The based on optimization LM learning techniques is used for nonlinear oscillatory plant identification and oscillation suppression by means of a direct integral term (I-term) adaptive neural control using RCVNN. Lastly,

In this work, a recursive Levenberg-Marquardt (LM) learning algorithm in the complex domain is developed and applied to the learning of an adaptive control scheme composed by ComplexValued Recurrent Neural Networks (CVRNN). We simplified the derivation of the LM learning algorithm using a diagrammatic method to derive the adjoint CVRNN used to obtain the gradient terms. Furthermore, we apply the CVRNN control scheme for a particular case of a nonlinear, oscillatory mechanical plant to validate the performance of the adaptive neural controller and the learning algorithm. The obtained simulation results using a flexible robot arm confirm a good performance of the derived control schemes and learning algorithms to suppress the occurred robot oscillations and tracking error. Keywords—Complex-valued Levenberg-Marquardt learning, direct adaptive neural control, diagrammatic rules, recurrent complex-valued neural network topology, system identification of nonlinear oscillatory plants.