Iterative Learning Control with Mismatch Compensation for Residual Vibration Suppression in Delta Robots

TL;DR

This paper introduces an adaptive iterative learning control method with mismatch compensation and input shaping to suppress residual vibrations and enhance tracking accuracy in Delta robots, considering their flexible dynamics.

Contribution

It presents a novel adaptive iterative learning controller with mismatch compensation based on fuzzy logic and input shaping tailored to Delta robot dynamics.

Findings

The proposed controller guarantees convergence of tracking errors to zero.

Simulations demonstrate improved vibration suppression and tracking accuracy.

The method effectively compensates for model mismatches in flexible robotic systems.

Abstract

Unwanted vibrations stemming from the energy-optimized design of Delta robots pose a challenge in their operation, especially with respect to precise reference tracking. To improve tracking accuracy, this paper proposes an adaptive mismatch-compensated iterative learning controller based on input shaping techniques. We establish a dynamic model considering the electromechanical rigid-flexible coupling of the Delta robot, which integrates the permanent magnet synchronous motor. Using this model, we design an optimization-based input shaper, considering the natural frequency of the robot, which varies with the configuration. We proposed an iterative learning controller for the delta robot to improve tracking accuracy. Our iterative learning controller incorporates model mismatch where the mismatch approximated by a fuzzy logic structure. The convergence property of the proposed controller is proved using a Barrier Composite Energy Function, providing a guarantee that the tracking errors along the iteration axis converge to zero. Moreover, adaptive parameter update laws are designed to ensure convergence. Finally, we perform a series of high-fidelity simulations of the Delta robot using Simscape to demonstrate the effectiveness of the proposed control strategy.