A control and monitoring system for multiple-sensor industrial robots

Barnes, D. P., Lee, M. H., Hardy, N. W. (1983). A control and monitoring system for multiple-sensor industrial robots. In Proc. 3rd. Int. Conf. Robot Vision and Sensory Controls, Cambridge, MA. USA., 471-479.

Data Reduction of Power System Disturbances using Wavelet Transforms for Network Operation and Control

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the reduction properties of the wavelet transform using real power system data and discusses the application of the reduction method for information transfer in network communications.

Using Graphs to Visualise and Detect Anomalies in Access Control System Transactions

Physical Access Control Systems are commonly used to secure doors in buildings such as airports, hospitals, government buildings and offices. These systems are designed primarily to provide an authentication mechanism, but they also log each door access as a transaction in a database. Unsupervised learning techniques can be used to detect inconsistencies or anomalies in the mobility data, such as a cloned or forged Access Badge, or unusual behaviour by staff members. In this paper, we present an overview of our method of inferring directed graphs to represent a physical building network and the flows of mobility within it. We demonstrate how the graphs can be used for Visual Data Exploration, and outline how to apply algorithms based on Information Theory to the graph data in order to detect inconsistent or abnormal behaviour.

Improvement in clinical step and shoot intensity modulated radiation therapy delivery accuracy on an integrated linear accelerator control system

Purpose: The dose delivery accuracy of 30 clinical step and shoot intensity modulated radiation therapy plans was investigated using the single integrated multileaf collimator controller of the Varian Truebeam linear accelerator (linac) (Varian Medical Systems, Palo Alto, CA) and compared with the dose delivery accuracy on a previous generation Varian 2100CD C-Series linac.

Methods and Materials: Ten prostate, 10 prostate and pelvic node, and 10 head-and-neck cases were investigated in this study. Dose delivery accuracy on each linac was assessed using Farmer ionization chamber point dose measurements, 2-dimensional planar ionization chamber array measurements, and the corresponding Varian dynamic log files. Absolute point dose measurements, fluence delivery accuracy, leaf position accuracy, and the overshoot effect were assessed for each plan.

Results: Absolute point dose delivery accuracy increased by 1.5% on the Truebeam compared with the 2100CD linac. No improvement in fluence delivery accuracy between the linacs, at a gamma criterion of 3%/3 mm was measured using the 2-dimensional ionization chamber array, with median (interquartile range) gamma passing rates of 98.99% (97.70%-99.72%) and 99.28% (98.26%-99.75%) for the Truebeam and 2100CD linacs, respectively. Varian log files also showed no improvement in fluence delivery between the linacs at 3%/3 mm, with median gamma passing rates of 99.97% (99.93%-99.99%) and 99.98% (99.94%-100%) for the Truebeam and 2100CD linacs, respectively. However, log files revealed improved leaf position accuracy and fluence delivery at 1%/1 mm criterion on the Truebeam (99.87%; 99.78%-99.94%) compared with the 2100CD linac (97.87%; 91.93%-99.49%). The overshoot effect, characterized on the 2100CD linac, was not observed on the Truebeam.

Conclusions: The integrated multileaf collimator controller on the Varian Truebeam improves clinical treatment delivery accuracy of step and shoot intensity modulated radiation therapy fields compared with delivery on a Varian C-series linac. © 2014.

Automatic extruder for processing recycled polymers with ultrasound and temperature control system

Melt viscosity is one of the main factors affecting product quality in extrusion processes particularly with regard to recycled polymers. However, due to wide variability in the physical properties of recycled feedstock, it is difficult to maintain the melt viscosity during extrusion of polymer blends and obtain good quality product without generating scrap. This research investigates the application of ultrasound and temperature control in an automatic extruder controller, which has ability to maintain constant melt viscosity from variable recycled polymer feedstock during extrusion processing. An ultrasonic modulation system has been developed and fitted to the extruder prior to the die to convey ultrasonic energy from a high power ultrasonic generator to the polymer melt. Two separate control loops have been developed to run simultaneously in one controller: the first loop controls the ultrasonic energy or temperature to maintain constant die pressure, the second loop is used to control extruder screw speed to maintain constant throughput at the extruder die. Time response and energy consumption of the control methods in real-time experiments are also investigated and reported this paper.

Reach & grasp therapy: Design and control of a 9-DOF robotic neuro-rehabilitation system

This paper presents the Gentle/G integrated system for reach & grasp therapy retraining following brain injury. The design, control and integration of an experimental grasp assistance unit is described for use in robot assisted stroke rehabilitation. The grasp assist unit is intended to work with the hardware and software of the Gentle/S robot although the hardware could be adapted to other rehabilitation applications. When used with the Gentle/S robot a total of 6 active and 3 passive degrees of freedom are available to provide active, active assist or passive grasp retraining in combination with reaching movements in a reach-grasp-transfer-release sequence.

Dynamic recurrent neural network for system identification and control

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

Modelling and control of Hammerstein system using B-spline approximation and the inverse of De Boor algorithm

In this article a simple and effective controller design is introduced for the Hammerstein systems that are identified based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The controller is composed by computing the inverse of the B-spline approximated nonlinear static function, and a linear pole assignment controller. The contribution of this article is the inverse of De Boor algorithm that computes the inverse efficiently. Mathematical analysis is provided to prove the convergence of the proposed algorithm. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.