Wei Tech Ang

Feedforward Controller With Inverse Rate-Dependent Model for Piezoelectric Actuators in Trajectory-Tracking Applications

Effective employment of piezoelectric actuators in microscale dynamic trajectory-tracking applications is limited by two factors: 1) the intrinsic hysteretic behavior of piezoelectric ceramic and 2) structural vibration as a result of the actuators own mass, stiffness, and damping properties. While hysteresis is rate-independent, structural vibration increases as the piezoelectric actuator is driven closer to its resonant frequency. Instead of separately modeling the two interacting dynamic effects, this work treats their combined effect phenomenologically and proposes a rate-dependent modified Prandtl-Ishlinskii operator to account for the hysteretic nonlinearity of a piezoelectric actuator at varying actuation frequency. It is shown experimentally that the relationship between the slope of the hysteretic loading curve and the rate of control input can be modeled by a linear function up to a driving frequency of 40 Hz

Toward active tremor canceling in handheld microsurgical instruments

This paper describes research in active instruments for enhanced accuracy in microsurgery. The aim is to make accuracy enhancement as transparent to the surgeon as possible. Rather than using a robotic arm, we have taken the novel approach of developing a handheld instrument that senses its own movement, distinguishes between desired and undesired motion, and deflects its tip to perform active compensation of the undesired component. The research has therefore required work in quantification and modeling of instrument motion, filtering algorithms for tremor and other erroneous movements, and development of handheld electromechanical systems to perform active error compensation. The paper introduces the systems developed in this research and presents preliminary results.

Design of all-accelerometer inertial measurement unit for tremor sensing in hand-held microsurgical instrument

We present the design of an all-accelerometer inertial measurement unit (IMU). The IMU forms part of an intelligent hand-held microsurgical instrument that senses its own motion, distinguishes between hand tremor and intended motion, and compensates in real-time the erroneous motion. The new IMU design consists of three miniature dual-axis accelerometers, two of which are housed in a sensor suite at the distal end of the instrument handle, and one located at the proximal end close to the instrument tip. By taking the difference between the accelerometer readings, we decouple the inertial and gravitational accelerations from the rotation-induced (centripetal and tangential) accelerations, hence simplifies the kinematic computation of angular motions. We have shown that the error variance of the Euler orientation parameters /spl theta//sub x/, /spl theta//sub y/ and /spl theta//sub z/ is inversely proportional to the square of the distance between the three sensor locations. Comparing with a conventional three gyros and three accelerometers IMU, the proposed design reduces the standard deviation of the estimates of translational displacements by 29.3% in each principal axis and those of the Euler orientation parameters /spl theta//sub x/, /spl theta//sub y/ and /spl theta//sub z/ by 99.1%, 99.1% and 92.8% respectively.

Physical model of a MEMS accelerometer for low-g motion tracking applications

This paper develops a physical model of a MEMS capacitive accelerometer in order to use the accelerometer effectively in low-g motion tracking applications. The proposed physical model includes common physical parameters used to rate an accelerometer: scale factor, bias, and misalignment. Simple experiments used to reveal the behavior and characteristics of these parameters are described. A phenomenological modeling method is used to establish mathematical representations of these parameters in relation to errors such as nonlinearity, hysteresis, cross-axis effect, and temperature effect, without requiring a complete understanding of the underlying physics. Experimental results are presented, in which the physical model reduces RMSE by 93.1% in comparison with the manufacturers recommended method.

Modeling rate-dependent hysteresis in piezoelectric actuators

Hysteresis of a piezoelectric actuator is rate dependent. Most hysteresis models are based on elementary rate independent operators and are not suitable for modeling actuator behavior across a wide frequency band. This work proposes a rate dependent modified Prandtl-Ishlinskii (PI) operator to account for the hysteresis of a piezoelectric actuator at varying frequency. We have shown experimentally that the relationship between the slope of the hysteretic loading curve and the rate of control input can be modeled by a linear function. The proposed rate-dependent hysteresis model is implemented for open-loop control of a piezoelectric actuator. In experiments tracking multi-frequency nonstationary motion profiles, it consistently outperforms its rate-independent counterpart by a factor of two in maximum error and a factor of three in rms error.

Nonlinear Regression Model of a Low-

This paper proposes a nonlinear regression model of a microelectromechanical systems capacitive accelerometer, targeted to be used in tilt sensing and low-g motion-tracking applications. The proposed model for the accelerometers deterministic errors includes common physical parameters used to rate an accelerometer: scale factor, bias, and misalignment. Simple experiments used to reveal the behavior and characteristics of these parameters are described. A phenomenological modeling method is used to establish mathematical representations of these parameters in relation to errors such as nonlinearity and cross-axis effect, without requiring a complete understanding of the underlying physics. Tilt and motion-sensing experiments show that the proposed model reduces sensing errors to a level close to the residual stochastic noise

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Real-time accurate human posture tracking in unconstrained environments provides an enabling technology for physicians and other care providers to monitor the movements of their patients in real-life situations. Constructing a posture tracking system with the form factor suitable for human wear requires the development of miniature units that can be attached to the limb segments of interest in an unobtrusive way. Simultaneously, fast algorithms that can produce real-time posture estimates at sufficient rates are needed. In this paper, we focus on the development of efficient and accurate algorithms that compute the human posture information from low-cost miniature inertial and magnetic sensors. We present a new technique that computes posture estimates from the sensor data 23.8 times faster than the most efficient previously proposed technique, and simultaneously increases the accuracy of the estimates.

MEMS Accelerometer

Accurate filtering of physiological tremor is extremely important in robotics assisted surgical instruments and procedures. This paper focuses on developing single stage robust algorithms for accurate tremor filtering with accelerometers for real-time applications. Existing methods rely on estimating the tremor under the assumption that it has a single dominant frequency. Our time-frequency analysis on physiological tremor data revealed that tremor contains multiple dominant frequencies over the entire duration rather than a single dominant frequency. In this paper, the existing methods for tremor filtering are reviewed and two improved algorithms are presented. A comparative study is conducted on all the estimation methods with tremor data from microsurgeons and novice subjects under different conditions. Our results showed that the new improved algorithms performed better than the existing algorithms for tremor estimation. A procedure to separate the intended motion/drift from the tremor component is formulated.

An efficient real-time human posture tracking algorithm using low-cost inertial and magnetic sensors

This paper presents the theory and modeling of a quaternion-based augmented state Kalman filter for real-time orientation tracking of a handheld microsurgical instrument equipped with a magnetometer-aided all-accelerometer inertial measurement unit (IMU). The onboard sensing system provides two complementary sources of orientation information. The all-accelerometer IMU provides a high resolution but drifting angular velocity estimate, while the magnetic north vector is combined with the estimated gravity vector to yield a non-drifting but noisy orientation estimate. Analysis of the dominant stochastic noise components of the sensors and derivation of the noise covariance are presented. The proposed Kalman filter obtains a non-drifting orientation estimate with improved resolution by incorporating the motion dynamics of the instrument during microsurgery and models the angular velocity drift explicitly as extra dynamic states.

Estimation of Physiological Tremor from Accelerometers for Real-Time Applications

Presents the development and. initial experimental results of the first prototype of Micron, an active hand-held instrument to sense and compensate physiological tremor and other unwanted movement during vitreoretinal microsurgery. The instrument incorporates six inertial sensors, allowing the motion of the tip to be computed. The motion captured is processed to discriminate between desired and undesired components of motion. Tremor canceling is implemented via the weighted-frequency Fourier linear combiner (WFLC) algorithm, and compensation of non-tremorous error via a neural network technique is being investigated. The instrument tip is attached to a three-degree-of-freedom parallel manipulator with piezoelectric actuation. The actuators move the tool tip in opposition to the tremor, thereby suppressing the erroneous motion. Motion canceling experiments with oscillatory motions in the frequency band of physiological tremor show that Micron is able to reduce error amplitude by 45.3% in 1-D tests and 37.2% in 3-D tests.