Decentralized intelligent tracking control for uncertain high-order stochastic nonlinear strong interconnected systems in drift and diffusion terms

This paper focuses mainly on decentralized intelligent tracking control for a class of high-order stochastic nonlinear systems with unknown strong interconnected nonlinearity in the drift and diffusion terms. For the control of uncertain high-order nonlinear systems, the approximation capability of RBF neural networks 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 monotonically increasing property of the bounding functions, the variable separation technique is achieved. Lastly, based on the Lyapunov stability theory, a decentralized adaptive neural control method is proposed to reduce the number of online adaptive learning parameters. It is shown that, for bounded initial conditions, 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 simulation examples including a practical example are used to further illustrate the effectiveness of the design method.