Neural network based approximate output regulation in discrete-time uncertain nonlinear systems

The existing approaches to the discrete-time nonlinear output regulation problem rely on the off-line solution of a set of mixed nonlinear functional equations known as discrete regulator equations or complex nonlinear systems, it is difficult to solve the discrete regulator equations even approximately. Moreover, for systems with uncertainty, these approaches cannot offer a reliable solution. By combining the approximation capability of the feedforward neural networks with an online parameter optimization mechanism, we develop an approach to solving the discrete-time nonlinear output regulation problem without solving the discrete regulator equations. The advantages of our approach is that it is much more efficient than the existing approaches, and it can handle systems with uncertain parameters.