Trends in Systems and Control Engineering

In the context of systems and control engineering, a system is a group of interconnected components or elements that work together toward a specific goal.¹ It can be made up of physical items, such as machines or electronic circuits, or abstract entities, such as economic or biological processes.

Systems and control engineering focuses on designing and optimizing systems to function as intended. Using mathematical models, simulations and advanced control algorithms to analyze and improve system performance, engineers manipulate the systems’ dynamics to get the results they need.

As a subset of systems engineering, control engineering addresses the design and implementation of control systems: mechanisms or processes that regulate a system’s behavior to maintain it within set criteria—often with the goal of keeping it stable, responsive and efficient.²

There’s need for systems and control engineering expertise in nearly every industry—aerospace, robotics, industrial automation, biomedical systems, energy management, traffic control, and many more—because technology and engineering can combine to make systems run more safely, smoothly and efficiently, even when conditions within and around them change.

Read on to explore some of the current trends in systems and control engineering.

Networked Control Systems and Cloud Computing

The phrases ‘distributed environments’ and ‘distributed networks’ refer to computing or systems architectures with component parts spread across multiple locations or nodes.³ This distribution can be physical, such as with different computers in a network, or geographical, spanning locations around the world.

Systems and control engineers are directly involved in implementing precise control mechanisms over distributed networks and making sure all the components work together seamlessly. This is especially important in industrial automation and smart grids, where real-time decision-making and feedback loops are essential. For example:

In industrial automation, distributed control systems make manufacturing processes efficient and responsive by orchestrating machinery and sensors across different locations⁴
In the energy sector, smart grids rely on control mechanisms for stability, as they manage the flow of electricity across sources and consumers⁵
In traffic management, distributed control algorithms regulate signals and sensors, easing congestion and improving the overall flow of traffic⁶

By using the flexibility and accessibility of cloud technologies, engineers can remotely—and efficiently—monitor performance metrics, analyze data and implement control strategies. That’s crucial in situations where it’s a challenge to work up close, such as in industrial settings or complex infrastructures. Engineering’s many uses for cloud computing include:

Manufacturing

The integration of cloud platforms means that sensors and devices on a factory floor can collect real-time performance information and send it to the cloud for analysis. Response teams can monitor production lines remotely and take quick action if problems arise. By implementing control systems through the cloud, engineers can adjust parameters, identify inefficiencies and optimize production—from anywhere with internet access.⁷

Energy Management

Cloud-based solutions are key in overseeing smart buildings, including heating, ventilation and air conditioning (HVAC) systems. Sensors and smart devices collect temperature, occupancy and energy consumption data, which is transmitted to the cloud. This lets building managers control the HVAC systems remotely, adjusting settings for optimal energy efficiency, comfort and cost savings.⁸

Healthcare

Thanks to cloud-based solutions, medical teams can monitor patient data and medical devices, such as heart monitors, remotely, which speeds up and streamlines the delivery of patient care.⁹ Further, cloud platforms make it easier to manage medical equipment, which makes proactive maintenance, updates and troubleshooting possible without the need for staff on site.¹⁰

Adaptive Techniques

As people grow and mature, we learn to adapt to new and unexpected situations—what to do, for example, in case of rain delay, flight cancellation or unforeseen job change. Machines and systems that need to adapt to changing circumstances, however, can’t rely on wisdom gained through human experience. That’s where systems and control engineers step up: in the development of controllers that can adapt as system dynamics vary.

This involves creating intelligent control algorithms that can dynamically adjust parameters in response to changes in a system. By using feedback mechanisms, engineers build controllers that can continuously assess and respond to varying conditions.¹¹ This flexibility is key when outside factors or uncertainty can change everything. For example:

Adaptive Cruise Control in Vehicles

When you’re using cruise control and traffic conditions change, intelligent control algorithms make your vehicle speed up or slow down in response. Using sensors such as radar or cameras, the algorithm continuously monitors the distance to the car ahead of you. If the traffic flow changes, the control system adjusts your speed to keep you and your passengers safe.¹²

Aircraft Flight Control Systems

While you enjoy the in-flight movie, intelligent algorithms make adjustments in response to changing flight conditions. In fly-by-wire systems, for instance, the control algorithm continually assesses factors such as airspeed, altitude and turbulence. When any of them changes, the control system adapts engine thrust, control surface positions and other parameters to keep the aircraft stable.¹³

Learning Control Techniques

Self-tuning algorithms, also known as learning algorithms, are computational techniques that automatically adjust their parameters based on real-time feedback. Essentially, they enable a system to learn from its own behavior and adjust accordingly. They contribute to the adaptive systems discussed above. Everyday life presents a large number of uses for them, including:

Thermostats with Adaptive Temperature Control

Many modern thermostats utilize self-tuning algorithms to control heating and cooling systems in homes. They learn the thermal characteristics of a space and adjust the temps based on factors such as occupancy patterns and weather conditions. This makes homes more comfortable and energy-efficient without the need for manual temperature adjustments.¹⁴

Smartphone Keyboard Predictive Text

Got autocorrect? Smartphone keyboards often use self-tuning algorithms for predictive text input. These algorithms learn from your typing habits, frequently used words and context to suggest or correct words as you type.¹⁵

Fitness Trackers with Adaptive Activity Recognition

Due to self-tuning algorithms, fitness trackers can learn and adapt to individual movement patterns, distinguishing between activities such as walking, running or cycling. This gives users more accurate, precise information about their physical activities and health metrics.¹⁶

Get Ahead With Expertise in the Latest Technology

For more insight about how qualified engineers shape a tech-focused world, read these articles from the Case School of Engineering:

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