Neuroadaptive Control With Given Performance Specifications for MIMO Strict-Feedback Systems Under Nonsmooth Actuation and Output Constraints

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 9; pp. 4414 - 4425
• Main Authors: Song, Yongduan, Zhou, Shuyan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuation; Adaptive control; Artificial neural networks; Basis functions; Computer simulation; Control design; Control stability; Control systems; Feedback; Given performance specifications; input saturations; Liapunov functions; Mathematical models; matrix factorization technique; MIMO; MIMO (control systems); multi-input multi-output (MIMO) strict-feedback systems; Neural networks; Nonlinear systems; output constraints; Parameter uncertainty; Radial basis function; speed transformation; Stability analysis; Symmetric matrices; Tracking control; Uncertainty; Virtual networks
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2017.2766123

This paper studies the prescribed performance tracking control problem for a class of multi-input multi-output strict-feedback systems with asymmetric nonsmooth actuator characteristics and output constraints as well as unexpected external disturbances. By combining a novel speed transformation with barrier Lyapunov function, a neural adaptive control scheme is developed that is able to achieve given tracking precision within preassigned finite time at prespecified converging mode. At each of the first n - 1 steps of backstepping design, we make use of the radial basis function neural networks to cope with the uncertainties arising from unknown and time-varying virtual control gains, and in the last step, we introduce a matrix factorization technique to remove the restrictive requirement on the unknown control gain matrix and its NN-approximation, simplifying control design. Furthermore, to reduce the number of parameters to be online updated, we introduce a virtual parameter to handle the lumped uncertainties, resulting in a control scheme with low complexity and inexpensive computations. The effectiveness of the proposed control strategy is validated by systematic stability analysis and numerical simulation.