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Updated: Jul 7, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

New approach to intelligent control systems with self-exploring process.

Liang-Hsuan Chen¹, Cheng-Hsiung Chiang

¹Dept. of Ind. Manage. Sci., Nat. Cheng Kung Univ., Taiwan.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 2, 2008

Summary

This summary is machine-generated.

This study introduces a self-exploring-based intelligent control system (SEICS) for adaptive control. The SEICS enhances robotic path-planning in uncertain environments by exploring new control actions and generating fuzzy rules.

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Intelligent control systems require adaptability in dynamic and information-scarce environments.
Existing systems may struggle with real-time performance optimization and rule generation.

Purpose of the Study:

To propose a novel self-exploring-based intelligent control system (SEICS) for enhanced adaptive control.
To improve the performance of control systems in challenging environments through self-exploration and rule generation.

Main Methods:

The SEICS integrates a fuzzy neural network (FNN) controller, a performance evaluator (PE), and an adaptive mechanism.
The adaptor comprises an action explorer (AE) using a multiobjective genetic algorithm (GA) and a rule generator (RG).
AE explores new actions via a three-stage process, and RG translates actions into fuzzy rules.

Main Results:

Simulations demonstrated the SEICS's effectiveness in robotic path-planning.
The robot successfully reached its target in environments with limited information and frequent changes.
The adaptive nature of the SEICS allowed for successful navigation under uncertainty.

Conclusions:

The proposed SEICS provides a robust framework for adaptive intelligent control.
The system's ability to generate new control behaviors enhances its applicability in complex scenarios.
SEICS shows significant potential for improving robotic navigation and control in unpredictable conditions.