Constrained Adaptive Control of Nonlinear Systems with Application to Hypersonic Vehicles

Abstract

Constraints in input, output, and states are evident in most of the practical systems. Explicitly incorporating these constraints into the control design process leads to its superior performance in general. Therefore, considering different types of constraints, several robust constrained adaptive nonlinear control designs are proposed in this thesis for different classes of uncertain nonlinear. In the first part of this thesis, a barrier Lyapunov function (BLF) based state constrained adaptive control design is presented for two different classes of uncertain nonlinear systems, known as nonlinear systems with relative degree one and Euler-Lagrange systems. In adaptive control synthesis, a neural network-based approximated system dynamics is constructed to approximate the model uncertainties of the system, and then a tracking controller is designed to achieve the desired tracking response. The weights of the neural network are updated using a Lyapunov stable weight update rule. It is shown that the closed-loop states of the system both remain bounded within the imposed constraints as well as asymptotically converge to a predefined domain. In the second part of this thesis, error transformation based state-constrained adaptive control design is proposed for generic second-order nonlinear systems with state and input constraints, model uncertainties, and external disturbances. A new error transformation is proposed to enforce state constraints; Nussbaum gain is used to impose desired input constraints, and radial basis function neural networks (RBFNNs) are utilized to approximate modeling uncertainties. In this control design philosophy, first, imposed constraints are converted into error constraints and then, using the proposed error transformation, the constrained system is transformed into equivalent unconstrained system. Next, a stable adaptive controller is designed for the unconstrained system, which indirectly establishes the stability of the constrained system without violation of imposed constraints...