# Visual Predictive Control for Robotics with RBF-EKF Coupled State-Disturbance Estimation and Task-Oriented K-Means Clustering

## TL;DR

This paper introduces a new visual control system for robotics that improves stability and accuracy by combining advanced estimation and clustering techniques.

## Contribution

A novel Visual Predictive Control framework integrating RBF-EKF estimation and task-oriented K-means clustering for improved IBVS performance.

## Key findings

- The proposed framework achieves uniformly ultimately bounded stability through Lyapunov analysis.
- Simulation results show reduced estimation errors and improved tracking accuracy compared to traditional methods.
- The task-oriented K-means clustering optimizes RBF network efficiency for IBVS paths.

## Abstract

Image-Based Visual Servoing (IBVS) systems often suffer from instability due to measurement noise, modeling errors, and external disturbances. To address these issues, this study proposes a Visual Predictive Control framework integrating Radial Basis Function (RBF) and Extended Kalman Filter (EKF) coupled state-disturbance estimation and task-oriented K-means clustering. First, a feedback linearization Model Predictive Control (MPC) law is designed to handle system nonlinearities and physical constraints. Second, a coupled estimation mechanism is established where the EKF suppresses noise while the RBF network learns lumped disturbances. Crucially, to optimize network efficiency, a task-oriented K-means clustering method is introduced to select RBF centers based on the nominal IBVS path. Lyapunov analysis confirms the Uniformly Ultimately Bounded (UUB) stability. Simulation results demonstrate that the proposed method significantly reduces estimation errors and improves tracking accuracy compared to traditional schemes. Ultimately, this approach enhances the robustness and engineering practicality of robotic visual servoing through the deep coordination of control and estimation.