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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...
In this paper, we study deterministic learning from adaptive neural control (ANC) of nonlinear strict-feedback systems, with the affine terms as unknown functions of system states. Through system decomposition and state transformation, the problem caused by the strict-feedback structure and affine terms is transformed into the stability analysis of a class of cascade LTV subsystems, for which exponential...
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...
Controlling non-affine nonlinear systems is a challenging problem in the control community. In this paper, an adaptive neural control approach is presented for the completely non-affine pure-feedback system with only one mild assumption. By combining adaptive neural design with input-to-state stability (ISS) analysis and the small-gain theorem, the difficulty in controlling non-affine pure-feedback...
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