Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 6; pp. 2638 - 2644
• Main Authors: Niu, Ben, Li, Lu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive systems; Artificial neural networks; Basis functions; Control systems design; Coordinate transformations; Design; Feedback control; Genetic transformation; input delay; Liapunov functions; Mass-spring-damper systems; MIMO; MIMO (control systems); neural network (NN); Neural networks; Nonlinear control; Nonlinear systems; Radial basis function; Subsystems; switched multiple-input multiple-output (MIMO) nonlinear systems; Switched systems; Switches; Tracking control
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2017.2690465

This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.