title: Universal neural network control of MIMO uncertain nonlinear systems.
abstract: In this brief, a continuous tracking control law is proposed for a class of high-order multi-input-multi-output uncertain nonlinear dynamic systems with external disturbance and unknown varying control direction matrix. The proposed controller consists of high-gain feedback, Nussbaum gain matrix selector, online approximator (OLA) model and a robust term. The OLA model is represented by a two-layer neural network. The continuousness of the control signal is guaranteed to relax the requirement for the actuator bandwidth and avoid the incurred chattering effect. Asymptotic tracking performance is achieved theoretically by standard Lyapunov analysis. The control feasibility is also verified in simulation environment.
authors.forename: Qinmin
authors.initials: Q
authors.lastname: Yang
authors.forename: Zaiyue
authors.initials: Z
authors.lastname: Yang
authors.forename: Youxian
authors.initials: Y
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence / Q000592:standards*; D003198:Computer Simulation; D004777:Environment; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D035501:Uncertainty