Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm

Fengjun Hu,Qian Zhang,Gang Wu

doi:10.1155/2020/2096302

Abstract

Standard cubature Kalman filter (CKF) algorithm has some disadvantages in stochastic system control, such as low control accuracy and poor robustness. This paper proposes a stochastic system control method based on adaptive correction CKF algorithm. Firstly, a nonlinear time-varying discrete stochastic system model with stochastic disturbances is constructed. The control model is established by using the CKF algorithm, the covariance matrix of standard CKF is optimized by square root filter, the adaptive correction of error covariance matrix is realized by adding memory factor to the filter, and the disturbance factors in nonlinear time-varying discrete stochastic systems are eliminated by multistep feedback predictive control strategy, so as to improve the robustness of the algorithm. Simulation results show that the state estimation accuracy of the proposed adaptive cubature Kalman filter algorithm is better than that of the standard cubature Kalman filter algorithm, and the proposed adaptive correction CKF algorithm has good control accuracy and robustness in the UAV control test.

Highlights

Due to the influence of the external environment or uncertain factors, there is some stochastic noise in the control system [1, 2]
A linear matrix inequality (LMI) control method is proposed by combining the stochastic two-dimensional delay control model with the Lyapunov function [14]
The nonlinear control method transforms the system linearly through the principle of calculus geometry or feedback linearization, it still has the problem of low control accuracy for large-scale complex stochastic nonlinear discrete systems

Summary

Introduction

Due to the influence of the external environment or uncertain factors, there is some stochastic noise in the control system [1, 2]. The nonlinear control method transforms the system linearly through the principle of calculus geometry or feedback linearization, it still has the problem of low control accuracy for large-scale complex stochastic nonlinear discrete systems Aiming at this problem, this paper proposes a stochastic system control method based on adaptive correction CKF algorithm. For the nonlinear time-varying discrete stochastic system constructed above, a cubature Kalman filter (CKF) [37,38,39] algorithm is used to estimate the state of the system. The standard error of the cubature Kalman Filter algorithm for nonlinear time-varying discrete stochastic systems is about 0.2, and the influence of external disturbances on it needs to be improved

Cubature Kalman Filter Algorithm with Adaptive Correction

Performance Simulation of Improved CKF Algorithm

Simulation Test of Unmanned Aerial Vehicle Control System

Summary