Using neural networks to improve gain scheduling techniques for linear parameter-varying systems

Proposes a new method to improve the gain scheduling technique for linear parameter-varying systems. We have derived the neural networks that can approximate the gain scheduling controller arbitrarily well when the sampling frequency satisfies the sample theorem. That means the gain scheduling controller can be fully reconstructed by neural networks using the discrete-time gain and time input as training pairs. Besides, we also show that the neural networks controller is independent of the sampling time. As a result, when the sampling rate is changed, the controller does not need to redesign. It turns out that the proposed neural networks controller has the following important properties: (i) the same performance as the continuous-time gain scheduling controller which can be generated by Algorithm 1 developed in this paper, (ii) less computing time than continuous-time gain scheduling controller (iii) good robustness against the sampling rates. Computer simulations show that the proposed method has better performance and less computing time than some existing gain scheduling techniques.