Trajectory Adjustment Research Articles

A Fuzzy Model Application to an Adaptive ARC Welding Robot

Basic problem in the arc welding is a good technology performance in the varying vorking conditions. The necessary adaptation is performed by noncontact sensing of the welding edges. The shape and geometry information of the sensed edges is intrinsic indefinite by nature and therefore ill-defined. This presumes the use of fuzzy models for control. In the article a fuzzy estimate of the robot’s states by a noncontact sensor is described. The sensor gives direct measured membership function of robot’s position. Interpretation of this estimate is performed using fuzzy set powers and solution rule forming for robot’s trajectory adjustment. By this way the human interpretation which reflects the internal structure of the model through man’s insight, is changed by fuzzy estimate by robot itself using physical properties of the object. The proposed fuzzy model is algorithmized, programmed and under test in real working conditions with an arc welding adaptive robot.

Optimization of the response of a nuclear reactor plant to changes in demand

Optimal control systems are developed for response to changing load demands by on-line analog solution of a set of matrix Riccati equations. The feedback network parameters are optimized continuously whatever the pattern of load demand or of unplanned plant excursions. Solution is essentially instantaneous by the correct choice of integrator gains. Nonlinearities can be handled by a suitable choice of performance index or by a continuous adjustment of a reference trajectory to be close to the optimum. Plants with positive or negative temperature coefficients can be controlled. The functioning of such control systems is verified by means of a simulated reactor plant.