title: Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure.
abstract: This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.
authors.forename: Zhenfeng
authors.initials: Z
authors.lastname: Chen
authors.forename: Shuzhi Sam
authors.initials: SS
authors.lastname: Ge
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhang
authors.forename: Yanan
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors; D035501:Uncertainty