Brief paper: Adaptive tracking of nonlinear systems with non-symmetric dead-zone input
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics
Mathematics and Computers in Simulation
Fuzzy adaptive backstepping robust control for SISO nonlinear system with dynamic uncertainties
Information Sciences: an International Journal
Observer-based adaptive control for uncertain time-delay systems
Information Sciences: an International Journal
Information Sciences: an International Journal
International Journal of Automation and Computing
Design of Robust Adaptive Controllers for Nonlinear Systems with Dynamic Uncertainties
Automatica (Journal of IFAC)
Brief A combined backstepping and small-gain approach to adaptive output feedback control
Automatica (Journal of IFAC)
Robust adaptive control of a class of nonlinear systems with unknown dead-zone
Automatica (Journal of IFAC)
Robust adaptive control of nonlinear systems with unknown time delays
Automatica (Journal of IFAC)
Unknown inputs' adaptive observer for a class of chaotic systems with uncertainties
Mathematical and Computer Modelling: An International Journal
Robust Output Feedback Tracking Control for Time-Delay Nonlinear Systems Using Neural Network
IEEE Transactions on Neural Networks
Output Feedback Stabilization for Time-Delay Nonlinear Interconnected Systems Using Neural Networks
IEEE Transactions on Neural Networks
International Journal of Automation and Computing
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This paper presents an up-to-date study on the observer-based control problem for nonlinear systems in the presence of unmodeled dynamics and actuator dead-zone. By introducing a dynamic signal to dominate the unmodeled dynamics and using an adaptive nonlinear damping to counter the effects of the nonlinearities and dead-zone input, the proposed observer and controller can ensure that the closed-loop system is asymptotically stable in the sense of uniform ultimate boundedness. Only one adaptive parameter is needed no matter how many unknown parameters there are. The system investigated is more general and there is no need to solve Linear matrix inequality (LMI). Moreover, with our method, some assumptions imposed on nonlinear terms and dead-zone input are relaxed. Finally, simulations illustrate the effectiveness of the proposed adaptive control scheme.