| In this thesis we present neural network based adaptive algorithms for full state feedback, output feedback, and decentralized control of feedback linearizable nonlinear systems.; We first introduce a novel approach to dynamic model inversion of a class of nonlinear non-affine dynamic systems that leads to a controller architecture that can host both approximate feedback linearizing controllers. We specifically construct adaptive update laws for the single hidden layer and the full adaptive (adapting gain, centers, and widths) radial basis function neural networks. We strengthened the stability result with respect to previously developed proofs by introducing a robust adaptive gain to estimate on-line the bound on the higher order terms of the neural network output.; Next, we present a state observer based adaptive output feedback control architecture limited to relative degree 2 systems, but applicable to multi-input-multi-output systems. This architecture employs functional link perceptron neural. We also consider an adaptive output feedback that overcomes the relative degree restriction of the adaptive observer approach. We argue that it is considerably more convenient to design an observer for the output tracking error dynamics, since it appears linear as a result of feedback linearization. We prove ultimate boundedness of both the observer and controller tracking errors. The proof of stability is limited to linearly parameterized neural networks.; Finally, we develop a decentralized adaptive control design procedure for large-scale uncertain systems using single hidden layer neural networks. The subsystems are assumed to be feedback linearizable and non-affine in the control, and their interconnections bounded linearly by the tracking error norms. Single hidden layer neural networks are introduced to approximate the feedback linearization error signal on-line from available measurements. A robust adaptive signal is required in the analysis to shield the feedback linearizing control law from the interconnection effects.; As an Appendix we also report experimental results obtained by implementing a neural network based adaptive controller to the CalTech ducted fan. |
