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
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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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Journal ArticleDOI
Approximation-based disturbance observer approach for adaptive tracking of uncertain pure-feedback nonlinear systems with unmatched disturbances
Hyoung Oh Kim,Sung Jin Yoo +1 more
TL;DR: An approximation-based nonlinear disturbance observer (NDO) methodology for adaptive tracking of uncertain pure-feedback nonlinear systems with unmatched external disturbances to develop an NDO-based control framework in the presence of non-affine nonlinearities and disturbances unmatched in the control input.
Journal ArticleDOI
MLP technique based reinforcement learning control of discrete pure-feedback systems
Yu Zhang,Shixing Wang +1 more
TL;DR: The reinforcement learning control with neural networks (NNs) is investigated for a class of pure-feedback systems in discrete time using minimal-learning-parameter (MLP) technique and the feasibility of the proposed controller is verified by a simulation example.
Journal ArticleDOI
Optimal Nonlinear Controller Design for Different Classes of Nonlinear Systems Using Black Hole Optimization Method
Hazem I. Ali,Musadaq A. Hadi +1 more
TL;DR: In this paper, a new optimal nonlinear controller is proposed for different classes of nonlinear systems using black hole (BH) optimization method, and the simulation results show that the asymptotic stability and optimal performance are achieved with the lowest possible control action.
Proceedings ArticleDOI
Adaptive model-free consensus control for a network of nonlinear agents under the presence of measurement noise
TL;DR: A decentralized model-free consensus control is proposed for a network of nonlinear agents with unknown nonlinear dynamics, unknown process disturbances and white noise measurement disturbances to first synchronize the states of all follower agents in the network to a leader and then track a reference trajectory in the systems state-space.
References
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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.