Dynamic Neural Networks for Model-Free Control and Identification

Neural networks have been used to solve a broad diversity of problems on different scientific and technological disciplines. Particularly, control and identification of uncertain systems have received attention since many years ago by the natural interest to solve problem such as automatic regulation or tracking of systems having a high degree of vagueness on their formal mathematical description. On the other hand, artificial modeling of uncertain systems (where the pair output-input is the only available information) has been exploited by many years with remarkable results. Within automatic control and identification theory, neural networks must be designed using a dynamic structure. Therefore, the so-called dynamic neural network scheme has emerged as a relevant and interesting field. Dynamic neural networks have used recurrent and differential forms to represent the uncertainties of nonlinear models. This couple of representations has permitted to use the well-developed mathematical machinery of control theory within the neural network framework. The purpose of this special issue is to give an insight on novel results regarding neural networks having either recurrent or differential models. This issue has encouraged application of such type of neural networks on adaptive control designs or/and no parametric modeling of uncertain systems. The contributions of this issue reflect the well-known fact that neural networks traditionally cover a broad variety of the thoroughness of techniques deployed for new analysis and learning methods of neural networks. Based on the recommendation of the guest editors, a number of authors were invited to submit their most recent and unpublished contributions on the aforementioned topics. Finally, five papers were accepted for publication. So, the paper of P. K. Kim and S. Jung titled " Experimental studies of neural network control for one-wheel mobile robot " presents development and control of a disc-typed one-wheel mobile robot, called GYROBO. Several models of the one-wheel mobile robot are designed, developed, and controlled. The current version of GYROBO is successfully balanced and controlled to follow the straight line. GYROBO has three actuators to balance and move. Two actuators are used for balancing control by virtue of gyroeffect and one actuator for driving movements. Since the space is limited and weight balance is an important factor for the successful balancing control, careful mechanical design is considered. To compensate for uncertainties in robot dynamics , a neural network is added to the nonmodel-based PD-controlled system. The reference compensation technique (RCT) is used for the neural network controller …