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.