Adaptive control of partially known nonlinear multivariable systems using neural networks

An adaptive neural control scheme is proposed for a class of partially known interconnected MIMO nonlinear systems in block-triangular form, with both unknown nonlinearities and parametric uncertainties. The MIMO systems is composed of interconnected subsystems. The system state interconnections make it difficult to conclude the stability of the whole system by stability analysis of individual subsystem separately. By exploiting the block-triangular structure properties, we first design for each subsystem a full state feedback controller, and then conclude the stability of all the state variables in a nested iterative manner. Semi-global uniform ultimate boundedness of all the signals in the closed-loop of MIMO nonlinear systems is guaranteed. The outputs of the systems are proven to converge to small neighborhoods of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters