Abstract We proposed an online identification and adaptive control framework for the nonlinear dynamical systems using a novel hybrid‐feed‐forward recurrent neural network (HFRNN) model. The HFRNN is a combination of a feed‐forward neural network (FFNN) and a local recurrent neural network (LRNN). We aim to leverage the simplicity of FFNN and the effectiveness of RNN to capture changing dynamics accurately and design an indirect adaptive control scheme. To derive the weights update equations, we have applied the gradient‐descent‐based Back‐Propagation (BP) technique, and the stability of the proposed learning strategy is proven using the Lyapunov stability principles. We also compared the proposed method's results with those of the Jordan network‐based controller (JNC) and the local recurrent network‐based controller (LRNC) in the simulation examples. The results demonstrate that our approach performs satisfactorily, even in the presence of disturbance signals.

In this article, an attempt has been made to propose an improved version of the classical Elman neural network (ENN) and its application is presented to identify the unknown dynamics of time-delayed nonlinear plants. The model proposed, known as memory recurrent ENN (MRENN), consists of an additional number of weighted self-feedback loops, an extra output context layer, and weighted connections of input signals (through adjustable weights) to the output-layer neuron. The model is proposed to be given only two signals as its inputs: plant unit-delayed value and the present value of the externally applied signal irrespective of the actual order of the plant (which most of the time may not be known). To guarantee stability, the parameters of the MRENN model are updated using the equations that are obtained by applying Lyapunov-stability criteria. Furthermore, the recursive learning rate scheme is constructed for speeding up the learning process. From the simulation results, MRENN model appears to have produced comparably superior outcomes when the performance of the suggested model is compared with that of other well-known models.