Adaptive Observer-Based Control for a Class of Nonlinear Stochastic Systems with Parameter Uncertainty

Abstract

The adaptive control problem was discussed for stochastic nonlinear systems with parameter uncertainty in this paper. The actual systems such as distributed network systems, weather systems or industrial control systems are constantly changing. There is a correlation between various factors and information exchange, material exchange or energy exchange between the system and the outside world in the system. These exchanges have randomness, more or less. For these kinds of systems, the model descriptions need the help of stochastic differential equations. Considering stochastic nonlinear systems with unknown constant parameters, we introduced the separation theory into the adaptive parameter estimators design in the design process. Using Lyapunov functional and stochastic analysis technology, we studied the design method of robust adaptive observers for stochastic systems. So that the gain matrices can be easily obtained in sequence in the same algorithm. A numerical test was presented to verify the feasibility of the conclusions obtained.