scholarly journals Prescribed Performance Synchronization Control of Chaotic Systems with Unknown Control Gain Signs

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xin Ma ◽  
Fang Zhu
Keyword(s):  
Chaotic Systems ◽  
Control Method ◽  
Lyapunov Theory ◽  
Synchronization Control ◽  
Fuzzy Logic Systems ◽  
Prescribed Performance ◽  
Synchronization Errors ◽  
Nussbaum Function ◽  
Control Gain ◽  
Unknown Control

For a class of uncertain nonlinear chaotic systems with unknown control gain signs and saturated input, by means of Nussbaum function, a scheme of finite-time prescribed performance synchronization control is proposed. Here, Nussbaum function is used to eliminate the influence of unknown control gain signs, and fuzzy logic systems are used to estimate unknown functions. Lyapunov theory is used to prove that all synchronization errors converge to a predefined small performance range under the designed control method. Finally, simulation results are provided to illustrate the feasibility of the proposed method.

scholarly journals Fuzzy Adaptive Prescribed Performance Control for Uncertain Horizontal Platform System with Unknown Control Gain

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Wei Xiang ◽  
Yeguo Sun ◽  
Chunzhi Yang
Keyword(s):  
Control Method ◽  
Adaptive Controller ◽  
Performance Bounds ◽  
Constrained System ◽  
Prescribed Performance ◽  
Performance Technique ◽  
Control Gain ◽  
Unknown Control ◽  
Platform System ◽  
Fuzzy Adaptive

This paper proposes a fuzzy adaptive control method for uncertain horizontal platform system with unknown control gain, which is capable of guaranteeing the prescribed performance bounds. An error transformation is introduced to transform the original constrained system into an equivalent unconstrained one. Then, based on the error transformation technique and the predefined performance technique, a fuzzy adaptive controller is designed for the unconstrained system. It is shown that all the variables of the resulting closed-loop system are bounded. Finally, an illustrative example is given to demonstrate the effectiveness and usefulness of the proposed method.

scholarly journals Fuzzy Adaptive Prescribed Performance Control for MIMO Uncertain Chaotic Systems in Nonstrict Feedback Form

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yeguo Sun ◽  
Heng Liu
Keyword(s):  
Chaotic Systems ◽  
Control Method ◽  
Tracking Error ◽  
Performance Bounds ◽  
Fuzzy Logic Systems ◽  
Feedback Form ◽  
Constrained System ◽  
Prescribed Performance ◽  
Uncertain Chaotic Systems ◽  
Fuzzy Adaptive

This paper presents a fuzzy adaptive control method for MIMO uncertain chaotic systems in nonstrict feedback form, which is capable of guaranteeing the prescribed performance bounds. For the prescribed performance bounds, we mean that the tracking error should converge to a predefined arbitrarily small set, with convergence rate no more than a prescribed value. A novel output error transformation is introduced to transform the original constrained system into an equivalent unconstrained one, and it is proved that the stabilization of the unconstrained system is sufficient to solve the problem. Based on the error transformation technique, a fuzzy adaptive controller is designed for the unconstrained system. For updating the parameters of the fuzzy logic systems, a proportional-integral adaptation law is proposed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed results.

Adaptive Iterative Learning Control for Nonlinear Systems With Unknown Control Gain1

2004 ◽  
Vol 126 (4) ◽  
pp. 916-920 ◽  
Author(s):  
Huadong Chen ◽  
Ping Jiang
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Learning Gain ◽  
Adaptive Iterative Learning Control ◽  
Control Gain ◽  
Unknown Control

An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.

scholarly journals Robust Synchronization Control of Uncertain Fractional-Order Chaotic Systems via Disturbance Observer

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kaijuan Xue ◽  
Yongbing Huangfu
Keyword(s):  
Fractional Order ◽  
Disturbance Observer ◽  
Chaotic Systems ◽  
Control Method ◽  
Observer Design ◽  
Synchronization Error ◽  
Synchronization Control ◽  
Compact Set ◽  
Control Scheme ◽  
Unknown Disturbances

This paper studies the synchronization of two different fractional-order chaotic systems through the fractional-order control method, which can ensure that the synchronization error converges to a sufficiently small compact set. Afterwards, the disturbance observer of the synchronization control scheme based on adaptive parameters is designed to predict unknown disturbances. The Lyapunov function method is used to verify the appropriateness of the disturbance observer design and the convergence of the synchronization error, and then the feasibility of the control scheme is obtained. Finally, our simulation studies verify and clarify the proposed method.

scholarly journals Adaptive Fuzzy Synchronization of Fractional-Order Chaotic Neural Networks with Backlash-Like Hysteresis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Wenqing Fu ◽  
Heng Liu
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Control Method ◽  
Fuzzy Controller ◽  
Small Region ◽  
Fuzzy Logic Systems ◽  
Chaotic Neural Networks ◽  
Adaptive Fuzzy ◽  
Synchronization Errors ◽  
Lyapunov Stability Criterion

An adaptive fuzzy synchronization controller is designed for a class of fractional-order neural networks (FONNs) subject to backlash-like hysteresis input. Fuzzy logic systems are used to approximate the system uncertainties as well as the unknown terms of the backlash-like hysteresis. An adaptive fuzzy controller, which can guarantee the synchronization errors tend to an arbitrary small region, is given. The stability of the closed-loop system is rigorously analyzed based on fractional Lyapunov stability criterion. Fractional adaptation laws are established to update the fuzzy parameters. Finally, some simulation examples are provided to indicate the effectiveness and the robust of the proposed control method.

scholarly journals Fuzzy Adaptive Prescribed Performance Control for a Class of Uncertain Nonlinear Systems with Unknown Dead-Zone Inputs

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Xiang ◽  
Ning Li ◽  
Yeguo Sun
Keyword(s):  
Control Method ◽  
Dead Zone ◽  
Constrained System ◽  
Performance Control ◽  
Prescribed Performance ◽  
Performance Technique ◽  
Prescribed Performance Control ◽  
Feedback Control Method ◽  
Unknown Control ◽  
Fuzzy Adaptive

This paper proposes a fuzzy adaptive prescribed performance control scheme for a class of uncertain chaotic systems with unknown control gains and unknown dead-zone inputs. Firstly, an error transformation is introduced to transform the original constrained system into an equivalent unconstrained one. Then, based on the error transformation technique and the predefined performance technique, a fuzzy adaptive feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded. Finally, an illustrative example is given to demonstrate the effectiveness and usefulness of the proposed technique.

scholarly journals Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wei Xiang ◽  
Guangkui Xu ◽  
Fang Zhu ◽  
Chunzhi Yang
Keyword(s):  
Neural Control ◽  
Control Method ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Feedback Systems ◽  
Nonlinear Functions ◽  
Prescribed Performance ◽  
Complexity Problem ◽  
The Stability ◽  
Strict Feedback

This paper provides a disturbance observer-based prescribed performance control method for uncertain strict-feedback systems. To guarantee that the tracking error meets a design prescribed performance boundary (PPB) condition, an improved prescribed performance function is introduced. And radial basis function neural networks (RBFNNs) are used to approximate nonlinear functions, while second-order filters are employed to eliminate the “explosion-complexity” problem inherent in the existing method. Meanwhile, disturbance observers are constructed to estimate the compounded disturbance which includes time-varying disturbances and network construction errors. The stability of the whole closed-loop system is guaranteed via Lyapunov theory. Finally, comparative simulation results confirm that the proposed control method can achieve better tracking performance.

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