Fuzzy-Based Parameter Optimization of Adaptive Unscented Kalman Filter: Methodology and Experimental Validation

Reza Mohammadi Asl,Rainer Palm,Heikki Handroos,Huapeng Wu

doi:10.1109/access.2020.2979987

Abstract

This study introduces a fuzzy based optimal state estimation approach. The new method is based on two principles: Adaptive Unscented Kalman filter, and Fuzzy Adaptive Grasshopper Optimization Algorithm. The approach is designed for the optimization of an adaptive Unscented Kalman Filter. To find the optimal parameters for the filter, a fuzzy based evolutionary algorithm, named Fuzzy Adaptive Grasshopper Optimization Algorithm, is developed where its efficiency is verified by application to different benchmark functions. The proposed optimal adaptive unscented Kalman filter is applied to two nonlinear systems: a robotic manipulator, and a servo-hydraulic system. Different simulation tests are conducted to verify the performance of the filter. The results of simulations are presented and compared with a previous version of the unscented Kalman filter. For a realistic test, the proposed filter is applied on the practical servo-hydraulic system. Practical results are discussed, and presented results approve the capability of the presented method for practical applications.

Highlights

State estimation is one of the most important parts of industrial system control
SIMULATION AND RESULTS the optimized intelligent filter, which is a combination of previously introduced methods, Adaptive Unscented Kalman Filter (AUKF) and Fuzzy Adaptive Grasshopper Optimization Algorithm (FAGOA), will applied on two different systems: a robotic manipulator and a servo-hydraulic system
This study presents a new and intelligent adaptive unscented Kalman filter

Summary

Introduction

State estimation is one of the most important parts of industrial system control. In practical systems, it is difficult to measure some of their states. To design a proper controller for these systems, it is necessary to develop methods to identify the states. The first method is using sensors to estimate/detect different states. This method has some disadvantages: high cost, difficulty of implementation, storage requirement, and some equipment for transferring data. These problems require developing new methods which can compensate for the disadvantages of the first method. The other method is the usage of state estimators which have been investigated during recent years. Many studies have focused on this topic, and different methods have been investigated for proposing a new state estimator.

Methods

Results

Conclusion