Mechanical CAD Design and Development of an Adaptive Control Algorithm for Autonomous Robotics Systems in Manufacturing

Abstract

The rise of smart automation and Industry 4.0 requires flexible, precise, and adaptive capabilities in autonomous robotic manufacturing. But traditional robotic control systems are based on rigid parameters and deterministic models, making them difficult for complex environments as they undergo changes. Moreover, existing studies tend to separate controller algorithms from mechanical system optimization, resulting in a lack of integrated robot systems. The research proposes to design and develop a CAD mechanical integrated robotic system with an adaptive control algorithm to improve motion control and adaptability in an autonomous manufacturing process. The study applied CAD and Denavit-Hartenberg parameters in kinematic and dynamic modeling of the robotic manipulator system. This physical model was integrated with closed-loop feedback from the actuators, Model Reference Adaptive Control (MRAC), Iterative Learning Control (ILC), and robust adaptive controller techniques. The entire system was tested in a SolidWorks simulation with different industrial scenarios. This integrated adaptive controller showed remarkable improvement over traditional approaches, achieving an 80.3% increase in tracking performance and a trajectory error ≤ 0.5 mm with heavy loads and a high level of external disturbances. The step response of the actuators was extremely fast, with a settling time of 0.35 seconds and 92% disturbance rejection. The ILC simulation for repetitive industrial manufacturing achieved 97.1% convergence, reducing the root mean square (RMS) error to 0.05 mm in just eight iterations. The findings verified that combining physical CAD mechanical design with advanced adaptive control algorithms substantially enhanced the operational precision, robustness, and overall efficiency of industrial robots.

Keywords

Applied Science - Engineering, Convergence Science - Invention and Design

Citation

Publication Details

ISSN: Online ISSN: 3143-5653

License: Author-retained; open access display by NSRI unless a separate article license states otherwise.

Peer review status: NSRI uses editorial and scholarly review. When appropriate, manuscripts may undergo blinded review by reviewers with relevant subject knowledge.

AI disclosure: No AI disclosure is attached to this public record unless stated in the manuscript.

Conflict of interest statement: No conflict of interest statement is attached to this public record unless stated in the manuscript.

References

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