Adaptive Neural-PID Visual Servoing Tracking Control via Extreme Learning Machine

Abstract

The vision-guided robot is intensively embedded in modern industry, but it is still a challenge to track moving objects in real time accurately. In this paper, a hybrid adaptive control scheme combined with an Extreme Learning Machine (ELM) and proportional–integral–derivative (PID) is proposed for dynamic visual tracking of the manipulator. The scheme extracts line features on the image plane based on a laser-camera system and determines an optimal control input to guide the robot, so that the image features are aligned with their desired positions. The observation and state–space equations are first determined by analyzing the motion features of the camera and the object. The system is then represented as an autoregressive moving average with extra input (ARMAX) and a valid estimation model. The adaptive predictor estimates online the relevant 3D parameters between the camera and the object, which are subsequently used to calculate the system sensitivity of the neural network. The ELM–PID controller is designed for adaptive adjustment of control parameters, and the scheme was validated on a physical robot platform. The experimental results showed that the proposed method’s vision-tracking control displayed superior performance to pure P and PID controllers.

Enhancing Underwater Robot Manipulators with a Hybrid Sliding Mode Controller and Neural-Fuzzy Algorithm

“…These scholarly documents collectively explore remotely operated vehicles (ROVs) for inspection purposes, encompassing a comprehensive review of inspection-class ROVs, the development of an intelligent support system for ROV operators to enhance real-world performance, and the introduction of a novel control method utilizing terminal sliding mode and dynamic damping to improve fault tolerance and predictive capabilities for tracked ROVs [1][2][3], highlighting the roles of robotic arms. These studies also investigate the impact of machining trajectory on grinding force for complex-shaped stone by a robotic manipulator, apply adaptive neural-PID visual servoing tracking control using an Extreme Learning Machine (ELM), and model impedance control with limited interaction power for a 2R planar robot arm [4][5][6] or manipulators [7,8] in conducting complex underwater tasks beyond human reach. These manipulators, operated via controllers like joysticks, are designed for diverse missions, varying in size and strength, and are equipped with hydraulic or electric power, along with sensory or visual aids.…”

Optimal Design of a Five-Bar Planar Manipulator and Its Controller by Using Different Algorithms for Minimum Shaking Forces and Moments for the Largest Trajectory in a Usable Workspace

“…In recent years, various control algorithms seem to work together or nested. [38][39][40]. In [40], the extreme learning machine-PID controller has shown that it outperforms the pure P and PID controllers with the adaptive tuning of the control parameters.…”

“…[38][39][40]. In [40], the extreme learning machine-PID controller has shown that it outperforms the pure P and PID controllers with the adaptive tuning of the control parameters. Optimization methods such as GA [38] and PSO [39] are used, especially to select the coefficients of the widely used PID controller.…”