Identification of tissue types and boundaries with a fiber optic force sensor

2016 IEEE 13th International Conference on Signal Processing (ICSP)

Huo

et al. 2016

Self Cite

In this paper, an impedance control is proposed for a robotic needle with a fiber optic force sensor. The fiber optic force sensor is placed at the tip of the needle to directly measure the interaction force between needle and soft tissue, overcoming the noise like friction force. The sensor is sensitive to the temperature condition, and their relationship is first established, and then used to compensate for the temperature influence to enhance the force measurement. An impedance control is presented based on insertion model whose impedance parameters are identified in terms of acquired test data, and experimental results show that the control strategy is effective to avoid excessive insertion force, reducing the risk of robotic needle.

Section: A Fiber Optic Force Sensor Principlementioning

confidence: 99%

Impedance control of a robot needle with a fiber optic force sensor

Xiao

2016 IEEE 13th International Conference on Signal Processing (ICSP)

Huo

et al. 2016

Self Cite

“…A fiber optic sensor was devised in our lab to implement real-time measurement of the force at the needle tip during insertion [13,14]. This force occurs as the needle is inserted into soft tissue.…”

Section: A Insertion Force Meausrementmentioning

confidence: 99%

“…Most recently, optical fibre technology is applied to the area of sensing, and grows increasingly in medical fields, due to its big advantages over electrical sensors, such as miniaturization, higher accuracy and resolution, immunity from electromagnetic interference and biocompatibility [12]. A fibre optic force sensor was devised in our lab to measure the interactive force at the tip of a needle, and to identify the types of soft tissue [13,14]. Herein, the force information is now fed back into the control loop to reduce the interactive force at the needle tip, for purpose of insertion safety.…”

Section: Introductionmentioning

confidence: 99%

Force Feedback Control of a Robotic Needle Insertion into Layered Soft Tissues

2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

Xiao

Chen

et al. 2018

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In this study, a feedback control law is proposed to steer a robotic needle using force information from a fiber optic sensor. This sensor is integrated into the lumen at the distal end of the needle to measure the needle insertion force. The force signals are analyzed via wavelet transform to identify the boundaries of layered soft tissues in real time. The boundaries information is then used in the robotic needle steering scheme to reduce as much as possible the insertion force, and mitigate the risk of tissue injury. Porcine belly tissue phantoms are herein used in the ex vivo tests of robotic needle insertion, and comparative study has been done with and without insertion force feedback. Experimental results show that the force feedback control approach proposed reduces effectively the insertion force.

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

Chen

Adaptive Control & Signal

2020

Summary In this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non‐affine nonlinearities and avoid the algebraic loop problem, the radial basis function neural network (RBFNN) is adopted to approximate the unknown non‐affine nonlinearities with the help of Butterworth low‐pass filter. Based on the output of the RBFNN, the coupled design between sate observer and disturbance observer is presented to estimate the unmeasured states and compounded disturbances. Then, the adaptive output feedback control scheme is proposed for unknown pure feedback nonlinear systems, where a first‐order filter is introduced to tackle with the issue of “explosion of complexity” in the traditional back‐stepping approach. The boundedness and convergence of the closed‐loop system are proved rigorously by utilizing the Lyapunov stability theorem. Finally, simulation studies are worked out to demonstrate the effectiveness of the proposed scheme.