Neuroadaptive Finite-Time Control for Nonlinear MIMO Systems With Input Constraint

• Published in: IEEE transactions on cybernetics Vol. 52; no. 7; pp. 1 - 8
• Main Authors: Yu, Jinpeng, Shi, Peng, Liu, Jiapeng, Lin, Chong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive neural network (NN) control; Adaptive systems; Backstepping; Convergence; Ear; Error compensation; finite-time (FT) convergence; input backlash; MIMO (control systems); MIMO communication; Neural networks; Nonlinear control; Nonlinear systems; Tracking control
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2020.3032530

This article considers the problem of finite-time (FT) tracking control for a class of uncertain multi-input-multioutput (MIMO) nonlinear systems with input backlash. A modified FT command filter is designed in each step of backstepping, which ensures the output of the filter can faster approximate the derivatives of virtual signals, suppress chattering, and relax the input signal limit of the Levant differentiator. Then, the corresponding improved FT error compensation mechanism is adopted to reduce the negative impact of filtering errors. Furthermore, a neural-network-adaptive technology is proposed for MIMO systems with input backlash via FT convergence. It is shown that desired tracking performance can be implemented in finite time. The simulation example is presented to illustrate the effectiveness and advantages of the new design method.