Abstract
Objectives: Increase quality factor of the CNC machine model in comparison with the Uncoupled System by synthesizing Neural Coordinated Control. Synthesis: We synthesized the Neural Coordinated Control algorithm based on the coordinated control algorithm and neural control. Experiment: Using mathematical modeling we compared the synthesized algo-rithms and the uncoupled system using the following criteria: contour error, contour speed error, and score function. Results: The four NCC algorithms were synthesized and trained. The experiment shows that synthesized algorithms have better score function values and better quality factor values in comparison to the reference Uncoupled System. Conclusion: The quality factor of the CNC machine model was successfully in-creased by using the synthesized Neural Coordinated Control algorithm.
Highlights
In this paper we propose the neural coordinated control (NCC) which is based on the coordinated control algorithm and neural control
The coordinated control algorithm is an algorithm with the coupled structure which has several advantages over systems with the uncoupled structure [1,4,5,6]: contour error is minimized directly – the Uncoupled System minimizes contour error indirectly through minimizations of coordinated errors; it is possible to set control priorities by choosing ratio of contour error and contour speed error
None of the synthesized algorithms is pareto optimal by the score function. It is probably connected with the non-convexity of the score function, i.e. during learning process the neural regulators got into local minimums
Summary
Increase of quality factor – ratio of contour speed to contour error – of contour tracking is a relevant task, because systems with high quality factor can make more accurate operations or make the same operations faster, that increases performance of the machine [1,2,3,4,5,6]. In this paper we propose the neural coordinated control (NCC) which is based on the coordinated control algorithm and neural control This combination is chosen by the following reasons:. Neural network regulators can be used to form complex non-linear combinations of input parameters on its outputs This can be used to create unique and specialized control for specific control tasks with relatively small amount of dynamic information [7,8,9]. Train the neural networks using the chosen score function and gradient descent learning algorithm; Compare the synthesized algorithms and the Uncoupled System using the following criteria: the root mean square quality factor, the minimum quality factor, the root mean square contour speed error and the score function
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