scholarly journals Robust Adaptive Neural Backstepping Control for a Class of Nonlinear Systems with Dynamic Uncertainties

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hongyan Yang ◽  
Huanqing Wang ◽  
Hamid Reza Karimi
Keyword(s):  
Neural Control ◽  
Unmodeled Dynamics ◽  
Rbf Neural Networks ◽  
Feedback Systems ◽  
Adaptive Neural Control ◽  
Control Approach ◽  
Closed Loop Systems ◽  
Control Scheme ◽  
Nonlinear Uncertainties ◽  
Robust Adaptive

This paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced. Radical basis function (RBF) neural networks are employed to model the packaged unknown nonlinearities, and then an adaptive neural control approach is developed by using backstepping technique. The proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. A simulation example is given to show the effectiveness of the presented control scheme.

scholarly journals Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668270 ◽  
Author(s):  
Zhonghua Wu ◽  
Jingchao Lu ◽  
Jingping Shi ◽  
Qing Zhou ◽  
Xiaobo Qu
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Flight Vehicle ◽  
Model Uncertainties ◽  
Adaptive Neural Control ◽  
Prescribed Performance ◽  
Hypersonic Flight Vehicle ◽  
Hypersonic Flight ◽  
Control Scheme ◽  
Robust Adaptive

A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.

scholarly journals Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ruliang Wang ◽  
Jie Li
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Continuous Functions ◽  
Rbf Neural Networks ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Simulation Results

This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF) neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

scholarly journals Partial-State-Constrained Adaptive Intelligent Tracking Control of Nonlinear Nonstrict-Feedback Systems with Unmodeled Dynamics and Its Application

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yuzhuo Zhao ◽  
Ben Niu ◽  
Xiaoli Jiang ◽  
Ping Zhao ◽  
Huanqing Wang ◽  
...  
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Unmodeled Dynamics ◽  
Feedback Systems ◽  
Feedback Form ◽  
Design Algorithm ◽  
Adaptive Tracking Control ◽  
Control Scheme ◽  
Nonlinear Uncertainties

In this paper, an adaptive intelligent control scheme is presented to investigate the problem of adaptive tracking control for a class of nonstrict-feedback nonlinear systems with constrained states and unmodeled dynamics. By approximating the unknown nonlinear uncertainties, utilizing Barrier Lyapunov functions (BLFs), and designing a dynamic signal to deal with the constrained states and the unmodeled dynamics, respectively, an adaptive neural network (NN) controller is developed in the frame of the backstepping design. In order to simplify the design process, the nonstrict-feedback form is treated by using the special properties of Gaussian functions. The proposed adaptive control scheme ensures that all variables involved in the closed-loop system are bounded, the corresponding state constraints are not violated. Meanwhile, the tracking error converges to a small neighborhood of the origin. In the end, the proposed intelligent design algorithm is applied to one-link manipulator to demonstrate the effectiveness of the obtained method.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

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