An Unmanned Forklift Steering Control System Utilizing IoT and PID with a Kalman Filter for Enhanced Stability and Precision
Received: 6 January 2026 | Revised: 10 February 2026 | Accepted: 23 February 2026 | Online: 4 April 2026
Corresponding author: Dechrit Maneetham
Abstract
This study focuses on developing an unmanned forklift steering control system utilizing the Internet of Things (IoT) for remote operations. The objective was to integrate a Proportional Integral Derivative (PID) control system to accurately measure the rotation speed and angular position of the steering wheel. Sensor data is refined using the Kalman Filter technique (KF), which effectively reduces noise and improves the accuracy of steering angle data, leading to smoother and more stable steering control during forklift turns. This study utilizes a rear-wheel steering configuration for autonomous control via microcontrollers and IoT-based remote operation systems. Kinematic analysis governs motion and steering by calculating the Instantaneous Center of Rotation (ICR) based on the vehicle's linear and angular velocities. A limit switch facilitates accurate angular position tracking and ensures system consistency for steering wheel homing. The feedback control system employs a wheel encoder and gyroscopic sensors with a PID controller to maintain precise steering through motor speed adjustments that correct errors. Experimental results demonstrate the ability of the control system to accurately adjust and follow the desired trajectory. Initially, during the turning phase, the vehicle may not adjust its direction in time, but the control system subsequently adjusts to ensure accurate path following. The angular error is high when the vehicle reaches a 90-degree corner but decreases to near zero as it moves along the straight path, indicating effective alignment with the desired direction. Control input shows linear velocity decreases near corners for smoother turns and increases on straight paths, while turning rate is high at corners and decreases on straight paths, reflecting precise steering response. Lateral error increases during turns but decreases to near zero on straight paths, demonstrating the control system's ability to maintain proximity to the desired path.
Keywords:
unmanned forklift, IoT, PID control, Kalman filter, steering control, enhanced stabilityDownloads
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Copyright (c) 2026 Sillapachai Klinklai, Dechrit Maneetham, Petrus Sutyasadi, Myo Min Aung
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