Recursive algorithms where random observations enter are studied in a fairly general framework. An important feature is that the observations may depend on previous ?outputs? of the algorithm. The considered class of algorithms contains, e.g., stochastic approximation algorithms, recursive identification algorithms, and algorithms for adaptive control of linear systems. It is shown how a deterministic differential equation can be associated with the algorithm. Problems like convergence with probality one, possible convergence points and asymptotic behavior of the algorithm can all be studied in terms of this differential equation. Theorems stating the precise relationships between the differential equation and the algorithm are given as well as examples of applications of the results to problems in identification and adaptive control.

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Analysis of Recursive Stochastic Algorithms

Human welder's experiences and skills are critical for producing quality welds in manual gas tungsten arc welding (GTAW) process. In this paper, a neuro-fuzzy-based human intelligence model is constructed and implemented as an intelligent controller in automated GTAW process to maintain a consistent desired full penetration. An innovative vision system is utilized to real-time measure the specular 3D weld pool surface under strong arc light interference. Experiments are designed to produce random changes in the welding speed and voltage resulting in fluctuations in the weld pool surface. Adaptive neuro-fuzzy inference system (ANFIS) is proposed to correlate the human welder's response to the 3D weld pool surface as characterized by its width, length and convexity. Closed-loop control experiments are conducted to verify the robustness of the proposed controller. It is found that the human intelligence model can adjust the current to robustly control the process to a desired penetration state despite different initial conditions and various disturbances. A foundation is thus established to explore the mechanism and transformation of human welder's intelligence into robotic welding systems.

Dynamic Neuro-Fuzzy-Based Human Intelligence Modeling and Control in GTAW

Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding

Adaptive regulation of super-heated steam temperature: a case study in an industrial boiler

The weld penetration monitoring is a challenging work in modern automated manufacturing industries. The weld quality can be improved with higher depth of penetration and less weld pool area. In this work, various pulse parameters have been varied to investigate their influence on weld penetration in pulsed metal inert gas (P-MIG) welding. The primary objective was to improve the depth of penetration adjusting the pulse parameters. The sound sensor and an infrared pyrometer were used along with arc sensors to properly monitor the depth of weld penetration. Finally, an attempt has also been made to correlate the time domain statistical features of each sensor with weld bead characteristics.

Monitoring of Weld Penetration Using Arc Acoustics

This application paper addresses the use of adaptive predictive control on arc welding trailing centerline temperature control. For tackling the high level of uncertainty in the process the multivariable multipredictive adaptive regulator (MUSMAR) adaptive algorithm, relying on separate estimation of predictive models is used. Experimental results presented include characterization of plant uncertainty and of the effect in control performance of various available knobs, in particular in the presence of plates with variable geometry.

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Multipredictive adaptive control of arc welding trailing centerline temperature

This paper concerns the application of adaptive predictive control for regulating arc welding trailing centerline temperature. Parallel integral action is used for achieving zero steady-state tracking error. As shown by experimental results, the process presents an high level of uncertainty, being impossible to stabilise all possible plant dynamics outcomes with a single linear, time i. ii van™ ant, controller. For tackling the high level of uncertainty in the process the MUSMAR adaptive algorithm, relying on separate estimation of predictive models is used. Experimental results are presented, forming the core of the paper. The possible convergence points of the adaptive controller together with the parallel integral action are characterized through the ODE method for the analysis of stochastic algorithms together with singular perturbation analysis.

T. O. Santos

Papers

Multipredictive adaptive control of arc welding trailing centerline temperature

Adaptive regulation of an highly uncertain process: ARC welding temperature