Velocity and acceleration estimation by iterative learning observer and performance validation with MEMS-based inertial sensors

Kawakami, Yasuhiro; Eguchi, Yasuhito; Nimiya, Tsuyoshi; Suemitsu, Haruo; Matsuo, Takami

doi:10.1504/ijamechs.2013.056001

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering these preliminary results, the stability of the overall closed-loop control system can be summarized by the following theorem. Theorem 3: Consider i) the system (1), ii) the initialization set Ω ξ ⊂ R 6 , iii) the appropriately selected performance functions (17), The proposed position (16) controllers and the iterative learning observer (19) and (20) solves the prescribed performance stabilization problem stated in Section III-A.…”

Section: Sketch Of the Stability Proofmentioning

confidence: 99%

See 1 more Smart Citation

A robust controller for micro-sized agents: The prescribed performance approach

Denasi

Misra

2016

2016 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

View full text Add to dashboard Cite

Abstract-Applications such as micromanipulation and minimally invasive surgery can be performed using micro-sized agents. For instance, drug-loaded magnetic micro-/nano-particles can enable targeted drug delivery. Their precise manipulation can be assured using a robust motion controller. In this paper, we design a closed-loop controller-observer pair for regulating the position of microagents. The prescribed performance technique is applied to control the microagents to follow desired motion trajectories. The position of the microagents are obtained using microscopic images and image processing. The velocities of the microagents are obtained using an iterative learning observer. The algorithm is tested experimentally on spherical magnetic microparticles that have an average diameter of 100 µm. The steady-state errors obtained by the algorithm are 20 µm. The errors converge to the steady-state in approximately 8 seconds.

show abstract

Section: Sketch Of the Stability Proofmentioning

confidence: 99%

“…Consequently, the noise on the estimated velocities is amplified. The adaptation of the Iterative Learning Observer (ILO) method [18] introduced in [19] does not depend on these variables and thus can deal with the high gain issue.…”

Section: Introductionmentioning

confidence: 99%