Multivariable adaptive control for non-linear systems: Application to the Tennessee Eastman Challenge Process

This paper investigates robust adaptive control for unknown non-linear and multivariable systems with fully connected recurrent neural networks. On-line weights updating law and closed loop performance are derived from the Lyapunov approach. Robust stability under the parametric uncertainties due to disturbances of the overall system is provided. This analysis is concerned by combining Lyapunov approach and linearization around the nominal parameters to establish analytical sufficient conditions for the global robust stability of adaptive neural network controller. The efficiency of the proposed algorithm is illustrated according to a real world simulation benchmark control problem : the Tennessee Eastman Challenge Process.