scholarly journals Decentralized Control for Large-Scale Interconnected Nonlinear Systems Based on Barrier Lyapunov Function

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Tao Guo
Keyword(s):  
Nonlinear Systems ◽  
Lyapunov Function ◽  
Tracking Control ◽  
Large Scale ◽  
Closed Loop ◽  
Barrier Lyapunov Function ◽  
Control Scheme ◽  
Asymptotic Tracking ◽  
Interconnected Nonlinear Systems ◽  
First Time

We present a novel decentralized tracking control scheme for a class of large-scale nonlinear systems with partial state constraints. For the first time, backstepping design with the newly proposed BLF is incorporated to effectively deal with the control problem of nonlinear systems with interconnected constraints. To prevent the states of each subsystem from violating the constraints, we employ a special barrier Lyapunov function (BLF), which grows to infinity whenever its argument approaches some finite limits. By ensuring boundedness of the barrier Lyapunov function in the closed loop, we ensure that those limits are not transgressed. Asymptotic tracking is achieved without violation of the constraints, and all closed-loop signals remain bounded. In the end, an illustrative example is presented to demonstrate the performance of the proposed control.

Tracking Control for Switched Nonlinear Systems Subject to Time-Varying Output Constraints

2012 ◽  
Author(s):  
Ben Niu ◽  
Georgi M. Dimirovski ◽  
Jun Zhao
Keyword(s):  
Nonlinear Systems ◽  
Lyapunov Function ◽  
Tracking Control ◽  
Closed Loop ◽  
Time Varying ◽  
Switched Nonlinear Systems ◽  
Feedback Form ◽  
Barrier Lyapunov Function ◽  
Output Constraints ◽  
Asymptotic Tracking

In this paper, we address the tracking control problem for switched nonlinear systems in strict-feedback form with time-varying output constraints. To prevent the output from violating the time-varying constraints, we employ a Barrier Lyapunov Function, which relies explicitly on time. Based on the simultaneous domination assumption, we design a controller for the switched system, which guarantees that asymptotic tracking is achieved without transgression of the constraints and all closed-loop signals remain bounded under arbitrary switchings. The effectiveness of the proposed results is illustrated using a numerical example.

scholarly journals Adaptive Tracking Control for a Class of Manipulator Systems with State Constraints and Stochastic Disturbances

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Wei Sun ◽  
Wenxing Yuan ◽  
Jing Zhang ◽  
Qun Sun
Keyword(s):  
Tracking Control ◽  
Closed Loop ◽  
State Constraints ◽  
Adaptive Controller ◽  
Compact Set ◽  
Closed Loop System ◽  
Stochastic Disturbances ◽  
Adaptive Tracking Control ◽  
Barrier Lyapunov Function ◽  
Control Scheme

An adaptive controller is constructed for a class of stochastic manipulator nonlinear systems in this paper. The states are constrained in the compact set. A tan-type Barrier Lyapunov Function (BLF) is employed to deal with state constraints. The proposed control scheme guarantees the output error convergence to a small neighbourhood of zero. All the signals in the closed-loop system are bounded. The simulation results illustrate the validity of the proposed method.

scholarly journals Robust Tracking Control of the Euler–Lagrange System Based on Barrier Lyapunov Function and Self-Structuring Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yi Wang ◽  
He Ma ◽  
Weidong Wu
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Lyapunov Function ◽  
Tracking Control ◽  
Tracking Error ◽  
Robust Tracking ◽  
Robust Tracking Control ◽  
Barrier Lyapunov Function ◽  
Control Scheme ◽  
Unknown Disturbances

This article studies the robust tracking control problems of Euler–Lagrange (EL) systems with uncertainties. To enhance the robustness of the control systems, an asymmetric tan-type barrier Lyapunov function (ATBLF) is used to dynamic constraint position tracking errors. To deal with the problems of the system uncertainties, the self-structuring neural network (SSNN) is developed to estimate the unknown dynamics model and avoid the calculation burden. The robust compensator is designed to estimate and compensate neural network (NN) approximation errors and unknown disturbances. In addition, a relative threshold event-triggered strategy is introduced, which greatly saves communication resources. Under the proposed robust control scheme, tracking behavior can be implemented with disturbance and unknown dynamics of the EL systems. All signals in the closed-loop system are proved to be bounded by stability analysis, and the tracking error can converge to the neighborhood near the origin. The numerical simulation results show the effectiveness and the validity of the proposed robust control scheme.

Adaptive decentralized tracking control of a class of large-scale nonlinear systems with unknown dead-zone inputs using neural network

2019 ◽  
Vol 41 (16) ◽  
pp. 4499-4510 ◽  
Author(s):  
Yu-Qun Han ◽  
Shan-Liang Zhu ◽  
De-Yu Duan ◽  
Lei Chu ◽  
Shu-Guo Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
Large Scale ◽  
Controller Design ◽  
Dead Zone ◽  
Computational Cost ◽  
Tracking Errors ◽  
Control Approach ◽  
Control Scheme

In this paper, an adaptive decentralized control approach is proposed for a class of large-scale nonlinear systems with unknown dead-zone inputs using neural network. Firstly, the dead-zone outputs are firstly represented as simple linear systems with a static time-varying gain and bounded disturbance by introducing characteristic function. Secondly, in the controller design, neural networks are utilized to approximate the unknown nonlinear functions. Thirdly, an adaptive decentralized tracking control approach is constructed via backstepping design technique. It is shown that the proposed control approach can assure that all the signals of the closed-loop system semi-globally uniformly ultimately bounded and the tracking errors finally converge to a small domain around the origin. The proposed method can get precise tracking results with low computational cost, and have a good real-time performance and convergence. Finally, two examples are given to demonstrate the effectiveness of the proposed control scheme.

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