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The recovery of three dimensional structure and motion from time vary images with the aid of CCD camera(s) is usually performed using a nonlinear dynamic system, often referred to as a perspective dynamic system, where the major task is formulated as the problem of state estimation and parameter estimation. A Luenberger-type observer can be used to measure the constant motion parameter system states...
We investigate the learning issue in the robust adaptive neural-network (NN) control process of manipulator with unknown system dynamics and disturbance. Based on recently developed deterministic theory, the regression vector of an appropriately designed robust adaptive NN controller satisfies the partial persistent exciting (PE) condition when tracking a periodic or periodic-like reference orbit,...
In this paper, we investigate identification of a class of distributed parameter systems (DPS) with both spatially invariant and spatially varying parameters via deterministic learning. The plant is a parabolic type partial differential equation (PDE) describing the propagation of heat conduction in a one-dimensional circle. We firstly employ the discrete Fourier transform (DFT) and the Inverse discrete...
In this paper, based on a recent result on deterministic learning theory, we investigate learning from adaptive neural control for a class of discrete-time nonlinear systems. First, we use an adaptive neural control law without any robustification term to ensure the finite time tracking error convergence. With the tracking convergence of the system states to a periodic reference orbit, a partial PE...
A dynamical model of weak coupling periodic structure is established by using Lagrange's equation. The Lyapunov exponents of every disordered situation are computed based on the model, and the influence law and change trend of structure dynamic characters are obtained ultimately. Then, the active vibration control of the structures is studied by applying Neural Networks Predictive (NNP) control and...
In this paper, direct adaptive neural tracking control is proposed for a class of completely non affine pure feedback nonlinear systems with only one mild assumption on affine terms, which are obtained using implicit function theorem and mean value theorem. To effectively remove the restriction of the upper bound on the affine terms, a smooth function is introduced to compensate the interconnected...
The diagnosis of faults is one of the important tasks in engineering systems. In this paper, based on the recent results on deterministic learning (DL) theory and rapid dynamical pattern recognition, a rapid fault diagnosis scheme is proposed for nonlinear oscillation systems. Firstly, a neural network bank for fault detection and isolation (FDI) is established through DL. Secondly, a mechanism for...
In this paper, we investigate deterministic learning from adaptive neural control of general Brunovsky systems, in which the affine terms are unknown functions of system states. We firstly present an extension of a recent result on stability analysis of linear time varying (LTV) systems. We then analyze the difficulties caused by the unknown affine term in deterministic learning for general Brunovsky...
Recently, it was shown that for a class of nonlinear systems with only output measurements, by using a high-gain observer and a dynamical radial basis function network (RBFN), locally-accurate identification of the underlying system dynamics can be achieved along the estimated state trajectory. In this paper, it will be shown that the learned knowledge on system dynamics can be reused in an RBFN-based...
In this paper, the problem of disturbance rejection for a class of non-minimum-phase cascaded nonlinear systems with parameter uncertainty is considered. For the purpose of reducing the reservation from robust control method, we develop an adaptive control design approach based on Lyapunov method and neural network theory. Because the radial-basis function networks (RBF NNs) have the good structure...
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