Adaptive NN prescribed performance control for nonlinear systems with output dead zone. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Yan, Hailong; Li, Yansong
• Is Part Of: Neural computing & applications. Volume 28:Issue 1(2017)
• Page Start: 145
• Page End: 153
• Publication Date: 2017-01
• Subjects: Adaptive -- Neural network -- Backstepping -- Dead zone -- Prescribed performance; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2043-4
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract This paper investigates adaptive neural network (NN) prescribed performance output tracking control problem for a class of strict-feedback nonlinear systems with output dead zone. By introducing a Nussbaum function, the problem of unknown virtual control coefficient is resolved, which is caused by the nonlinearity in the output dead zone. By designing the state observer and utilizing backstepping recursive design technique, a new adaptive NN control method is proposed. It is shown that all the signals of the resulting closed-loop system are bounded and the tracking error remains an adjustable neighborhood of the origin with the predefined performance under the effect of output dead zone. Finally, a simulation example is given at the simulation part, which further demonstrate the effectiveness of proposed control method.