Dynamic performance enhancement for nonlinear stochastic systems using RBF driven nonlinear compensation with extended Kalman filter

Journal Article · Tue Dec 03 00:00:00 EST 2019 · Automatica

DOI:https://doi.org/10.1016/j.automatica.2019.108693· OSTI ID:1607182

Zhou, Yuyang ^[1]; Wang, Aiping ^[2]; Zhou, Ping ^[3]; Wang, Hong ^[4]; Chai, Tianyou ^[3]

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1. Univ. of Manchester (United Kingdom)
2. Bozhou Univ. (People's Republic of China); Anhui Univ. (PR China)
3. Northeastern Univ., Shenyang (People's Republic of China)
4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Herein, a novel hybrid control method is proposed to enhance the tracking performance of the Proportional–Integral (PI) based control system for a class of nonlinear and non-Gaussian stochastic dynamic processes with unmeasurable states. The system performance is presented by tracking error entropy as the system is nonlinear and subjected to non-Gaussian noises. The well-known kernel density estimation (KDE) technique is employed to estimate the entropy because the precise statistical property of noises is not available for many industrial processes. Since in many industrial cases gains of PI controllers are fixed, a compensative controller is designed without changing the existing closed loop PI controller. Moreover, the compensative signal is formed using the estimated states from the extended Kalman filter (EKF) and a nonlinear compensation realized by the radial basis function (RBF) neural network. The weights of RBF are trained to minimize the entropy of the closed loop tracking error. The convergence of RBF network is discussed and the stability of the resulting closed-loop control system is analysed in mean square sense. Finally, two numerical examples and a practical system simulation are given to illustrate the effectiveness of the proposed control method.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: National Natural Science Foundation of China (NSFC); USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1607182
• Alternate ID(s): OSTI ID: 1765118
• Journal Information: Automatica, Journal Name: Automatica Journal Issue: C Vol. 112; ISSN 0005-1098
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
Extended Kalman filter
Kernel density estimation
Minimum entropy criterion
Non-Gaussian stochastic nonlinear systems
RBF neural network
Tracking performance enhancement