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How can you improve the scalability of your control systems with new research?

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Control systems are essential for managing complex and dynamic processes in engineering applications, such as robotics, smart grids, and industrial automation. However, as the scale and complexity of these systems increase, so do the challenges of designing, implementing, and maintaining them. How can you improve the scalability of your control systems with new research? In this article, we will explore some of the latest trends and innovations that can help you achieve more efficient, robust, and adaptable control systems.

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Eamonn McCormick

VP Strategic Relations Renewables
Jogendra Naik

| Engineering | Marketing |
Boris Radonic

Research And Development Engineer

1 Distributed control

One way to improve the scalability of your control systems is to use distributed control, which means that instead of having a central controller that oversees the entire system, you have multiple local controllers that communicate and coordinate with each other. Distributed control can reduce the communication and computation burden on the central controller, increase the fault tolerance and resilience of the system, and enable more flexibility and modularity in the system design. However, distributed control also poses some challenges, such as ensuring the consistency and stability of the system, dealing with delays and uncertainties in the communication network, and finding optimal ways to allocate and integrate the local controllers.

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Eamonn McCormick

VP Strategic Relations Renewables
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We have been working with SEPA and the Grid Architecture working group on how to apply distributed control to energy systems. The cornerstone of the approach we are working on is the concept of laminar decomposition, where distributed control is broken down into sub-control domains, with a controller dedicated to the each domain in the control hierarchy. We are currently working to apply this concept on several large projects. A key idea though is the need for system wide optimization to divide up the control problem dynamically between centralized control and decentralized so "decentralized control" is complimentary and additive to existing centralized control approaches.

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Jogendra Naik

| Engineering | Marketing |
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Continuous monitoring of the latest research in control systems, machine learning, and related fields is crucial to identifying and implementing scalable solutions for control systems.

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Jogendra Naik

| Engineering | Marketing |
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Implement distributed control algorithms, leverage advanced machine learning for adaptive systems, and explore real-time data analytics to enhance scalability in control systems.

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2 Data-driven control

Another way to improve the scalability of your control systems is to use data-driven control, which means that instead of relying on mathematical models and assumptions about the system dynamics, you use data from sensors and measurements to design and optimize your control algorithms. Data-driven control can improve the accuracy and performance of your control systems, especially when the system is nonlinear, uncertain, or time-varying. Data-driven control can also adapt to changes and disturbances in the system, and learn from the feedback and experience of the system. However, data-driven control also requires some considerations, such as ensuring the quality and availability of the data, dealing with noise and outliers in the data, and ensuring the robustness and safety of the control system.

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Jogendra Naik

| Engineering | Marketing |
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effectiveness of data-driven control depends on the specific characteristics of the system and the quality of the data we collect. Regularly revisit and update y approach as needed based on the system's evolving behavior and requirements. Document the entire process, including data collection methods, preprocessing steps, model selection, and control strategies. This documentation is essential for troubleshooting, replication, and knowledge transfer.

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3 Cloud-based control

A third way to improve the scalability of your control systems is to use cloud-based control, which means that instead of having local hardware and software for your control system, you use cloud computing and internet of things (IoT) technologies to access and manage your control system remotely. Cloud-based control can offer several benefits, such as reducing the cost and maintenance of your control system, increasing the accessibility and reliability of your control system, and enabling more collaboration and sharing of your control system. However, cloud-based control also involves some risks, such as ensuring the security and privacy of your control system, dealing with latency and bandwidth issues in the cloud network, and complying with the regulations and standards of your control system.

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4 Model predictive control

A fourth way to improve the scalability of your control systems is to use model predictive control (MPC), which means that instead of using a fixed or predefined control strategy, you use an optimization algorithm that predicts the future behavior of the system and chooses the best control action at each time step. MPC can handle complex and nonlinear systems, incorporate constraints and objectives in the control design, and account for uncertainties and disturbances in the system. MPC can also improve the efficiency and sustainability of your control system, by reducing the energy consumption and emissions of your system. However, MPC also requires some trade-offs, such as ensuring the feasibility and solvability of the optimization problem, dealing with the computational complexity and speed of the algorithm, and ensuring the stability and robustness of the control system.

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Boris Radonic

Research And Development Engineer
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MPC requires the solution of an optimization problem at each control step, which can be computationally intensive, especially for systems with a large number of variables and constraints. This can make real-time implementation impossible, particularly for fast-dynamic systems. The performance of MPC heavily relies on the accuracy of the model used to predict future system behavior. MPC may not be robust to model uncertainties and violation of constraints can lead to instability. The complexity of the MPC algorithm makes its implementation and maintenance expensive compared to simpler control strategies. Simplifying the system model, reducing the prediction horizon, and using efficient optimization algorithms can help... but...

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5 Hybrid control

A fifth way to improve the scalability of your control systems is to use hybrid control, which means that instead of using a single type of control technique, you use a combination of different control techniques that suit the different aspects and requirements of your system. Hybrid control can exploit the advantages and overcome the limitations of each control technique, and achieve a better performance and functionality of your system. Hybrid control can also enable more interoperability and compatibility of your system, by integrating different components and subsystems that have different control modes and protocols. However, hybrid control also introduces some complexities, such as ensuring the coordination and synchronization of the different control techniques, dealing with the transitions and switching of the control modes, and finding the optimal balance and trade-off between the different control objectives.

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Boris Radonic

Research And Development Engineer
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The tallest windmill is the MingYang Smart Energy MySE-16-260, located in China. This impressive 260-meter-tall wind turbine represents a significant advancement in harnessing wind energy With a power capacity of 15 MW and a height that surpasses both the Eiffel Tower and the Pyramid of Khufu, it provides electricity for 36,000 Chinese households. One can only imagine the number of control systems integrated into this turbine. MingYang Smart Energy employs adaptive pitch angle control, adjusting to weather conditions and wind fluctuations to maximize energy extraction. They use MPC, but from my opinion it must be a combination of State Feedback Control, Model Predictive Control, Fuzzy Logic Control and Adaptive Control.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you improve the scalability of your control systems with new research?

1

2

3

4

5

6

1 Distributed control

2 Data-driven control

3 Cloud-based control

4 Model predictive control

5 Hybrid control

6 Here’s what else to consider

Electrical Engineering

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How can you improve the scalability of your control systems with new research?

1

2

3

4

5

6

1 Distributed control

2 Data-driven control

3 Cloud-based control

4 Model predictive control

5 Hybrid control

6 Here’s what else to consider

Electrical Engineering

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