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<p>The demand for affordable, accurate, and fast manufacturing has led to the use of robotic systems in industries, such as automotive procedures like welding necessitate accuracy and specialized labor. However, commanding robots in complicated tasks remains difficult due to nonlinear dynamics and multi-joint designs. The exploration intends to improve robotic arm control in smart manufacturing contexts, especially welding, by generating an energy-efficient control system that reduces errors, improves response time, and consumes less energy. Real-time sensor data is processed using the Kalman filter to reduce noise and provide accurate state estimation for adaptive robotic control. The actuator for the robotic arm is a nonlinear robotic controller, which uses a control technique termed Migrating Birds Optimized Fuzzy Proportional-Integral-Derivative (MigBO-Fuzzy PID) that combines Fuzzy PID control and (MigBO). The MigBO approach improves controller parameters offline, lowering the complexity of the robotic arm’s control system. Simulations are used to conduct a comparison of controllers, including the fuzzy surveillance controller with MigBO. The MigBO-Fuzzy PID controller delivered 0.10% overshoot and 0.040&#xa0;s rise time and 0.002 steady-state error for improved robotic welding accuracy, response speed and system stability. The designed controller successfully addresses the issues of operating a robotic arm in Industry 4.0 manufacturing settings. It improves control precision and efficiency significantly, making it a promising alternative for manufacturing quality assurance and control, especially in time-sensitive, skill-intensive tasks such as welding.</p>

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Adaptive and energy-aware motion control strategies for industrial robots in smart manufacturing

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • R. M. Rajeshwari,
  • Rajeshwari Sharma,
  • Rupesh Gupta,
  • K. Manivannan,
  • Elangovan Muniyandy,
  • R. Mohan Kumar

摘要

The demand for affordable, accurate, and fast manufacturing has led to the use of robotic systems in industries, such as automotive procedures like welding necessitate accuracy and specialized labor. However, commanding robots in complicated tasks remains difficult due to nonlinear dynamics and multi-joint designs. The exploration intends to improve robotic arm control in smart manufacturing contexts, especially welding, by generating an energy-efficient control system that reduces errors, improves response time, and consumes less energy. Real-time sensor data is processed using the Kalman filter to reduce noise and provide accurate state estimation for adaptive robotic control. The actuator for the robotic arm is a nonlinear robotic controller, which uses a control technique termed Migrating Birds Optimized Fuzzy Proportional-Integral-Derivative (MigBO-Fuzzy PID) that combines Fuzzy PID control and (MigBO). The MigBO approach improves controller parameters offline, lowering the complexity of the robotic arm’s control system. Simulations are used to conduct a comparison of controllers, including the fuzzy surveillance controller with MigBO. The MigBO-Fuzzy PID controller delivered 0.10% overshoot and 0.040 s rise time and 0.002 steady-state error for improved robotic welding accuracy, response speed and system stability. The designed controller successfully addresses the issues of operating a robotic arm in Industry 4.0 manufacturing settings. It improves control precision and efficiency significantly, making it a promising alternative for manufacturing quality assurance and control, especially in time-sensitive, skill-intensive tasks such as welding.