The Role of Systems Control Engineers in Emerging Technologies

While ‘automation’ may feel like the latest tech buzzword, it can be traced back to 1764, when James Hargreaves invented the Spinning Jenny: an automated textile spinning machine that allowed an operator to spin multiple threads at once.¹ Since then, the world has seen invention after invention of automated systems.

Today, advanced technologies such as robotics, machine learning and artificial intelligence are transforming work processes in ways we hadn't imagined. Systems and control engineers play a crucial role in shaping the future of automation. Keep reading to explore the role of systems and control engineering in emerging technologies.

Understanding Emerging Technologies

Robotics. Autonomous systems. The Internet of Things (IoT). Artificial intelligence. Machine learning.

All these advancements fall under the umbrella of emerging technologies. Almost every industry, including manufacturing, health care, finance, agriculture and transportation, can benefit from them.

Applying these emerging technologies in a work environment has been proven to improve production efficiency and productivity. A study by McKinsey & Company found that automation technologies can boost productivity by up to 50%.² These emerging technologies also give companies across sectors the ability to improve product or service quality by personalizing product recommendations, reducing errors in production and identifying fraudulent transactions, among other benefits.

The advancement of automation, however, also poses various challenges such as job displacement and ethical and safety concerns. According to the World Economic Forum, automation will displace 85 million jobs by 2025.³ On the same note, there are concerns that surveillance robots could be used in public spaces or warfare.

Systems and Control Engineers’ Role in Emerging Technologies

Systems and control engineers ensure the smooth operation and effectiveness of automated systems. They use their knowledge and skills to create control plans that improve system performance and reliability in a wide range of applications, from cutting-edge robotics to industrial automation and intelligent manufacturing.

Typically, the roles and responsibilities of systems and control engineers vary by company or industry. In general, they include designing control algorithms, implementing control hardware and software, selecting the right sensors and testing system performance. Depending on the technology at hand, their specific responsibilities include those that follow here.

Robotics and Autonomous Systems

Systems and control engineers leverage their expertise in control theory, systems integration and dynamic modeling to create robots and autonomous systems that can perform tasks with precision and adaptability. For example, they apply control theory to design control algorithms that dictate how a system responds to inputs or corrects deviations.⁴ They are also responsible for continued testing and refining of the systems to ensure they are adaptable to the dynamic and complex environments in which they operate.

Robotics makes a difference throughout our culture and infrastructure. In the manufacturing sector, for example, robotic systems perform basic repetitive operations like assembling, loading and modifying parts.⁵ Through integration with AI, IoT solutions and ML algorithms, robots can also handle cognitive tasks and make instant decisions without human intervention.

In the health care sector, robotic machines serve as surgeons’ assistants, improving accuracy during surgical procedures.⁶ The agricultural sector benefits, as well, with robots performing tasks such as spraying weeds and pests, picking fruit and harvesting more quickly and efficiently than human workers.⁷

The Internet of Things

The Internet of Things is a network of objects or systems embedded with electronics, sensors, software, or network connectivity that can communicate and share data.⁸ Think of smart homes, industrial automation, connected cars and smart cities.

Systems and control engineers integrate sensors, actuators, control systems and other components into the IoT ecosystem, allowing the entire network to communicate without any hitches. They are also responsible for designing algorithms to collect data from IoT sensors, selecting and implementing communication protocols and integrating IoT data with cloud platforms.

Process Control and Optimization

Systems and control engineers apply engineering principles, mathematical models and computer software to continuously monitor and adjust the variables that affect the safety, quality and efficiency of products and systems. To do this, they have to choose control systems with sensors, actuators, controllers and communication devices that combine well to regulate the process variables.⁴

To implement each control system, they install hardware and software components, test and tune the controllers, configure the settings and verify the control performance. Once the system is implemented, the engineers have to monitor the control performance regularly to detect and diagnose any faults or deviations from the control objectives. This allows them to apply corrective and preventive actions to achieve optimal operating conditions.⁴

Process control and optimization are critical, especially in chemical, petrochemical and power generation industries. Since these industries deal with hazardous materials, process control helps detect and respond to abnormal conditions or emergencies, thus enhancing safety. In addition, process control can help in environmental compliance by managing waste, reducing emissions and preventing accidents.

Artificial Intelligence and Machine Learning

The foundation of artificial intelligence (AI) and machine learning (ML) integration in automation is data.⁹ These two technologies have changed how companies store, use and transform data. For example, AI and ML algorithms can clean and preprocess vast datasets, identify and correct errors and add missing values. This ensures the data under analysis is accurate and reliable. Further, AI systems based in Natural Language Processing (NLP) can map and match data from multiple sources, even if that data doesn't have a similar structure. This addresses a key challenge, making it easy to integrate heterogeneous datasets.

When it comes to developing intelligent control algorithms, systems and control engineers play a major role. Some of their responsibilities include:

• Deciding the right data sources for relevant information
• Ensuring the data collected is sufficient for training
• Preparing the data for training and real-time operation
• Training machine learning models
• Designing control policies that determine how the system should respond to different scenarios

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