Designing an AI-Based Adaptive Controller Augmented with a System Identifier for a Micro-Class Robot Equipped with a Vibrating Actuator

TL;DR

This paper presents an AI-based adaptive control system with a neural network and system identifier for a vibrating-actuator micro-robot, enabling precise path tracking despite nonlinear dynamics.

Contribution

It introduces a novel neural network-based adaptive controller combined with a system identifier tailored for a micro-robot with vibrating actuators, improving control accuracy.

Findings

Effective path tracking with near-zero errors

Handles nonlinear system behavior

Simulations confirm control robustness

Abstract

In this paper, an adaptive control scheme based on using neural networks is designed to guarantee the desired behavior of a micro-robot which is equipped with vibrating actuators and follows the principle of slip-stick movement. There are two tiny shaking motors which have been utilized to run the micro-class robotic system. Dynamic modeling equations are expressed by considering the spring coefficient of the bases. After that, the effect of the spring on the foundations was investigated. In addition to designing neural-based controller, an AI-based system identifier has been developed to help the controller update its parameters and achieve its desired targets. Using this method, several specific paths for the movement of this micro robot are simulated. Based on the simulation results, the proposed controlling strategy guarantees acceptable performance for tracking different paths due to plotted near-zero errors and handles the nonlinear behavior of the micro-robot system.