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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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A transductive neuro-fuzzy controller: application to a drilling process.

Agustín Gajate¹, Rodolfo E Haber, Pastora I Vega

¹Centro de Automática y Robótica, Consejo Superior de Investigaciones Científicas, Madrid 28500, Spain. agajate@iai.csic.es

IEEE Transactions on Neural Networks

|July 28, 2010

Summary

A new transductive neuro-fuzzy inference method effectively controls force in high-performance drilling. This approach offers improved transient response and error reduction compared to adaptive neuro-fuzzy inference systems, minimizing tool wear risks.

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Area of Science:

Intelligent control systems
Fuzzy logic and neural networks
Manufacturing process optimization

Background:

Complex systems exhibit time-varying behavior and uncertainty, challenging traditional control methods.
Neuro-fuzzy inference systems offer a hybrid approach to model and control such complexities.
Existing adaptive neuro-fuzzy inference systems (ANFIS) have limitations in dynamic process control.

Purpose of the Study:

To design and apply a novel transductive neuro-fuzzy inference method for high-performance drilling force control.
To analyze the dynamic modeling, computational efficiency, and real-time viability of the proposed system.
To assess the impact of neuro-fuzzy system topology on control performance.

Main Methods:

A transductive reasoning method was employed to create localized neuro-fuzzy models for input/output data.
Direct and inverse dynamics of the drilling process were modeled using this transductive strategy.
The neuro-fuzzy models were integrated into an internal model control (IMC) scheme for comparative analysis against ANFIS.

Main Results:

The transductive neuro-fuzzy control system demonstrated superior transient response without overshoot.
The proposed method achieved better error-based performance indices compared to the ANFIS-based system.
The IMC system effectively mitigated the impact of increasing cutting forces with drill depth.

Conclusions:

The transductive neuro-fuzzy inference approach is a viable and effective method for complex process control.
This method enhances drilling process stability and reduces the risk of tool wear and breakage.
The study validates the synergy of fuzzy, neural, and transductive strategies for robust control applications.