Dynamic Surface Neural Control for a Class of Nonlinear Time-Varying Delay Systems Preceded by Unknown Hysteresis

2013 ◽  
Vol 651 ◽  
pp. 937-942 ◽  
Author(s):  
Xiu Yu Zhang ◽  
Cui Ping Liu ◽  
Yan Sun
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Dynamic Surface Control ◽  
Delay Systems ◽  
Time Varying ◽  
Dynamic Surface ◽  
Time Varying Delay ◽  
Complexity Problem ◽  
Robust Adaptive ◽  
Varying Delay

In this paper, a new robust adaptive neural dynamic surface control is proposed for a class of time-varying delay nonlinear systems preceded by backlash-like hysteresis. Compared with the present schemes of dealing with time-varying delay and hysteresis input, the main advantages of the proposed scheme are that the prespecified transient and steady state performance of tracking error can be guaranteed for the first time when using DSC to deal with the time-varing delays; the computational burden can be greatly reduced and the explosion of complexity problem inherent in backstepping control can be eliminated. It is proved that the new scheme can guarantee all the closed-loop signals semi-globally uniformly ultimate bounded. Simulation results are presented to demonstrate the validity of the proposed scheme.

scholarly journals Robust Adaptive Control for a Class of Uncertain Nonlinear Systems with Time-Varying Delay

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ruliang Wang ◽  
Jie Li ◽  
Shanshan Zhang ◽  
Dongmei Gao ◽  
Huanlong Sun
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Robust Adaptive Control ◽  
Delay Systems ◽  
Time Varying ◽  
Triangular Form ◽  
Time Varying Delay ◽  
Robust Adaptive ◽  
Error Dynamics ◽  
Varying Delay

We present adaptive neural control design for a class of perturbed nonlinear MIMO time-varying delay systems in a block-triangular form. Based on a neural controller, it is obtained by constructing a quadratic-type Lyapunov-Krasovskii functional, which efficiently avoids the controller singularity. The proposed control guarantees that all closed-loop signals remain bounded, while the output tracking error dynamics converge to a neighborhood of the desired trajectories. The simulation results demonstrate the effectiveness of the proposed control scheme.

Robust Adaptive Neural Control for a Class of Time-Varying Delay Systems with Backlash-like Hysteresis Input

2015 ◽  
Vol 18 (3) ◽  
pp. 1087-1101 ◽  
Author(s):  
Xiuyu Zhang ◽  
Zhi Li ◽  
Chun-Yi Su ◽  
Xinkai Chen ◽  
Jianguo Wang ◽  
...  
Keyword(s):  
Neural Control ◽  
Delay Systems ◽  
Time Varying ◽  
Adaptive Neural Control ◽  
Time Varying Delay ◽  
Robust Adaptive ◽  
Varying Delay

scholarly journals Adaptive Backstepping Control for a Class of Uncertain Nonaffine Nonlinear Time-Varying Delay Systems with Unknown Dead-Zone Nonlinearity

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Wei-Dong Zhou ◽  
Cheng-Yi Liao ◽  
Lan Zheng
Keyword(s):  
Controller Design ◽  
Dead Zone ◽  
Tracking Error ◽  
Delay Systems ◽  
Mean Value Theorem ◽  
Time Varying ◽  
Adaptive Backstepping ◽  
Nonlinear Part ◽  
Time Varying Delay ◽  
Varying Delay

An adaptive backstepping controller is constructed for a class of nonaffine nonlinear time-varying delay systems in strict feedback form with unknown dead zone and unknown control directions. To simplify controller design, nonaffine system is first transformed into an affine system by using mean value theorem and the unknown nonsymmetric dead-zone nonlinearity is treated as a combination of a linear term and a bounded disturbance-like term. Owing to the universal approximation property, fuzzy logic systems (FLSs) are employed to approximate the uncertain nonlinear part in controller design process. By introducing Nussbaum-type function, the a priori knowledge of the control gains signs is not required. By constructing appropriate Lyapunov-Krasovskii functionals, the effect of time-varying delay is compensated. Theoretically, it is proved that this scheme can guarantee that all signals in closed-loop system are semiglobally uniformly ultimately bounded (SUUB) and the tracking error converges to a small neighbourhood of the origin. Finally, the simulation results validate the effectiveness of the proposed scheme.

Robust Adaptive NN Design for Course-Tracking Control of Ship with Input Saturation

2013 ◽  
Vol 712-715 ◽  
pp. 2768-2774 ◽  
Author(s):  
Hong Ying Sun ◽  
Feng Wei Yu
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Input Saturation ◽  
Dynamic Surface Control ◽  
Design System ◽  
Dynamic Surface ◽  
Control Scheme ◽  
Robust Adaptive ◽  
The Stability ◽  
Computation Load

In this paper, a novel robust adaptive neural control scheme is proposed for a class of ship course autopilot with input saturation.RBF neural networks (NNs) are used to tackle unknown nonlinear functions,then the robust adaptive NN tracking controller is constructed by combining dynamic surface control (DSC) technique and the minimal-learning-parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constrains is conducted employing an auxiliary design system. With only one learning parameter and reduced computation load, the proposed algorithm can avoid both problem of “explosion of complexity” in the conventional backstepping method and singularity problem. In addition, the boundedness stability of the closed-loop system is guaranteed and tracking error can be made arbitrary small. The effectiveness of the presented autopilot has been demonstrated in the simulation.

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