2025 |
Gaeun Kim / Hyungbo Shim Determination of Bandwidth of Q-Filter in Disturbance Observers to Guarantee Transient and Steady State Performance Under Measurement Noise Proceedings Article In: Institute of Control, Robotics and Systems 2025 25th International Conference on Control, Automation and Systems (ICCAS), Songdo Convensia, Incheon, Korea, 2025. Abstract | Links | BibTeX | Tags: Disturbance observer, Noise measurement, Singular perturbation @inproceedings{nokey,Q-filter-based disturbance observer (DOB) is one of the most widely used robust controller due to its design simplicity. Such simplicity arises from that reducing τ, which is the time constant of low pass filters, not only ensures robust stability but also enhances nominal performance recovery—ability to recover the trajectory of nominal closed-loop system. However, in contrast to noise-free setup, excessively small τ can rather damage the nominal performance recovery under measurement noise. That is, minimizing τ is no longer immediately guaranteeing nominal performance recovery. Motivated by this observation, this paper concentrates on determination of τ to guarantee transient and steady state performance. This analysis uses Lyapunov method utilizing the coordinate transformation inspired by the analysis based on singular perturbation theory. As a result, we present an affordable noise level and open interval for τ that guarantees both the required performances. The analysis can also lead to theoretical demonstration on that excessively reducing τ gives assurance to achieve target performance only for noise-free case. |
2020 |
Jin Gyu Lee / Hyungbo Shim A distributed algorithm that finds almost best possible estimate under non-vanishing and time-varying measurement noise Journal Article In: IEEE Control Systems Letters, vol. 4, no. 1, pp. 229-234, 2020, ISSN: 2475-1456. Abstract | Links | BibTeX | Tags: Noise measurement @article{LeeShim20,In this letter, we review an existing distributed least-squares solver and share some new insights on it. Then, by the observation that an estimation of a constant vector under output noise can be translated into finding the least-squares solution, we present an algorithm for distributed estimation of the state of linear time-invariant systems under measurement noise. The proposed algorithm consists of a network of local observers, where each of them utilizes local measurements and information transmitted from the neighbors. It is proven that even under non-vanishing and time-varying measurement noise, we could obtain an almost best possible estimate with arbitrary precision. Some discussions regarding the plug-and-play operation are also given. |
List of English Publication
2025 |
Determination of Bandwidth of Q-Filter in Disturbance Observers to Guarantee Transient and Steady State Performance Under Measurement Noise Proceedings Article In: Institute of Control, Robotics and Systems 2025 25th International Conference on Control, Automation and Systems (ICCAS), Songdo Convensia, Incheon, Korea, 2025. |
2020 |
A distributed algorithm that finds almost best possible estimate under non-vanishing and time-varying measurement noise Journal Article In: IEEE Control Systems Letters, vol. 4, no. 1, pp. 229-234, 2020, ISSN: 2475-1456. |