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In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how...
This paper presents output tracking control for strict feedback nonlinear systems with time-varying output constraint. A Barrier Lyapunov Function (BLF), which depends explicitly on time, is employed at the outset to prevent the output from violating the time-varying constraint. Specifically, we allow the barrier limit to vary with the desired trajectory in time. Through a change of coordinates for...
In this paper, adaptive control is presented for a class of parametric output feedback nonlinear systems with output constraint. Adaptive observer backstepping is adopted to achieve the output tracking. To prevent the output constraint violation, the barrier Lyapunov function (BLF) is employed in Lyapunov synthesis. By ensuring the boundedness of the BLF, we also guarantee that the output constraint...
In this paper, we investigate the control design for a class of strict-feedback nonlinear systems preceded by unknown backlash-like hysteresis. Using the characteristics of backlash-like hysteresis, adaptive dynamic surface control (DSC) is developed without constructing a hysteresis inverse. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Function uncertainties...
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