Nonlinear Adaptive Neural Control

Abstract

Neural networks are capable of learning and reconstructing complex nonlinear mappings and have been widely studied by control researchers in the design of control systems. A large number of control structures have been proposed, including supervised control (Werbos, 1990), direct inverse control (Miller et al., 1990), model reference control (Narendra and Parthasarathy, 1990), internal model control (Hunt and Sbararo, 1991), predictive control (Hunt et al., 1992; Willis et al., 1992), gain scheduling (Guez et al., 1988), optimal decision control (Fu, 1970), adaptive linear control (Chi et al., 1990), reinforcement learning control (Anderson, 1989; Barto, 1990), indirect adaptive control (Narendra and Parthasarathy, 1990; Liu et al., 1999a) and direct adaptive control (Polycarpou and Ioannou, 1991; Sanner and Slotine, 1992; Karakasoglu et al., 1993; Sadegh, 1993; Lee and Tan, 1993). The principal types of neural networks used for control problems are the multilayer perceptron neural networks with sigmoidal units (Psaltis et al., 1988; Miller et al., 1990; Narendra and Parthasarathy, 1990) and the radial basis function neural networks (Powell, 1987; Niranjan and Fallside, 1990; Poggio and Girosi, 1990a).