DRNAS: Differentiable RBF neural architecture search method considering computation load in adaptive control

In this work, we investigated an online differential neural network search control algorithm using a backstepping method with a radial basis function (RBF) neural network (NN) framework. In this approach, we mainly focused on searching a neural network architecture with optimal control performance and optimal computation load by learning NN parameters among a finite number of RBF NNs with different architectures. The previous works on RBFNN and backstepping methods mainly considered the control performance of systems, and the computation load limitations of control computers were rarely considered. In this paper, we herein propose a differentiable RBF neural architecture search (DRNAS) method. First, we built a hypernetwork and constructed an appropriate optimization objective function with information of a tracking error and a computation load. This hypernetwork consists of different networks with weight parameters. Then, through backpropagation and based on the gradient descent method, we updated the parameters of the hypernetwork and determined the optimal RBF NN architecture in the search space. Finally, we performed simulations to verify the effectiveness of the proposed method, where we designed an RBF NN adaptive backstepping controller for aircraft pitch rate dynamics and used the DRNAS method to train the hypernetwork based on different mission scenarios. The simulation results verified that the proposed method can effectively balance the controller’s tracking capability with its computation load.