|Performance Comparison of Neural Network Training Approaches in Indirect Adaptive Control
Ayachi Errachdi* and Mohamed Benrejeb
International Journal of Control, Automation, and Systems, vol. 16, no. 3, pp.1448-1458, 2018
Abstract : "This paper proposes an indirect adaptive control method using neural network (NN) based on a variable
learning rates (VLRs) combined with Taylor development (TD) for nonlinear systems. In the proposed control
architecture, two neural network blocks are used both as an identifier and a controller. The tracking error and the
identification error are used, respectively, to train the neural controller and the neural model. The NN identifier
approximates dynamic systems and provides the NN controller with information about the system sensitivity. The
gradient-descent method using a developed variable learning rate is mixed with the Taylor development and is
applied to train all weights of the NN. The NN TD-VLRs are applied to guarantee the convergence of the proposed
control system. The effectiveness of the proposed algorithm applied to an example of nonlinear dynamic systems
is demonstrated by simulation experiments. The results of simulation show that applying the mixed proposed
method ensures the smallest MSE and the optimal time simulation. Added to that, the neural network controller is
insensitive to variations of the system parameters."
Indirect adaptive control, neural network, Taylor development, variable learning rates.