Real-time active suspension control design using adaptive inverse control

2010 ◽  
Author(s):  
Yan Shiyuan ◽  
Cao Lijia ◽  
Xi Tao ◽  
Liu Yinan ◽  
Zhang Shengxiu
Keyword(s):  
Real Time ◽  
Control Design ◽  
Active Suspension ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Suspension Control

Adaline neural network-based adaptive inverse control for an electro-hydraulic servo system

2011 ◽  
Vol 17 (13) ◽  
pp. 2007-2014 ◽  
Author(s):  
Jianjun Yao ◽  
Xiancheng Wang ◽  
Shenghai Hu ◽  
Wei Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Servo System ◽  
Inverse Model ◽  
Lms Algorithm ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
The Neural Network ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

Based on adaptive inverse control theory, combined with neural network, neural network adaptive inverse controller is developed and applied to an electro-hydraulic servo system. The system inverse model identifier is constructed by neural network. The task is accomplished by generating a tracking error between the input command signal and the system response. The weights of the neural network are updated by the error signal in such a way that the error is minimized in the sense of mean square using (LMS) algorithm and the neural network is close to the system inverse model. The above steps make the gain of the serial connection system close to unity, realizing waveform replication function in real-time. To enhance its convergence and robustness, the normalized LMS algorithm is applied. Simulation in which nonlinear dead-zone is considered and experimental results demonstrate that the proposed control scheme is capable of tracking desired signals with high accuracy and it has good real-time performance.

scholarly journals MPC Based Semi-active Suspension Control for Overtaking Maneuvers

2021 ◽  
Author(s):  
Tamás Hegedűs ◽  
Balázs Németh ◽  
Péter Gáspár
Keyword(s):  
Autonomous Vehicles ◽  
Control Process ◽  
Control Design ◽  
Active Suspension ◽  
Simulation Software ◽  
Dynamics Simulation ◽  
Vertical Control ◽  
Maximum Value ◽  
Suspension Control ◽  
The Given

In this paper, the lateral and vertical control design is presented for autonomous vehicles. The vertical control of the vehicle is based on a semi-active suspension system. In the first step, a decision-making process is made. Based on the results of this algorithm an optimal trajectory is planned. Since the trajectory is known, the lateral accelerations can be computed for the given control horizon. In the second step, the vertical control is achieved, which uses the results of the trajectory planning algorithms. The control design is made by a Model Predictive Control (MPC), in which the sign and the maximum value of the additional force can be taken into account. The main goal in the vertical control process is vehicle roll angle minimization. The results of the algorithm are validated using a high fidelity vehicle dynamics simulation software, CarMaker.

Study on Control Technology for Vehicle Active Suspension

2011 ◽  
Vol 143-144 ◽  
pp. 69-73
Author(s):  
Xiao Bin Fan ◽  
Bing Xu Fan ◽  
Hui Gang Wang
Keyword(s):  
Pid Control ◽  
Ride Comfort ◽  
Design Method ◽  
Control Design ◽  
Active Suspension ◽  
Fuzzy Pid ◽  
Linear Quadratic ◽  
Fuzzy Pid Control ◽  
Suspension Control ◽  
Lqg Control

An active suspension system has been proposed to improve the ride comfort. A quarter-car 2 degree-of-freedom system is designed and constructed on the basis of the concept of a four-wheel independent suspension. The aim of the work described in the paper was to illustrate the application of fuzzy Proportional Integration Derivative (PID) technique and Linear Quadratic Guass (LQG) control to the active suspension control system. The paper describes also the model and controller used in the study and discusses the vehicle response results obtained from a range of road input simulations. This work describes some comparison of active suspension fuzzy PID control and LQG control design method by MATLAB simulations. Simulation results show that the LQG controller achieved better performances in all carried-out investigations.

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