System identification is a crucial step in understanding complex dynamical systems, such as those found in robotics, control systems, and mechanical engineering. Traditionally, system identification has relied on mathematical modeling and assumptions about the underlying system. However, with the advent of data-driven techniques, it’s now possible to identify systems by learning from data directly. In this review, we’ll explore the advantages and challenges of using data-driven techniques for system identification.
Advantages
- Data-driven methods can handle complex systems: Traditional system identification methods often struggle with complex systems that exhibit nonlinear behavior or have multiple inputs and outputs. Data-driven techniques, such as neural networks and kernel methods, are well-suited for handling these types of systems because they can learn from the data directly and capture nonlinear relationships between variables.
- Improved accuracy: Data-driven techniques can provide more accurate system identification compared to traditional methods because they use empirical data to estimate the parameters of the underlying system. This leads to better predictions and control of the system.
Challenges
- Data quality is crucial: The performance of data-driven techniques relies heavily on the quality of the data used for learning. Noisy or limited data can lead to overfitting, which can result in poor generalization of the learned model to new situations. Therefore, it’s essential to ensure that the data is clean and comprehensive enough to capture the underlying system accurately.
- Computational cost: As the size of the data set grows, so does the computational cost of learning the model. This can be a challenge for real-time applications where fast inference times are critical.
- Robustness and stability: Producing robust and stable models that can handle safety-critical and control applications is essential. Data-driven techniques may struggle to achieve this, especially when dealing with complex systems or noisy data.
In conclusion, data-driven techniques offer a powerful approach to system identification by learning from empirical data. While they have many advantages, such as improved accuracy and ability to handle complex systems, they also come with challenges like data quality, computational cost, and robustness. By carefully selecting the appropriate data set and using appropriate regularization techniques, it’s possible to overcome these challenges and obtain accurate and reliable models for system identification.
