Neural network based adaptive algorithms for nonlinear control
Flavio Nardi · 2000
this paper, back-stepping control, has become a very popular and powerful tool in nonlinear adaptive control. A complete account for such methods can be found in [59, 73, 121]. An extension to non linearizable systems was proposed in [107]. The combination of adaptive control and feedback linearization applied to flight control can be found in [126]. In most of the classical adaptive control literature it is common to assume the unknown dynamics to have a known structure with unknown parameters entering linearly in the dynamics. The linear parameterization of unknown dynamics poses serious obstacles in adopting adaptive control algorithms in practical applications, because it is di#cult to fix the structure of the unknown nonlinearities. This fact has been the motivating factor behind the interest in on-line function approximators to estimate and learn the unknown function. The most common function approximators used in adaptive control are artificial neural network and fuzzy logic structures. On line control algorithms that do not require knowledge of the system dynamics (except its dimension and relative degree) have been made possible by employing artificial neural networks in the feedback loop [34]. The ability of neural networks to approximate uniformly continuous functions has been proven in several articles [21, 27, 38, 28, 40]. An important aspect of neural network control applications is the di#erence between approximation theory results and what is achievable in on-line adaptive schemes using such approximators. First and most importantly, in o#-line applications the neural network weights are updated based on input-output matching, 5 whereas in direct adaptive control situations the update of the network parameters is driven by a tracking error, which by it...