40734 - Mastering Digital Twins: Advanced Modelling, Optimization and Control (Pilot)

Type of course:

Digital learning, Lesson

Language:

Duration:

0 hours, 30 minutes

Proficiency:

Intermediate

Target:

Professionals, Students, Manager

SUMMARY

This course provides an in-depth exploration of advanced techniques for creating, optimizing, and controlling digital twins in complex systems. Digital twins are virtual replicas of physical assets or processes that can be used for real-time monitoring, predictive maintenance, and dynamic optimization. The course is structured into three main sections, each addressing critical aspects of digital twin technology and its applications.

Section 1: Advanced Asset-Twin System Identification This section covers techniques for accurately modeling the relationship between the physical system and its digital twin. It begins with State Space Models, which provide a mathematical framework for describing system dynamics and predicting future behavior. Parameter Estimation techniques are then explored to identify unknown system parameters and improve model accuracy using real-world data. Finally, Sensor Integration is discussed, focusing on how to incorporate data from various sensors to enhance the digital twin’s fidelity and reliability.

Section 2: Optimization Strategies for Digital Twins In this section, various optimization techniques are presented for improving the performance and efficiency of digital twins. It includes Gradient-Based Methods, which use derivatives to guide optimization, as well as Meta-Heuristic Algorithms and Evolutionary Algorithms, which can solve complex, non-linear problems. The section also covers Constraints Incorporation in Uncertain Environments, discussing methods to handle real-world limitations and uncertainties within the optimization process.

Section 3: Dynamic Optimization and Control in Digital Twins The final section focuses on control techniques for dynamic systems, with a particular emphasis on Model Predictive Control (MPC). MPC is a powerful approach for real-time optimization that uses a predictive model to compute optimal control actions while accounting for system constraints. The integration of robust and stochastic extensions, as well as the incorporation of adaptive tuning and machine learning techniques, is discussed to enhance MPC’s effectiveness in digital twin applications.

About The Author

Ioannis Astli is a Data Scientist at the CORE Innovation Centre, with a strong academic foundation holding an MSci in Computer Engineering. Driven by a deep passion for Artificial Intelligence, he consistently advocates for advancing AI knowledge and its practical applications across various domains.

Learning outcomes

Develop Accurate Digital Twin Models: By the end of Section 1, you will be able to operate and validate digital twin models of dynamic systems using state-space modeling and parameter estimation techniques, achieving at least 90% accuracy against sample real-world data.
Optimize Digital Twin Performance: Upon completing Section 2, you will be able to apply optimization methods, including gradient-based, meta-heuristic, and evolutionary algorithms, to enhance a digital twin’s performance by 20% on key performance indicators, effectively managing real-world constraints and uncertainties.
Implement Advanced Control Strategies: By the course’s end, you will be able to implement an MPC framework that stabilizes a digital twin model within 5% of a target trajectory, enabling real-time control and adaptability for complex, multi-variable systems.