Neural Networks for Control Signals and Control Systems
Artificial Neural Networks have been widely used in the control area. Studies found that artificial neural networks are good tools for system control and control signal generating. Narendra and Parthasarathy (1990) develop identification and control techniques of dynamical systems using artificial neural networks. Arai, Kohon, and Imai (1991) study an adaptive control of neural network with variable function of a unit and its application. Chen and Khalil (1992) develop an adaptive control of nonlinear systems using neural networks. Hu and Shao (1992) show the neural network adaptive control systems. Yamada and Yabuta (1992) investigate a neural network controller which uses an auto-tuning method for nonlinear functions. Campolucci, Capparelli, Guarnieri, Piazza, & Uncini (1996) learn neural networks with adaptive spline activation function. Lewis, Yesildirek, & Liu, (1996) design Multilayer neural-net robot controller with guaranteed tracking performance. Polycarpou (1996) applies stable adaptive neural control scheme for nonlinear systems. Lewis, Jagannathan, & Yesildirek (1998) build neural network control for robot manipulators and non-linear systems.
Norgaard, Ravn, Poulsen, & Hansen (2000) generate neural networks for modelling and control of dynamic systems. Poznyak, Sanchez, & Yu (2000) investigate differential neural networks for robust nonlinear control. Chen & Narendra (2002) present nonlinear adaptive control using neural networks and multiple models. Diao & Passino (2002) examine adaptive neural/fuzzy control for interpolated nonlinear systems. Holubar, Zani, Hager, Froschl, Radak, Braun (2002) explore advanced controlling of anaerobic digestion by means of hierarchical neural networks. Plett (2003) inspects adaptive inverse control of linear and nonlinear systems using dynamic neural networks. Ge, Zhang, & Lee (2004) probe adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time. Shi & Li (2004) contribute a novel control of a small wind turbine driven generator based on neural networks. Bukovsky, Bila, & Gupta (2005) analyze linear dynamic neural units with time delay for identification and control. Yih, Wei, & Tsu (2005) experiment observer-based direct adaptive fuzzy-neural control for nonffine nonlinear systems. Farrell & Polycarpou (2006) indicate adaptive approximation-based control by unifying neural, fuzzy and traditional adaptive approximation approaches. Boutalis, Theodoridis, & Christodoulou (2009) suppose a new neuro FDS definition for indirect adaptive control of unknown nonlinear systems using a method of parameter hopping. Hou, Cheng, & Tan (2009) supply decentralized robust adaptive control for the multiagent system consensus problem using neural networks. Alanis, Sanchez, Loukianov, & Perez-Cisneros (2010) seek real-time discrete neural block control using sliding modes for electric induction motors. Weidong, Yubing, & Xingpei (2010) offer short-term forecasting of wind turbine power generation based on genetic neural network. Kumar, Panwar, Sukavanam, Sharma, & Borm (2011) run neural network-based nonlinear tracking control of kinematically redundant robot manipulators. Pedro, & Dahunsi (2011) grant neural network-based feedback linearization control of a servo-hydraulic vehicle suspension system. All the studies above suggest that artificial neural networks are powerful tools for control signals and control systems