Journal ArticleDOI
Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer
Mou Chen,Shuzhi Sam Ge +1 more
TLDR
Both state feedback and output feedback direct adaptive controls can guarantee semiglobal uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis.Abstract:
In this paper, the direct adaptive neural control is proposed for a class of uncertain nonaffine nonlinear systems with unknown nonsymmetric input saturation. Based on the implicit function theorem and mean value theorem, both state feedback and output feedback direct adaptive controls are developed using neural networks (NNs) and a disturbance observer. A compounded disturbance is defined to take into account of the effect of the unknown external disturbance, the unknown nonsymmetric input saturation, and the approximation error of NN. Then, a disturbance observer is developed to estimate the unknown compounded disturbance, and it is established that the estimate error converges to a compact set if appropriate observer design parameters are chosen. Both state feedback and output feedback direct adaptive controls can guarantee semiglobal uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis. Numerical simulation results are presented to illustrate the effectiveness of the proposed direct adaptive neural control techniques.read more
Citations
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Performance guaranteed fault control of uncertain constrained nonaffine systems under input quantization and actuation failures
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Disturbance observer‐based robust motor control enhanced by adaptive neural network in the absence of velocity measurement
TL;DR: In this paper , an adaptive hybrid model, which includes an adaptive neural network and a sign function, is employed to compensate the discontinuous and fast time-varying friction dynamics.
Proceedings ArticleDOI
Neural Learning-Based Integrated Guidance and Control Algorithm of Multiple Underactuated AUVs
TL;DR: Effective tunning laws are proposed for each NN approximator which can guarantee a uniformly ultimately bounded (UUB) stability of the entired system with the use of rigorous Lyapunov-based stability proofs.
Journal ArticleDOI
Adaptive tracking control for an unmanned autonomous helicopter using neural network and disturbance observer
TL;DR: In this article, an adaptive tracking control scheme is investigated for a medium scale unmanned autonomous helicopter (UAH) with unknown external disturbances and system uncertainties to achieve improvement on the flight performance, where the neural networks (NNs) are employed to compensate the system uncertainties.
Proceedings ArticleDOI
Similarity analysis of disturbance observer and active disturbance rejection control for typical motor-driven system
TL;DR: A similarity analysis of Disturbance Observer and ADRC for a typical motor-driven system in frequency domain is presented, similarities and differences between them are analyzed and could propose new design ideas based on DOB andADRC with better adaptablility for kinds of complex systems.
References
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Journal ArticleDOI
A nonlinear disturbance observer for robotic manipulators
TL;DR: The global exponential stability of the proposed disturbance observer (DO) is guaranteed by selecting design parameters, which depend on the maximum velocity and physical parameters of robotic manipulators.
Journal ArticleDOI
Disturbance observer based control for nonlinear systems
TL;DR: This work presents a general framework for nonlinear systems subject to disturbances using disturbance observer based control (DOBC) techniques and develops a nonlinear disturbance observer for disturbances generated by an exogenous system.
Journal ArticleDOI
Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints
TL;DR: The auxiliary design system is introduced to analyze the effect of input constraints, and its states are used to adaptive tracking control design, and the closed-loop semi-global uniformly ultimate bounded stability is achieved via Lyapunov synthesis.
Journal ArticleDOI
Adaptive neural control of uncertain MIMO nonlinear systems
Shuzhi Sam Ge,Cong Wang +1 more
TL;DR: Adapt neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms that avoid the controller singularity problem completely without using projection algorithms.
Journal ArticleDOI
Brief paper: Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form
Tao Zhang,Shuzhi Sam Ge +1 more
TL;DR: It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants.