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In this paper, an adaptive output-feedback control scheme is presented for a class of stochastic nonlinear systems with dynamic uncertainties and unmeasured states. Radial basis function neural networks are used to approximate the unknown nonlinear functions. K-filters are designed to estimate the unmeasured states. The changing supply function is employed to solve the problem of dynamical uncertainties...
This paper considers the path following problem of underactuated marine surface vessel in the presence of model uncertainty and external disturbances. A robust control strategy is proposed by employing the dynamic surface control and extended state observer. The key features of the presented control strategy include that, first, the extended state observer is proposed to estimate the model uncertainty...
This paper presents a robust adaptive output-feedback dynamic surface control for a class of nonlinear systems with output unmodeled dynamics. A new relation ship between the output and the state of the output unmodeled dynamics is established while the unmodeled dynamics is input-to-state practically stable. With such relationship, it is shown that all the signals of the closed-loop system are bounded...
A Barrier Lyapunov Function (BLF)-based robust adaptive dynamic surface control (DSC) algorithm is proposed for tracking control of a class of uncertain nonlinear systems with symmetric tracking error constraints. The uncertainties include both of the linearly parameterized terms and the nonlinear function terms. The nonlinear function terms are assumed to satisfy a ‘triangularity condition’ and handled...
This paper investigates the output-feedback control problem for a class of robot system with stochastic disturbances and a single-link manipulator. By utilizing a novel neural network (NN) approximation approach, the adaptive parameter is only one and the nonlinear terms are successfully handled without growth conditions. The constructed adaptive output-feedback controller guarantees the closed-loop...
In this paper, adaptive output feedback dynamic surface control is developed for a class of nonlinear systems with unmodeled dynamics and prescribed tracking performance. Unmodeled dynamics is handled by introducing a novel description on Lyapunov function. Prescribed performance is satisfied through a transformation with respect to tracking error. With the help of Nussbaum function, the problem of...
A novel adaptive neural network dynamic surface control is proposed for hypersonic vehicle. Based on the hypersonic vehicle model characteristics, the adaptive neural network dynamic surface control attitude controller and the neural networks velocity controller are designed, respectively. By combining Nussbaum gain function with decoupled backstepping, the problems of unknown control directions and...
The spacecraft attitude motion equations described by the Euler angles are expressed in vector form and viewed as two blocks, a kinematics block and a dynamics block. Based on Lyapunov stability theory, a robust dynamic surface control algorithm is designed for the nonlinear control system of attitude tracking of spacecraft in the presence of parameter uncertainty and external disturbances. Then,...
This paper reports an autopilot design for a robotic unmanned surface vehicle in the control laboratory at DMU. A robust adaptive steering law is developed with the aid of a predictor, a tracking differentiator, neural networks, and a dynamic surface control technique. The developed controller is able to achieve satisfactory performance in the presence of model uncertainties, time-varying ocean disturbances,...
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