Integration of Vision and Inertial Sensors for a Surgical Tool Tracking

This paper represents a hybrid Vision/INS system in a microsurgical tool tracking application. Surgical MEMS devices must not only cope with all of the challenges that conventional MEMS devices have, but also address the integration of electronics and signal processing, calibration, reliability, accuracy and testing. A hybrid Vision/INS system with the integration of the Extended Kalman Filter precisely calculates 6D position-orientation of a microsurgical tool during surgery. This configuration guarantees the real-time tracking of the instrument. Ultimately, the vision system supports the IMU to deal with the drift problem but the position error increases dramatically in the absence of the vision system. In this paper, the tool motion modeling is proposed to bind the error in the acceptable range for a short period of missing data. The motion of the tool is modeled and updated at any time that the instrument is in the camera view field. This model is applied to the estimation algorithm whenever the camera is not in line of site and the optical data is missing.

Inverse Wheel Dynamics

Wheel dynamics is a significant component of vehicle dynamics and performance analysis. This paper presents an innovative method of studying wheel dynamics and wheel performance control based on the inverse dynamics formulation of the problem. Such an approach opens up a new way to the optimization and control of both vehicle dynamics and vehicle performance by optimizing and controlling power distribution to the drive wheels. An equation of motion of a wheel is derived first from the wheel power balance equation that makes the equation more general. This equation of motion is considered the basis for studying both direct and inverse wheel dynamics. The development of a control strategy on the basis of the inverse wheel dynamics approach includes wheel torque control that provides a wheel with both the referred angular velocity and rolling radius and also with the required functionals of quality. An algorithm for controlling the angular velocity is presented as the first part in the implementation of the developed strategy of the inverse wheel dynamics/performance control.

Control of a PEM Fuel Cell Cooling System

Previous research has assumed that a perfect Proton Exchange Membrane Fuel Cell (PEMFC) body temperature manager is available. Maintaining this temperature at a desired value can ensure a high reaction efficiency over all operation. However, fuel cell internal body temperature control has not been specifically presented so far. This work presents such control, using a Multiple Input Single Output (MISO) fuel cell cooling system to regulate the internal body temperature of a PEMFC intended for transportation. The cooling system plant is taken from a recently developed hydrogen/air PEMFC total system model. It is linearized and used to design a series of controllers via μ-synthesis. μ-synthesis is chosen since system nonlinearities can be handled as parameter uncertainties. A controller must coordinate the desired fuel cell internal temperature and commanded mass flow rates of the coolant and cooling air. Each linear controller is created for a segment of the expected current density range. Plant parameters are expected to vary over their linearized values in each segment. Also, a common set of μ-synthesis weighting functions has been developed to ease controller design at different operating points. Thus, the nonlinear cooling subsystem can be controlled with a series of current density scheduled linear controllers. Current density step change simulations are presented to compare the controller closed loop performance and open loop response which uses cooling system flow rates taken from an optimal steady state solution of the whole fuel cell system. Furthermore, a closed loop sinusoid response is also given. These show that the closed loop driven internal fuel cell temperature will vary little during operation. However, this will only be true over the range that the cooling system is required to be active.

Combination of Neural Networks and State Vector Feedback Adaptive Control (SVFAC) Technique to Control the Gas Turbine Combustor

Neural networks have been applied very successfully in the identification and control of dynamic systems. When designing a control system to ensure the safe and automatic operation of the gas turbine combustor, it is necessary to be able to predict temperature and pressure levels and outlet flow rate throughout the gas turbine combustor to use them for selection of control parameters. This paper describes a nonlinear SVFAC controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the affection of nonlinear factors contained in controller. The neural network controller learns to produce the input selected by the optimization process. The controller is adaptively trained to force the plant output to track a reference output. Proposed Adaptive control system configuration uses two neural networks: a controller network and a model network. The model network is used to predict the effect of controller changes on plant output, which allows the updating of controller parameters. This paper presents the new adaptive SFVC controller using neural networks with compensation for nonlinear plants. The control performance of designed controller is compared with inverse control method and results have shown that the proposed method has good performance for nonlinear plants such as gas turbine combustor.

An Experimental and Theoretical Investigation of Nonlinear Vibrations of Piezoelectrically-Driven Microcantilevers

The nonlinear vibrations of a piezoelectrically-driven microcantilever beam are experimentally and theoretically investigated. A part of the microcantilever beam surface is covered by a piezoelectric layer, which acts as an actuator. Practically, the first resonance of the beam is of interest, and hence, the microcantilever beam is modeled to obtain the natural frequency theoretically. The bending vibrations of the beam are studied considering the inextensibility condition and the coupling between electrical and mechanical properties in piezoelectric materials. The nonlinear term appears in the form of quadratic due to presence of piezoelectric layer, and cubic form due to geometry of the beam (mainly due to the beam's inextensibility). Galerkin approximation is utilized to discretize the equations of motion. The obtained equation is simulated to find the natural frequency of the system. In addition, method of multiple scales is applied to the equations of motion to arrive at the closed-form solution for natural frequency of the system. The experimental results verify the theoretical findings very closely. It is, therefore, concluded that the nonlinear approach could provide better dynamic representation of the microcantilever than previous linear models.

Three-Degree-of-Freedom Parallel Robot Arm

This paper presents single- and dual-end-effector configurations of a planar three-degree of freedom parallel robot arm designed for automated pick-place operations in vacuum cluster tools for semiconductor and flat-panel-display manufacturing applications. The basic single end-effector configuration of the arm consists of a pivoting base platform, two elbow platforms and a wrist platform, which are connected through two symmetric pairs of parallelogram mechanisms. The wrist platform carries an end-effector, the position and angular orientation of which can be controlled independently by three motors located at the base of the robot. The joints and links of the mechanism are arranged in a unique geometric configuration which provides a sufficient range of motion for typical vacuum cluster tools. The geometric properties of the mechanism are further optimized for a given motion path of the robot. In addition to the basic symmetric single end-effector configuration, an asymmetric costeffective version of the mechanism is derived, and two dual-end-effector alternatives for improved throughput performance are described. In contrast to prior attempts to control angular orientation of the end-effector(s) of the conventional arms employed currently in vacuum cluster tools, all of the motors that drive the arm can be located at the stationary base of the robot with no need for joint actuators carried by the arm or complicated belt arrangements running through the arm. As a result, the motors do not contribute to the mass and inertia properties of the moving parts of the arm, no power and signal wires through the arm are necessary, the reliability and maintenance aspects of operation are improved, and the level of undesirable particle generation is reduced. This is particularly beneficial for high-throughput applications in vacuum and particlesensitive environments.

Lumped Parameter Modeling of a Magnetorheological Fluid Clutch

A lumped-parameter mathematical model of an automotive magnetorheological (MR) fluid fan clutch was developed. This model is able to describe the average fluid temperature, average clutch temperature, and output fan speed as a function of time, input current, and fluid composition. The model also reproduces numerous features of fan operation observed experimentally and revealed a mechanism for some observed cases of hysteresis. However, it fails to capture certain other features which lead us to conclude that phenomena which are not included in the model, e.g., sedimentation and re-suspension, are important to the clutch behavior. In addition, the results indicate that certain physical properties need to be measured over a larger temperature range in order for the model to better predict the clutch behavior.

An Adaptive Fuzzy Controller for Constant Cutting Force in End-Milling Processes

A novel multi-level fuzzy control (MLFC) system is introduced and implemented for online force control of end-milling processes to increase machining productivity and improve workpiece quality, where the cutting force is maintained at its maximum allowable level in the presence of different variations inherent in milling processes, such as tool wear, workpiece geometry and material properties. In the controller design, the fuzzy rules are generated heuristically without any mathematical model of the milling processes. An adaptation mechanism is embedded in to tune the control parameters on-line and the resultant closed-loop system is guaranteed to be stable based on the input-output passivity analysis. In the experiment, the control algorithm is implemented using a National Instrument real-time control computer in an open architecture control environment, where high metal removal rates (MRR) are achieved and the cycle time is reduced by up to 34% over the case without any force controller, and by 22% compared with the regular fuzzy logic controller (FLC), thereby indicating its effectiveness in improving the productivity for actual machining processes.

Smith Predictor Position Control for the Inlet Guide Vane of Centrifugal Air Compressors

Fast and accurate positioning of Inlet Guide Vane (IGV) is critical for flow regulation in centrifugal air compressors, especially for surge avoidance control. Both transport delay and external disturbance are present for IGV position control on a compressed air system. A Smith Predictor based control scheme with the process-model modification is applied to this problem. The adopted control scheme can both compensate for the time delay and effectively reject step disturbances present in the IGV system. Simulation was conducted using simple PI controller and a PI controller with the Smith Predictor with process-model modification. It showed that the proposed control scheme can achieve zero steady-state error with the presence of external disturbance and the settling time was shortened. Robust stability and performance of the system was discussed.

Model Reduction of Modal Representations

Dynamic analysis is extensively used to study the behavior of continuous and lumped parameter linear systems. In addition to the physical space, analyses can also be performed in the modal space where useful frequency information of the system can be extracted. More specifically, modal analysis can be used for the analysis and controller design of dynamic systems, where reduction of model complexity without degrading the accuracy is often required for the efficient use of the model. The reduction of modal models has been extensively studied and many reduction techniques are available. The majority of these techniques use frequency as the metric to determine the reduced model. This paper describes a new method for calculating the modal power of lumped parameter systems with the use of the bond graph representation, which is developed through a power conserving modal decomposition. This method is then used to reduce the size of the model. This technique is based on the previously developed Model Order Reduction Algorithm (MORA), which uses an energy-based metric to generate a series of proper reduced models. An example is provided to demonstrate the calculation of the modal power and the elimination of unimportant modes or modal elements using MORA.