Estananto Estananto

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About

Estananto Estananto is a researcher focused on advancing automation and robotics through intelligent control systems and embedded technologies. His work primarily addresses the intersection of manufacturing automation, robotic dynamics, and sensor-based navigation. A key contribution is the design and implementation of an Automatic Guided Vehicle (AGV) system that uses RFID for precise position tracking, directly improving the efficiency of goods distribution in production chains—a foundational problem in modern manufacturing. This work has garnered 4 citations, reflecting its practical relevance. More recently, Estananto has explored neural network control for robotic systems, proposing a multilayer perceptron-based alternative to traditional PID and computed torque control for a 3DOF robot arm. This 2024 study, with 2 citations, demonstrates his ongoing commitment to integrating machine learning with real-time robotic control. By combining sensor integration, neural network modeling, and applied automation, Estananto contributes to the development of smarter, more adaptive manufacturing systems, offering valuable insights for students and researchers interested in industrial robotics and intelligent control.