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This paper investigates the controller design problem for output-constrained uncertain nonlinear switched systems. By applying the idea of p-times differentiable unbounded functions and the backstepping technique, a novel method is proposed to design state-feedback controllers such that the system output of a class of uncertain switched nonlinear systems in lower triangular form can track a constant...
This paper investigates the backstepping-based distributed tracking control problem for multiple Lagrangian systems with linearly parameterized model uncertainties under the directed communication topology with a directed spanning tree. We consider the dynamic leader case. The leader information is available to only a subset of the followers. By using the position errors of the neighbors as the reference...
The design of a robust adaptive backstepping flight control law for a unmanned aerial vehicle (UAV) is discussed. The lumped uncertainties of the nonlinear UAV model are concerned in this paper. RBF neural networks are used inside the backstepping control law to approximate the lumped uncertainties. Command filters are used to implement the virtual control law and avoid “explosion of complexity”....
In order to realize high-precision position tracking control for (Medium Density Fiberboard, MDF) continuous hot press hydraulic position servo system in the presence of interior parameter perturbations and external load disturbance, an adaptive backstepping sliding mode control strategy with fuzzy disturbance observer (FDO) is presented. Firstly, a modified FDO is designed to estimate and compensate...
In this paper, we present adaptive tracking control designs for a class of constrained nonlinear systems with completely unknown constants. A Barrier Lyapunov Function (BLF) is adopted to protect this limit from being destroyed. By guaranteeing negative semi-definite of the BLF during the design process, we keep the constraint is not transgressed. Adaptive control is used to address the unknown constants...
This paper, based on radial basis function (RBF) neural network, presents an novel adaptive robust controller for a class of strict-feedback uncertainty nonlinear systems to address the tracking problem. The proposed approach, takes advantage of RBF neural network approximation property to approximate system uncertainties, and utilizes adaptive backstep-ping techniques for eliminating the effects...
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