Designing adaptive robust extended Kalman filter based on Lyapunov-based controller for robotics manipulators

TL;DR

This paper introduces an adaptive robust extended Kalman filter integrated with a Lyapunov-based controller to improve position and velocity estimation in robotic manipulators affected by disturbances, enhancing trajectory tracking accuracy.

Contribution

It proposes a novel combination of AREKF with Lyapunov-based control for disturbance rejection in robotic manipulators, validated through simulation.

Findings

AREKF outperforms standard EKF in estimation accuracy

The combined control approach achieves precise trajectory tracking

Simulation confirms robustness against disturbances

Abstract

In this paper, a position and velocity estimation method for robotic manipulators which are affected by constant bounded disturbances is considered. The tracking control problem is formulated as a disturbance rejection problem, with all the unknown parameters and dynamic uncertainties lumped into disturbance. Using adaptive robust extended Kalman filter(AREKF) the movement and velocity of each joint is predicted to use in discontinuous Lyapunov-based controller structure. The parameters of the error dynamics have been validated off-line by real data. Computer simulation results given for a two degree of freedom manipulator demonstrate the efficacy of the improved Kalman Filter by comparing the performance of EKF and improved AREKF. Although it is shown that accurate trajectory tracking can be achieved by using the proposed controller.