Abstract
In Chapter 5, neural network (NN) control is redesigned to track both altitude and yaw angle at the same time. We consider a scale model helicopter, mounted on an experimental platform, in the presence of model uncertainties, which may be caused by unmodelled dynamics, or aerodynamical disturbances from the environment. Two different types of NN, namely a multilayer neural network (MNN) and a radial basis function neural network (RBFNN) are adopted in control design and stability analysis. Based on Lyapunov synthesis, the proposed adaptive NN control ensures that both the altitude and the yaw angle track the given bounded reference signals to a small neighborhood of zero, and guarantees the Semi-Globally Uniformly Ultimate Boundedness (SGUUB) of all the closed-loop signals at the same time. The effectiveness of the proposed control is illustrated through extensive simulations. Compared with the model-based control, approximation-based control yields better tracking performance in the presence of model uncertainties.
Published Version
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