Received: 27 June 2017 Revised: 11 December 2017 Accepted: 22 December 2017
Decentralized intelligent tracking control for uncertain
high-order stochastic nonlinear strong interconnected
systems in drift and diffusion terms
Center for Control and Optimization,
School of Automation Science and
Engineering, South China University of
Technology, Guangzhou 510641, China
Wen-Jie Si, Center for Control and
Optimization, School of Automation
Science and Engineering, South China
University of Technology, Guangzhou
This paper focuses mainly on decentralized intelligent tracking control for a
class of high-order stochastic nonlinear systems with unknown strong intercon-
nected nonlinearity in the drift and diffusion terms. For the control of uncertain
high-order nonlinear systems, the approximation capability of RBF neural net-
works is utilized to deal with the difficulties caused by completely unknown
system dynamics and stochastic disturbances, and only one adaptive parameter
is constructed to overcome the overparameterization problem. Then, to address
the problem from high-order strong interconnected nonlinearities in the drift
and diffusion terms with full states of the overall system, by using the mono-
tonically increasing property of the bounding functions, the variable separation
technique is achieved. Lastly, based on the Lyapunov stability theory, a decen-
tralized adaptive neural control method is proposed to reduce the number of
online adaptive learning parameters. It is shown that, for bounded initial con-
ditions, the designed controller can ensure the semiglobally uniformly ultimate
boundedness of the solution of the closed-loop system and make the tracking
errors eventually converge to a small neighborhood around the origin. Two sim-
ulation examples including a practical example are used to further illustrate the
effectiveness of the design method.
decentralized intelligent control, neural network approximation, stochastic nonlinear systems,
uncertain high-order nonlinear systems
In the past decades, high-order nonlinear systems have attracted increasing attention, and large-scale systems have
many important applications, such as traffic systems, transportation networks, power systems, and multiagent systems.
Stochastic disturbances may cause performance degradation and even system instability.
Besides, high-order large-scale
stochastic nonlinear systems with completely unknown nonlinearities can represent a wide range of practical systems.
Therefore, this paper will investigate the problem of decentralized adaptive control for uncertain high-order large-scale
stochastic nonlinear systems subject to unknown unmatched strong interconnections in both drift and diffusion terms.
2780 Copyright © 2018 John Wiley & Sons, Ltd. wileyonlinelibrary.com/journal/rnc Int J Robust Nonlinear Control. 2018;28:2780–2805.