To ensure that CPS is safe, we need to address two fundamental scientific challenges. First, we need to develop basic logic regarding simultaneous discrete and continuous events. That is, the CPS will have to deal with discrete events as they occur, such as for autonomous vehicles or a medical event. The logic will also have to include an overall plan to deal with the process that is being monitored and automated, such as a production line in a factory or process plant. A traditional approach that has been used for some time, is to simulate and model a CPS as an instance of hybrid automation, which is a model of a finite state machine, where each state’s behavior is defined by a set of differential equations over continuous variables. Today, state-of-the art AI and ML algorithms are being used to simulate variable states.
CPS can operate in the presence of uncertainty. These are often due to external circumstances not under system control. For CPS operating in the physical world, unplanned natural events like weather, natural dis-asters like hurricanes and earthquakes; and of course, unplanned human error or intentionally malicious human actions. System failures such as faulty sensors and actuators and inaccurate or interrupted data streams could also create uncertainty. The research community is constantly exploring new approaches for simulation and modeling to deal with uncertainty. Common to many of these approaches is the use of probabilities which can predict the likelihood of certain events or occurrences.
Today, AI and ML are being applied to the problem of uncertainty. Probabilistic algorithms can deal with predictive and prescriptive analytical models. Intelligent CPS get much of their intelligence from both the use of ML, which introduces approximation and requires probabilistic and statistical training algorithms; and from inferencing engines embedded in intelligent edge devices. Use of current advanced simulation applications to model CPS will be critical to the development and implementation of the next generation of IoT ecosystems.
Simulation Technology Enables Advanced Cyber-physical Systems
The sheer complexity of many CPS use cases requires state-of-the-art simulation applications. These simulation application platforms typically include the traditional CAE testing applications like FEA, CFD, multi-physics, electro-magnetic, stress analysis, and other product design testing applications. Today, these platforms also include applications for modeling and simulating multi-discipline systems engineering. These allow engineers to apply a model-based, systems engineering approach to mechatronic product and process development. For CPS design, model-based design (MBD) is required to integrate the engineering disciplines (mechanical, electrical, software) that function together as a mechatronic system.
As mechatronic systems take advantage of more powerful microprocessors and the software that runs on them, the interaction between hard-ware and software becomes more complex. Managing this complexity can prove challenging for hardware and software engineering teams that develop requirements, describe functionality, and test and implement the concepts in a variety of ways. Most of these systems include closed-loop control methodologies that compensate for electromechanical interactions and other variables, adding further to the complexity.
Mechatronic design methods used today typically initialize the design process with mechanical modeling, followed by electrical design. Traditionally, when developing new software, engineers addressed software validation at a late stage in the development process, only testing the software through emulation on hardware prototypes. Just as electrical deign imposed constraints on mechanical systems, software typically imposes significant constraints on the overall electromechanical system design. Compensating for constraints and errors found in hardware or software at this late stage creates costly delays in the development process, since it can be time consuming to trace problems back to their root cause. Errors related to incomplete, incorrect, or conflicting requirements may even require a fundamental redesign.
ARC Advisory Group clients can view the complete report at ARC Client Portal
If you would like to buy this report or obtain information about how to become a client, please Contact Us
Keywords: Cyber-physical Systems, Model-based Design, Mechatronic Systems, AI, Machine Learning, Simulation Modeling, Inferencing Models, CAE, ARC Advisory Group.