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

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.