Adaptive Neural Dynamic Surface Control for Nonstrict-Feedback Systems With Output Dead Zone

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 11; pp. 5200 - 5213
• Main Authors: Shi, Xiaocheng, Lim, Cheng-Chew, Shi, Peng, Xu, Shengyuan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Adaptive output-constrained control; Adaptive systems; Artificial neural networks; Backstepping; Basis functions; Control systems; Control systems design; dynamic surface control (DSC); Feedback; Neural networks; neural networks (NNs); Nonlinear systems; nonstrict-feedback nonlinear systems; output dead zone; Radial basis function; Recursive methods; Tracking control
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2018.2793968

This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young's inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint.