Adaptive Asymptotic Neural Network Control of Nonlinear Systems With Unknown Actuator Quantization

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 12; pp. 6303 - 6312
• Main Authors: Xie, Kan, Chen, Ci, Lewis, Frank L., Xie, Shengli
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Adaptive algorithms; Adaptive control; asymptotic control; Asymptotic properties; Control algorithms; Control theory; Measurement; Network control; neural network; Neural networks; Nonlinear control; Nonlinear systems; Nonlinearity; Parameter estimation; Quantization (signal); quantization nonlinearities; Upper bounds
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2018.2828315

In this paper, we propose an adaptive neural-network-based asymptotic control algorithm for a class of nonlinear systems subject to unknown actuator quantization. To this end, we exploit the sector property of the quantization nonlinearity and transform actuator quantization control problem into analyzing its upper bounds, which are then handled by a dynamic loop gain function-based approach. In our adaptive control scheme, there is only one parameter required to be estimated online for updating weights of neural networks. Within the framework of Lyapunov theory, it is shown that the proposed algorithm ensures that all the signals in the closed-loop system are ultimately bounded. Moreover, an asymptotic tracking error is obtained by means of introducing Barbalat's lemma to the proposed adaptive law.