An Adaptive Neural Network Controller for Active Suspension Systems With Hydraulic Actuator

2020 ◽  
Vol 50 (12) ◽  
pp. 5351-5360 ◽  
Author(s):  
Yan-Jun Liu ◽  
Qiang Zeng ◽  
Lei Liu ◽  
Shaocheng Tong
Keyword(s):  
Neural Network ◽  
Active Suspension ◽  
Hydraulic Actuator ◽  
Adaptive Neural Network ◽  
Suspension Systems ◽  
Neural Network Controller ◽  
Active Suspension Systems ◽  
Network Controller

scholarly journals Adaptive neural network sliding mode control for active suspension systems with electrohydraulic actuator dynamics

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094198
Author(s):  
Jinwei Sun ◽  
Kai Zhao
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Active Suspension ◽  
Sliding Mode Controller ◽  
Adaptive Neural Network ◽  
Actuator Dynamics ◽  
Suspension Systems ◽  
Active Suspension Systems ◽  
Highly Nonlinear ◽  
Electrohydraulic Actuator

The object of this article is to design an observer-based adaptive neural network sliding mode controller for active suspension systems. A general nonlinear suspension model is established, and the electrohydraulic actuator dynamics are considered. The proposed controller is decomposed into two loops. Since the dynamics of the actuator is assumed highly nonlinear with uncertainties, the adaptive neural network is presented in the inner loop to ensure the control system robustness against uncertainties, and the self-tuning weighting vector is adjusted online according to the updated law obtained by Lyapunov stability theory. In the outer loop, a model reference sliding mode controller is developed to track the desired states of the hybrid reference model that combines skyhook and groundhook control methods. Besides, to obtain the unmeasured states of the system, an unscented Kalman filter is utilized to provide necessary information for the controller. Simulation results show that the exerted force can be tracked precisely even in the existence of uncertainties. Moreover, the proposed controller can improve the suspension’s performance effectively.

scholarly journals Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqing Xia ◽  
Xingchao Shao ◽  
Ang Zhao ◽  
Huiyong Wu
Keyword(s):  
Neural Network ◽  
Explicit Knowledge ◽  
Dead Zone ◽  
Nonlinear Function ◽  
Lyapunov Theory ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Adaptive Neural Network Control ◽  
Fuzzy Logic Technique

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

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