Advancements in System Identification and Control

The field of system identification and control is witnessing significant advancements, with a focus on developing innovative methods for online estimation, adaptive control, and sparse parameter identification. Researchers are exploring new approaches to address the challenges of non-stationary observations, non-persistent excitation, and high-dimensional systems. A key direction is the integration of advanced optimization techniques, such as alternating direction method of multipliers and recursive algorithms, to improve the efficiency and accuracy of system identification and control. Another area of interest is the application of control engineering principles to climate science, highlighting the potential for cross-disciplinary collaborations and innovative solutions. Noteworthy papers include:

  • A novel recursive identification method for mechanical systems, which delivers parametric continuous-time additive models and is applicable in both open-loop and closed-loop controlled systems.
  • A unified alternating optimization framework for joint sensor and actuator configuration in LQG systems, which ensures numerical efficiency and adaptability to various design constraints and configuration costs.
  • A novel parameter-tying theorem in multi-model adaptive systems, which enables significant dimension reduction and preserves system stability and performance.

Sources

Recursive Identification of Structured Systems: An Instrumental-Variable Approach Applied to Mechanical Systems

A Unified Alternating Optimization Framework for Joint Sensor and Actuator Configuration in LQG Systems

Towards minimax optimal algorithms for Active Simple Hypothesis Testing

Discrete-time Two-Layered Forgetting RLS Identification under Finite Excitation

A novel real-time aeroelastic hybrid simulation system of section model wind tunnel testing based on adaptive extended Kalman filter

A Novel Parameter-Tying Theorem in Multi-Model Adaptive Systems: Systematic Approach for Efficient Model Selection

Climate Science and Control Engineering: Insights, Parallels, and Connections

Recursive Algorithms for Sparse Parameter Identification of Multivariate Stochastic Systems with Non-stationary Observations

Built with on top of