H-infinity control design using dynamic neural networks
In this paper, a new approach is investigated for adaptive dynamic neural network-based H-infinity control, which is designed for a class of non-linear systems with unknown uncertainties. Currently, non-linear systems with unknown uncertainties are commonly used to efficiently and accurately express the real practical control process. Therefore, it is of critical importance but a great challenge and still at its early age to design a stable and robust controller for such a process. In the proposed research, dynamic neural networks were constructed to precisely approximate the non-linear system with unknown uncertainties first, a non-linear state feedback H-infinity control law was designed next, then an adaptive weighting adjustment mechanism for dynamic neural networks was developed to achieve H-infinity regulation performance, and last a recurrent neural network was employed as a neuro-solver to efficiently and numerically solve the standard LMI problem so as to obtain the appropriate control gains. Finally, case studies further verify the feasibility and efficiency of the proposed research.
h-infinity control design dynamic neural networks non-linear system lmi