Adaptive neural tracking control for a class of pure-feedback stochastic nonlinear systems

作者：Wang Huanqing^*; Chen Bing; Zhang Li

来源：ICIC Express Letters, 2012, 6(1): 91-98.

• Adaptive Control
• Adaptive neural control
• Adaptive neural controllers
• Adaptive parameters
• Control schemes
• Neural control
• Pure-feedback system
• Radial basis function neural networks
• Stochastic nonlinear systems
• Tracking controls
• Tracking errors
• Universal approximation

This paper focuses on the problem of the adaptive neural control for pure-feedback stochastic nonlinear systems. Based on the radial basis RBF) neural networks' universal approximation capability, a novel adaptive neural controller is constructed via back stepping technique. The proposed controller guarantees that all the signals in the closed-loop system are bounded in probability while the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. An advantage of the proposed control scheme lies in that only one adaptive parameter needs to be estimated online regardless of the number of neural networks bases used and the order of systems considered. Simulation results further illustrate the effectiveness of the suggested control scheme.

• 出版日期2012