Stability analysis of interval type-2 fuzzy-model-based control systems.

This paper presents the stability analysis of interval type-2 fuzzy-model-based (FMB) control systems. To investigate the system stability, an interval type-2 Takagi-Sugeno (T-S) fuzzy model, which can be regarded as a collection of a number of type-1 T-S fuzzy models, is proposed to represent the nonlinear plant subject to parameter uncertainties. With the lower and upper membership functions, the parameter uncertainties can be effectively captured. Based on the interval type-2 T-S fuzzy model, an interval type-2 fuzzy controller is proposed to close the feedback loop. To facilitate the stability analysis, the information of the footprint of uncertainty is used to develop some membership function conditions, which allow the introduction of slack matrices to handle the parameter uncertainties in the stability analysis. Stability conditions in terms of linear matrix inequalities are derived using a Lyapunov-based approach. Simulation examples are given to illustrate the effectiveness of the proposed interval type-2 FMB control approach.

BMI-based stability and performance design for fuzzy-model-based control systems subject to parameter uncertainties.

This paper presents the stability and performance design of a fuzzy-model-based control system subject to parameter uncertainties. A nonlinear controller with a favorable characteristic to relax the stability conditions is proposed to drive the system states of the nonlinear plant to follow those of a stable reference model. Stability and performance conditions in terms of bilinear matrix inequalities (BMIs) will be derived based on a Lyapunov-based approach. A combined genetic algorithm and convex programming technique process will be developed to solve the solution to the BMIs. An application example will be given to illustrate the merits of the proposed approach.

Ex vivo study of prostate cancer localization using rolling mechanical imaging towards minimally invasive surgery.

Rolling mechanical imaging (RMI) is a novel technique towards the detection and quantification of malignant tissue in locations that are inaccessible to palpation during robotic minimally invasive surgery (MIS); the approach is shown to achieve results of higher precision than is possible using the human hand. Using a passive robotic manipulator, a lightweight and force sensitive wheeled probe is driven across the surface of tissue samples to collect continuous measurements of wheel-tissue dynamics. A color-coded map is then generated to visualize the stiffness distribution within the internal tissue structure. Having developed the RMI device in-house, we aim to compare the accuracy of this technique to commonly used methods of localizing prostate cancer in current practice: digital rectal exam (DRE), magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) biopsy. Final histology is the gold standard used for comparison. A total of 126 sites from 21 robotic-assisted radical prostatectomy specimens were examined. Analysis was performed for sensitivity, specificity, accuracy, and predictive value across all patient risk profiles (defined by PSA, Gleason score and pathological score). Of all techniques, pre-operative biopsy had the highest sensitivity (76.2%) and accuracy (64.3%) in the localization of tumor in the final specimen. However, RMI had a higher sensitivity (44.4%) and accuracy (57.9%) than both DRE (38.1% and 52.4%, respectively) and MRI (33.3% and 57.9%, respectively). These findings suggest a role for RMI towards MIS, where haptic feedback is lacking. While our approach has focused on urological tumors, RMI has potential applicability to other extirpative oncological procedures and to diagnostics (e.g., breast cancer screening).

Three-Degree-of-Freedom MR-Compatible Multisegment Cardiac Catheter Steering Mechanism.

This paper presents a novel MR-compatible 3-DOF cardiac catheter steering mechanism. The catheter's steerable structure is tendon driven and consists of miniature deflectable, helical segments created by a precise rapid prototyping technique. The created catheter prototype has an outer diameter of 9 Fr (3 mm) and a steerable distal end that can be deflected in a 3-D space via four braided high-tensile Spectra fiber tendons. Any longitudinal twist commonly observed in helical structures is compensated for by employing clockwise (CW) and counter clockwise (CCW) helical segments in an alternating fashion. A 280 µm flexible carbon fiber rod is used as a backbone in a central channel to improve the structure's steering and positioning repeatability. In addition to the backbone, a carbon fiber tube can be inserted into the structure to a varying amount capable of changing the structure's forcibility and, thus, providing a means to change the curvature and to modify the deflectable length of the catheter leading to an extension of reachable points in the catheter-tip workspace. A unique feature of this helical segment structure is that the stiffness can be further adjusted by appropriately tensioning tendons simultaneously. An experimental study has been conducted examining the catheter-tip trajectory in a 3-D space and its positioning repeatability using a 5-DOF magnetic coil tracking system. Furthermore, MRI experiments in a 1.5-T scanner confirmed the MR-compatibility of the catheter prototype. The study shows that the proposed concept for catheter steering has great potential to be employed for robotically steered and MR-guided cardiac catheterization.

Stability analysis of a three-term backpropagation algorithm.

Efficient learning by the backpropagation (BP) algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. This paper analyzes the convergence of the new three-term backpropagation algorithm. If the learning parameters of the three-term BP algorithm satisfy the conditions given in this paper, then it is guaranteed that the system is stable and will converge to a local minimum. It is proved that if at least one of the eigenvalues of matrix F (compose of the Hessian of the cost function and the system Jacobian of the error vector at each iteration) is negative, then the system becomes unstable. Also the paper shows that all the local minima of the three-term BP algorithm cost function are stable. The relationship between the learning parameters are established in this paper such that the stability conditions are met.

Using visual cues to enhance haptic feedback for palpation on virtual model of soft tissue.

This paper explores methods that make use of visual cues aimed at generating actual haptic sensation to the user, namely pseudo-haptics. We propose a new pseudo-haptic feedback-based method capable of conveying 3D haptic information and combining visual haptics with force feedback to enhance the user's haptic experience. We focused on an application related to tumor identification during palpation and evaluated the proposed method in an experimental study where users interacted with a haptic device and graphical interface while exploring a virtual model of soft tissue, which represented stiffness distribution of a silicone phantom tissue with embedded hard inclusions. The performance of hard inclusion detection using force feedback only, pseudo-haptic feedback only, and the combination of the two feedbacks was compared with the direct hand touch. The combination method and direct hand touch had no significant difference in the detection results. Compared with the force feedback alone, our method increased the sensitivity by 5%, the positive predictive value by 4%, and decreased detection time by 48.7%. The proposed methodology has great potential for robot-assisted minimally invasive surgery and in all applications where remote haptic feedback is needed.

Intra-operative tumour localisation in robot-assisted minimally invasive surgery: A review.

Robot-assisted minimally invasive surgery has many advantages compared to conventional open surgery and also certain drawbacks: it causes less operative trauma and faster recovery times but does not allow for direct tumour palpation as is the case in open surgery. This article reviews state-of-the-art intra-operative tumour localisation methods used in robot-assisted minimally invasive surgery and in particular methods that employ force-based sensing, tactile-based sensing, and medical imaging techniques. The limitations and challenges of these methods are discussed and future research directions are proposed.

Inverse finite-element modeling for tissue parameter identification using a rolling indentation probe.

This paper investigates the use of inverse finite-element modeling (IFEM)-based methods for tissue parameter identification using a rolling indentation probe for surgical palpation. An IFEM-based algorithm is proposed for tissue parameter identification through uniaxial indentation. IFEM-based algorithms are also created for locating and identifying the properties of an embedded tumor through rolling indentation of the soft tissue. Two types of parameter identification for the tissue tumor are investigated (1) identifying the stiffness (µ) of a tumor at a known depth and (2) estimating the depth of the tumor (D) with known mechanical properties. The efficiency of proposed methods has been evaluated through silicone and porcine kidney experiments for both uniaxial indentation and rolling indentation. The results show that both of the proposed IFEM methods for uniaxial indentation and rolling indentation have good robustness and can rapidly converge to the correct results. The tissue properties estimated using the developed method are generic and in good agreement with results obtained from standard material tests. The estimation error of µ through uniaxial indentation is below 3 % for both silicone and kidney; the estimation error of µ for the tumor through rolling indentation is 7-9 %. The estimation error of D through rolling indentation is 1-2 mm.

Air-float palpation probe for tissue abnormality identification during minimally invasive surgery.

This paper presents a novel palpation probe based on optical fiber technology. It is designed to measure stiffness distribution of a soft tissue while sliding over the tissue surface in a near frictionless manner. A novelty of the probe is its ability to measure indentation depth for nonplanar tissue profiles which are commonly experienced during surgery. Since tumors are often harder than the surrounding tissue, the proposed probe can intraoperatively aid the surgeon to rapidly identify the presence, location, and size of the tumors through the generation of a tissue stiffness map. The probe can concurrently measure tissue reaction force, indentation depth, and the orientation of the probe with respect to the tissue surface. Hence, it can generate an elasticity model of the tissue with minimum measurement inaccuracies caused by surface profile variations. Further, the probe has a tunable force range and the indentation force can be adjusted externally to match tissue limitations. The performance of the probe developed was validated using simulated soft tissues samples. Our tumor identification experiments showed that the probe can accurately identify the location and size of tumors hidden inside nonflat tissue surfaces. Further, the probe has clearly demonstrated its potential to identify tumors with tumor-tissue stiffness ratios as low as 2.1.

A stiffness probe based on force and vision sensing for soft tissue diagnosis.

this paper introduces a novel approach of stiffness measurement based on force and vision sensing for tissue diagnosis. The developed probe is mainly composed of a force sensor and an image acquisition unit capable of obtaining contact area of probe-soft tissue interaction. By measuring the change of diameter of contact area during indentation test, the indentation depth can be determined. The stiffness of target soft tissue then can be evaluated by measuring indentation force and depth simultaneously. The probe can generalize a mechanical image to visualize the stiffness distribution for localization of abnormalities when sliding over soft tissue. The performance of the developed probe was validated by experiments on multiple materials including silicone phantoms and pork organs. The results show that the probe can perform stiffness measurement effectively when the probe indents or slides on the tissue surface.

MRI-compatible intensity-modulated force sensor for cardiac catheterization procedures.

This paper presents a novel, magnetic resonance imaging (MRI)-compatible, force sensor suitable for cardiac catheterization procedures. The miniature, fiber-optic sensor is integrated with the tip of a catheter to allow the detection of interaction forces with the cardiac walls. The optical fiber light intensity is modulated when a force acting at the catheter tip deforms an elastic element, which, in turn, varies the distance between a reflector and the optical fiber. The tip sensor has an external diameter of 9 Fr (3 mm) and can be used during cardiac catheterization procedures. The sensor is able to measure forces in the range of 0-0.85 N, with relatively small hysteresis. A nonlinear method for calibration is used and real-time MRI in vivo experiments are carried out, to prove the feasibility of this low-cost sensor, enabling the detection of catheter-tip contact forces under dynamic conditions.

Finite-element modeling of soft tissue rolling indentation.

We describe a finite-element (FE) model for simulating wheel-rolling tissue deformations using a rolling FE model (RFEM). A wheeled probe performing rolling tissue indentation has proven to be a promising approach for compensating for the loss of haptic and tactile feedback experienced during robotic-assisted minimally invasive surgery (H. Liu, D. P. Noonan, B. J. Challacombe, P. Dasgupta, L. D. Seneviratne, and K. Althoefer, "Rolling mechanical imaging for tissue abnormality localization during minimally invasive surgery, " IEEE Trans. Biomed. Eng., vol. 57, no. 2, pp. 404-414, Feb. 2010; K. Sangpradit, H. Liu, L. Seneviratne, and K. Althoefer, "Tissue identification using inverse finite element analysis of rolling indentation," in Proc. IEEE Int. Conf. Robot. Autom. , Kobe, Japan, 2009, pp. 1250-1255; H. Liu, D. Noonan, K. Althoefer, and L. Seneviratne, "The rolling approach for soft tissue modeling and mechanical imaging during robot-assisted minimally invasive surgery," in Proc. IEEE Int. Conf. Robot. Autom., May 2008, pp. 845-850; H. Liu, P. Puangmali, D. Zbyszewski, O. Elhage, P. Dasgupta, J. S. Dai, L. Seneviratne, and K. Althoefer, "An indentation depth-force sensing wheeled probe for abnormality identification during minimally invasive surgery," Proc. Inst. Mech. Eng., H, vol. 224, no. 6, pp. 751-63, 2010; D. Noonan, H. Liu, Y. Zweiri, K. Althoefer, and L. Seneviratne, "A dual-function wheeled probe for tissue viscoelastic property identification during minimally invasive surgery," in Proc. IEEE Int. Conf. Robot. Autom. , 2008, pp. 2629-2634; H. Liu, J. Li, Q. I. Poon, L. D. Seneviratne, and K. Althoefer, "Miniaturized force indentation-depth sensor for tissue abnormality identification," IEEE Int. Conf. Robot. Autom., May 2010, pp. 3654-3659). A sound understanding of wheel-tissue rolling interaction dynamics will facilitate the evaluation of signals from rolling indentation. In this paper, we model the dynamic interactions between a wheeled probe and a soft tissue sample using the ABAQUS FE software package. The aim of this work is to more precisely locate abnormalities within soft tissue organs using RFEM and hence aid surgeons to improve diagnostic ability. The soft tissue is modeled as a nonlinear hyperelastic material with geometrical nonlinearity. The proposed RFEM was validated on a silicone phantom and a porcine kidney sample. The results show that the proposed method can predict the wheel-tissue interaction forces of rolling indentation with good accuracy and can also accurately identify the location and depth of simulated tumors.

Rolling mechanical imaging for tissue abnormality localization during minimally invasive surgery.

We describe a novel approach for the localization of tissue abnormalities during minimally invasive surgery using a force-sensitive wheeled probe. The concept is to fuse the kinaesthetic information from the wheel-tissue rolling interaction into a pseudocolor rolling mechanical image (RMI) to visualize the spatial variation of stiffness within the internal tissue structure. Since tissue abnormalities are often firmer than the surrounding organ or parenchyma, a surgeon then can localize abnormalities by analyzing the image. Initially, a testing facility for validating the concept in an ex vivo setting was developed and used to investigate rolling "wheel-tissue" interaction. A silicone soft-tissue phantom with embedded hard nodules was constructed to allow for experimental comparison between an RMI and a known soft-tissue structure. Tests have also been performed on excised porcine organs to show the efficacy of the method when applied to biological soft tissues. Results indicate that the RMI technique is particularly suited to identifying the stiffness distribution within a tissue sample, as the continuous force measurement along a given rolling trajectory provides repeatable information regarding relative variations in the normal tissue response. When compared to multiple discrete uniaxial indentations, the continuous measurement approach of RMI is shown to be more sensitive and facilitates coverage of a large area in a short period of time. Furthermore, if parametric classification of tissue properties based on a uniaxial tissue indentation model is desirable, the rolling indentation probe can be easily employed as a uniaxial indenter.

Wheel/tissue force interaction: a new concept for soft tissue diagnosis during MIS.

This paper describes a feasibility study of a novel force sensor that can be used to localise tissue abnormalities and provide tactile feedback to the surgeon during minimally invasive surgery. The proposed sensor makes use of an air-supported rigid ball mounted at the end of a tubular shaft to indent the tissue under investigation. Variations in tissue stiffness which cause changes in position of the ball are measured by using an optical sensing scheme. The sensor enables rapid acquisition of tactile information over large areas of soft tissue. Laboratory experiments were conducted to demonstrate the feasibility of the proposed sensor system. The outcome of the conducted experiments shows similarity to the results from a cylindrical wheel-based force sensor [1] which are shown here for comparative purposes.