Guy Gauthier

High resolution positron emission tomography with a prototype camera based on solid state scintillation detectors

The prototype of a high-resolution PET (positron emission tomography) camera consisting of two opposite arrays of detectors with independent solid-state readout was built and tested. The basic detector unit is the RCA C30994 detector module consisting of two 3-mm*5-mm*20-mm BGO scintillators, each coupled to one silicon avalanche photodiode. The two-dimensional stacking capability of the module allows a high-resolution multiring detection system to be assembled without crystal coding. The prototype was used to simulate a 31-cm-diameter dual ring tomograph suitable for animal studies. Coincident detector pair resolution was measured, and the contributions to resolution loss were estimated using a platinum-sheathed 0.75 mm /sup 68/Ge line source. The intrinsic resolution is 1.9 mm FWHM (full width at half maximum), 3.5 mm FWTM at the center of the field. The reconstructed point spread function resolution in a stationary mode is 2.3 mm FWHM at the center and 3.1 mm FWHM (tangential), 3.9 mm FWHM (radial) 5 cm from the center. The axial resolution is less than 3.5 mm FWHM throughout the field. The ring sensitivity for an animal system is 67 kcps/mCi in air for an axial line source and 3.3 kcps/ mu Ci/ml for a 10-cm-diameter cylinder of /sup 22/Na solution. >

Terminal Iterative Learning Control design with singular value decomposition decoupling for thermoforming ovens

Terminal Iterative Learning Control (TILC) is a cycle-to-cycle control approach that can be used on thermoforming oven. TILC automatically tune the heater temperature setpoints such that the temperature at the surface of the plastic sheet tracks a desired temperature profile. Industrial thermoforming ovens can have a large number of temperature sensor (inputs) and heaters (outputs) which makes the design of TILC difficult. This paper presents the design of a TILC using the singular value decomposition decoupling technique. With this tool, the TILC design is facilitated for industrial thermoforming oven.

Robust design of terminal ILC with H ∞ mixed sensitivity approach for a thermoforming oven

This paper presents a robust design approach for terminal iterative learning control (TILC). This robust design uses the H∞ mixed-sensitivity technique. An industrial application is described where TILC is used to control the reheat phase of plastic sheets in a thermoforming oven. The TILC adjusts the heater temperature setpoints such that, at the end of the reheat cycle, the surface temperature map of the plastic sheet will converge to the desired one. Simulation results are included to show the effectiveness of the control law.

Terminal Iterative Learning Control Applied to Thermoforming Machine Reheat Phase

This paper shows the use of terminal iterative learning control (TILC) algorithm to control the plastic sheet reheat phase of thermoforming machine. We use TILC as cycle to cycle control because we want to improve the adjustment of heater temperature setpoint that is actually done manually. We introduce the model of a thermoforming machine and the TILC algorithm tested on a thermoforming machine. Experimental results are included to show the effectiveness of the control

High order robust Terminal Iterative Learning Control design using Genetic Algorithm

In the thermoforming industry, the heater temperature set points can be automatically tuned with Terminal Iterative Learning Control (TILC). This cycle-to-cycle control is used to adjust the heater temperature set points so that the temperature profile at the surface of the plastic sheet converges to the desired one. The structure of the proposed high order TILC is based on the Internal Model Control (IMC). The robustness of a closed-loop system with this TILC algorithm is measured using the H∞ Mixed Sensitivity approach. A Genetic Algorithm (GA) is used to find a high order TILC controller parameters giving the most robust closed-loop system. Simulation results are included to show the effectiveness of those designed robust TILC algorithms.

Robust design of Terminal ILC with an Internal Model Control using μ-analysis and a genetic algorithm approach

The thermoforming heater temperature setpoints can be automatically tuned with cycle-to-cycle control. Terminal Iterative Learning Control (TILC) is used to adjust the heater temperature setpoints so that the temperature profile at the surface of the plastic sheet converges to the desired temperature. Industrial thermoforming ovens generally have a large number of temperature sensors and heaters, which makes the design of TILC difficult. The proposed TILC design is based on Internal Model Control (IMC). The robustness of a closed-loop system with this TILC algorithm is measured using the μ-analysis approach. A Genetic Algorithm (GA) is used to find the IMC exponential filter parameters giving the most robust closed-loop system. Simulation results are included to show the effectiveness of this robust TILC algorithm.

Terminal Iterative Learning Control with Internal Model Control applied to the thermoforming reheat phase

Terminal Iterative Learning Control (TILC) is a cycle-to-cycle control approach that can be used on a thermoforming oven. TILC automatically tunes the heater temperature setpoints so that the temperature at the surface of the plastic sheet tracks a desired temperature profile. Industrial thermoforming ovens can have a large number of temperature sensors (inputs) and heaters (outputs), which makes TILC design difficult. This paper presents the design of a TILC using the Internal Model Control (IMC) approach.

Modeling and Simulation of a Multivariable Process Control

This paper presents a comparative survey of different multivariable techniques applied to process control. The modeling of the physical system and real time simulations are also presented using different PID structures and applied for the regulation of level and the temperature of a water reservoir control process. The structure of the multivariable control system has been implanted using LabViewreg software. This structure uses two control loops, the first for the level regulation and the second for the regulation of temperature. Five different PID controllers are included in this paper (Ziegler_Nichols, ITAE, IMC, poles placement and dual loop) and real time results are presented

Inverse Kinematics for a Novel Rehabilitation Robot for Lower Limbs

We present in this paper a new structure of cable robot for rehabilitation of lower limbs. The proposed concept is distinguished by the ability to synchronize and coordinate the joints of the hip, knee and foot. It explains a modeling approach to find explicit relations expressing the relationship between the desired trajectories in the physiological limb and the articular variables to be applied to the robot motors. The proposed robot (KINECAB) can be deployed in two configurations. In this work, we study a configuration that allows the two lower limbs to be manipulated to reproduce planar movements helping human walk or similar exercises. The inverse kinematics developed will be analyzed on the basis of a kinematic reference model of the physiological members. Validation tests by simulation and experimentation are also proposed. A patent application is deposited by ETS, University of Quebec (Application Number PCT/CA2016/051376).

Adaptive backstepping control of mobile manipulator robot based on virtual decomposition approach

This paper presents an adaptive backstepping control scheme for a mobile manipulator robot based on the virtual decomposition control (VDC). The control scheme was applied on a three degrees of freedom manipulator arm mounted on a two degrees of freedom mobile platform to track a desired workspace trajectory. The desired joint space trajectory is obtained by using the inverse kinematics. In this paper, the mobile manipulator has N degrees of freedom, divided virtually into N subsystems. The validation of the proposed scheme is proved in real time implementation. The experimental results show the effectiveness of the proposed control schemes based on VDC approach.