Adaptive Sliding Mode Control for Uncertain Euler-Lagrange Systems with Input Saturation

Author(s):  
Ke Shao ◽  
Rongchuan Tang ◽  
Feng Xu ◽  
Xueqian Wang ◽  
Jinchuan Zheng
Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Maode Yan ◽  
Jiacheng Song ◽  
Panpan Yang ◽  
Lei Zuo

This paper investigates the vehicle platoon control problems with both velocity constraints and input saturation. Firstly, radial basis function neural networks (RBF NNs) are employed to approximate the unknown driving resistance of a vehicle’s dynamic model. Then, a bidirectional topology, where vehicles can only communicate with their direct preceding and following neighbors, is used to depict the relationship among the vehicles in the platoon. On this basis, a neural adaptive sliding-mode control algorithm with an anti-windup compensation technique is proposed to maintain the vehicle platoon with desired distance. Moreover, the string stability and the strong string stability of the whole vehicle platoon are proven through the stability theorem. Finally, numerical simulations verify the feasibility and effectiveness of the proposed control method.


2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Jiangbin Wang ◽  
Ling Liu ◽  
Chongxin Liu ◽  
Xiaoteng Li

The main purpose of the paper is to control chaotic oscillation in a complex seven-dimensional power system model. Firstly, in view that there are many assumptions in the design process of existing adaptive controllers, an adaptive sliding mode control scheme is proposed for the controlled system based on equivalence principle by combining fixed-time control and adaptive control with sliding mode control. The prominent advantage of the proposed adaptive sliding mode control scheme lies in that its design process breaks through many existing assumption conditions. Then, chaotic oscillation behavior of a seven-dimensional power system is analyzed by using bifurcation and phase diagrams, and the proposed strategy is adopted to control chaotic oscillation in the power system. Finally, the effectiveness and robustness of the designed adaptive sliding mode chaos controllers are verified by simulation.


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