Robot welding process control

This final report documents the development and installation of software and hardware for Robotic Welding Process Control. Primary emphasis is on serial communications between the CYRO 750 robotic welder, Heurikon minicomputer running Hunter & Ready VRTX, and an IBM PC/AT, for offline programming and control and closed-loop welding control. The requirements for completion of the implementation of the Rocketdyne weld tracking control are discussed. The procedure for downloading programs from the Intergraph, over the network, is discussed. Conclusions are made on the results of this task, and recommendations are made for efficient implementation of communications, weld process control development, and advanced process control procedures using the Heurikon

Identification of Hidden Failures in Process Control Systems Based on the HMG Method

We will continue here the research work, the goal of which was to introduce the notion of nondeterministic aggregation operators and study their properties, even in relation to classification systems and the associated learning problem. Here we will concentrate mostly on the notion of the nondeterministic aggregation system and its relation with deterministic ones. We will also see how such a model extends a discretized version of a model of participatory learning with an arousal background mechanism

Adaptive process control in rubber industry

This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive. The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good results even using only a few training samples

Process for control of cell division

A method of controlling mitosis of biological cells was developed, which involved inducing a change in the intracellular ionic hierarchy accompanying the cellular electrical transmembrane potential difference (Esubm) of the cells. The ionic hierarchy may be varied by imposing changes on the relative concentrations of Na(+), K(+) and Cl(-), or by directly imposing changes in the physical Esubm level across the cell surface

Gaussian process model based predictive control

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark