An efficient conjugate gradient based Cholesky CMA-ES estimation algorithm for nonlinear systems

被引：4
作者：
Mao, Yawen [1 ]
Xu, Chen [2 ]
Chen, Jing [1 ]
Pu, Yan [1 ]
机构：
[1] Jiangnan Univ, Sch Sci, Wuxi, Peoples R China
[2] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi, Peoples R China
来源：
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2024年 / 34卷 / 03期
基金：
中国国家自然科学基金;
关键词：
auxiliary model; Cholesky CMA-ES; conjugate gradient; nonlinear system; parameter estimation; PARAMETER-ESTIMATION; EVOLUTION STRATEGY; LMS ALGORITHM; IDENTIFICATION;
D O I：
10.1002/rnc.7047
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article studies the parameter estimation problems of nonlinear systems with colored noise using the covariance matrix adaptation evolution strategy (CMA-ES), which is one of the most competitive evolutionary algorithms available and has been applied in the area of reinforcement learning and process control. However, a major limitation that impedes the application of the CMA-ES is the high computational complexity caused by matrix decomposition. To solve this problem, an efficient Cholesky CMA-ES which uses the Cholesky factor instead of the covariance matrix to reduce the computational complexity, and updates the search direction and distribution mean based on the conjugate gradient method to improve the search accuracy is proposed. By using the auxiliary model identification idea, the Cholesky CMA-ES can be applied to solve the parameter estimation problems of the Hammerstein nonlinear systems with colored noise. Two simulation examples are provided to demonstrate its effectiveness.
引用
收藏
页码：1610 / 1628
页数：19
相关论文
共 78 条
[31]   Maximum likelihood identification of dual-rate Hammerstein output-error moving average system [J].
Li, Junhong ;
Zhang, Jiali .
IET CONTROL THEORY AND APPLICATIONS, 2020, 14 (08) :1078-1090
[32]   Iterative identification methods for a class of bilinear systems by using the particle filtering technique [J].
Li, Meihang ;
Liu, Ximei .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2021, 35 (10) :2056-2074
[33]   Maximum likelihood hierarchical least squares-based iterative identification for dual-rate stochastic systems [J].
Li, Meihang ;
Liu, Ximei .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2021, 35 (02) :240-261
[34]   Fast Covariance Matrix Adaptation for Large-Scale Black-Box Optimization [J].
Li, Zhenhua ;
Zhang, Qingfu ;
Lin, Xi ;
Zhen, Hui-Ling .
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (05) :2073-2083
[35]   A Simple Yet Efficient Evolution Strategy for Large-Scale Black-Box Optimization [J].
Li, Zhenhua ;
Zhang, Qingfu .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2018, 22 (05) :637-646
[36]  
Li ZH, 2017, IEEE C EVOL COMPUTAT, P913, DOI 10.1109/CEC.2017.7969406
[37]   Expectation-maximization algorithm for bilinear systems by using the Rauch-Tung-Striebel smoother [J].
Liu, Siyu ;
Zhang, Xiao ;
Xu, Ling ;
Ding, Feng .
AUTOMATICA, 2022, 142
[38]   Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems [J].
Ma, Hao ;
Pan, Jian ;
Ding, Feng ;
Xu, Ling ;
Ding, Wenfang .
IET CONTROL THEORY AND APPLICATIONS, 2019, 13 (18) :3040-3051
[39]   Filtering-based recursive least squares estimation approaches for multivariate equation-error systems by using the multiinnovation theory [J].
Ma, Ping ;
Wang, Lei .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2021, 35 (09) :1898-1915
[40]   Auxiliary Model-Based Iterative Estimation Algorithms for Nonlinear Systems Using the Covariance Matrix Adaptation Strategy [J].
Mao, Yawen ;
Xu, Chen ;
Chen, Jing ;
Pu, Yan ;
Hu, Qingyuan .
CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2022, 41 (12) :6750-6773
12345678