A comparison study between static and dynamic recurrent neural networks based adaptive control of nonlinear multivariable systems

This paper considers the problem of real time adaptive control of nonlinear multivariable systems. Two neural networks techniques are presented to solve the problem mentioned above. The first technique combines the ability of a single-layer feedforward neural network for modeling purposes and a linear control law to design the controller. The second technique combines the ability of dynamic recurrent neural network for modeling purposes and a linear control law to design the controller. In this paper, we consider that the state of the system is accessible. A comparison between the simulation results for the above two techniques are presented to complete the study.