Gaussian Process adaptive control of nonlinear system base on online algorithm

It takes massive computation time to find the optimal hyper parameters of Gaussian Process. That can not be applied to the online training in real time applications or time-variant data source. The online algorithms proposed by other researchers are high computationally intensive. This manuscript presents natural gradient online algorithm for GP regression. GP may be used as a universal function approximator. Then an adaptive GP controller is designed in the state feedback control for a class nonlinear system. In order to demonstrate the availability of this adaptive GP controller, a simulation of the inverted pendulum system is given. The results of simulation demonstrate this GP online algorithm is very effective and the GP controller can achieve a satisfactory performance.