Disturbance rejection and feedback control algorithm for active suspension plant

2013 ◽  
Author(s):  
Shine Kamal ◽  
Krishna G. Namboothiri
Keyword(s):  
Feedback Control ◽  
Disturbance Rejection ◽  
Control Algorithm ◽  
Active Suspension

Feedforward and Feedback Control of Lane Keeping by the Human Driver

2007 ◽  
Author(s):  
Jing Zhou ◽  
Huei Peng
Keyword(s):  
Feedback Control ◽  
Disturbance Rejection ◽  
Control Algorithm ◽  
Lateral Position ◽  
Simulation Environment ◽  
Perceptual Cues ◽  
Lane Tracking ◽  
Human Driver ◽  
Road Geometry ◽  
Lane Keeping

A feedforward/feedback control model of drivers’ lane keeping behavior is presented in this paper. The model is based on the linearized analysis of the driver’s curve negotiation dynamics. In real driving, the driver previews the upcoming road geometry and relies on perceived vehicle states to maintain a desired lateral position. Feedforward and feedback roles are associated with different perceptual cues, and the lane keeping task is formulated into a disturbance rejection problem. Control parameters are determined to reflect natural stable human characteristics. Verification tests in a realistic simulation environment demonstrate the ability of the model to generate driver/vehicle lane keeping responses comparable to those obtained on a simulator. Potentially, the derived control algorithm can also be applied to automatic lane-tracking as long as reliable information regarding vehicle states and upcoming road conditions is accessible.

Control of a Helicopter Main Gearbox Active Suspension System

2014 ◽  
Author(s):  
Jonathan Rodriguez ◽  
Luc Gaudiller ◽  
Simon Chesne ◽  
Paul Cranga
Keyword(s):  
Disturbance Rejection ◽  
Control Algorithm ◽  
Narrow Band ◽  
Active Suspension ◽  
Frequency Bandwidth ◽  
Active System ◽  
Multibody Model ◽  
Suspension Systems ◽  
Set Up ◽  
New Generation

This paper considers the control of a helicopter gearbox electromagnetic suspension for a complete multibody model of the structure. As the new generation of helicopters includes variable engine RPM during flight, it becomes relevant to add active control in their suspension systems. Most of active system performances derive directly from the controller construction, its optimization to the system controlled and the disturbances expected. An investigation on a FXLMS control algorithm has been made to optimize it in terms of narrow band disturbance rejection. In this paper an active suspension based on DAVI principle is evaluated. Firstly, a multibody model is set up to estimate realistic acceleration levels inside the cabin. Then multiple controllers are tested, minimizing vibrations on different parts of the helicopter structure. The simulations tend to prove that it is possible to implement an effective active suspension with a low power actuator and obtain a significant vibration reduction level for a frequency bandwidth centered at the natural frequency of the original DAVI.

scholarly journals Adaptive Kalman Filter with L2 Feedback Control for Active Suspension Using a Novel 9-DOF Semi-Vehicle Model

Actuators ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 267
Author(s):  
Huan Yang ◽  
Jiang Liu ◽  
Min Li ◽  
Xilong Zhang ◽  
Jianze Liu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Feedback Control ◽  
Control Algorithm ◽  
Active Suspension ◽  
Adaptive Kalman Filter ◽  
Feedback Matrix ◽  
Kalman Filter Algorithm ◽  
Feedback Algorithm ◽  
Suspension Model ◽  
Rear Tire

In order to further improve driving comfort, this paper takes the semi-vehicle active suspension as the research object. Furthermore, combined with a 5-DOF driver-seat model, a new 9-DOF driver seat-active suspension model is proposed. The adaptive Kalman filter combined with L2 feedback control algorithm is used to improve the controller. First, a discrete 9-DOF driver seat-active suspension model is established. Then, the L2 feedback algorithm is used to solve the optimal feedback matrix of the model, and the adaptive Kalman filter algorithm is used to replace the linear Kalman filter. Finally, the improved active suspension model and algorithm are verified through simulation and test. The results show that the new algorithm and model not only significantly improve the driver comfort, but also comprehensively optimize the other performance of the vehicle. Compared with the traditional LQG control algorithm, the RMS value of the acceleration experienced by the driver’s limb are, respectively, decreased by 10.9%, 15.9%, 6.4%, and 7.5%. The RMS value of pitch angle acceleration experienced by the driver decreased by 6.4%, and the RMS value of the dynamic tire deflection of front and rear tire decreased by 32.6% and 12.1%, respectively.

Dynamic monitoring feedback iteration control for damping-adjustable semi-active suspension systems

2021 ◽  
pp. 1-9
Author(s):  
Chongchong Li ◽  
Jiangyong Xiong ◽  
Tingshan Liu ◽  
Ziang Zhang
Keyword(s):  
Root Mean Square ◽  
Working Conditions ◽  
Control Algorithm ◽  
Active Suspension ◽  
Dynamic Monitoring ◽  
Iteration Algorithm ◽  
Mean Square ◽  
Control Effect ◽  
Analytic Hierarchy ◽  
Vehicle Simulation

In order to further improve vehicle ride performance, a dynamic monitoring feedback iteration control algorithm is proposed by combining the features of a variable-damping semi-active suspension system and applying them to the system. A seven-degree-of-freedom finished vehicle simulation model is built based on MATLAB/Simulink. The root-mean-square values of the acceleration of the sprung mass, the dynamic travel of the suspension and the dynamic tire load are taken as evaluation indicators of vehicle ride performance. An analytic hierarchy process (AHP) is used to determine the weighting coefficients of the evaluation indicators, and a genetic algorithm is utilized to determine the optimal damping of the suspension under various typical working conditions. Suspension damping is controlled with a dynamic monitoring feedback iteration algorithm. The correction coefficients of the control algorithm are determined according to the deviation between the obtained damping and the optimized damping so that the control parameters will agree with the optimal result under typical working conditions, and the control effect under other working conditions is verified. The simulation results indicate that the proposed dynamic monitoring feedback iteration control algorithm can effectively reduce the root-mean-square value of the acceleration of the sprung mass by 10.56% and the root-mean-square value of the acceleration of the dynamic travel of the suspension by 11.98% under mixed working conditions, thus improving vehicle ride performance. The study in this paper provides a new attempt for damping control of semi-active suspension and lays a theoretical foundation for its application in engineering.

scholarly journals Comparative simulation study on two estimation methods and two control strategies for semi-active suspension

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110073
Author(s):  
Wang Xin ◽  
Gu Liang ◽  
Dong Mingming ◽  
Li Xiaolei
Keyword(s):  
Sliding Mode Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Structural Characteristics ◽  
Active Suspension ◽  
Road Surface ◽  
Estimation Methods ◽  
Vehicle Dynamic ◽  
Mode Control ◽  
Computational Overhead

With regard to the structural characteristics of the McPherson suspension system, when a vehicle is being driven on a rough road surface, the force direction of the suspension varies. This poses challenges to the vehicle’s driving safety and handling stability. Based on Lagrangian equations, this paper proposes a new nonlinear semi-vehicle suspension model and presents comparative studies, conducted through simulation, on the estimated accuracy and computational overhead of the small-computational-overhead extended Kalman filter (EKF) and unscented Kalman estimation (UKF) methods, and on the effectiveness of the skyhook sliding mode control (SHSMC) and nonlinear skyhook-sliding mode control (NSHSMC) semi-active suspension control methods. The response of the vehicle to the state estimation algorithm was evaluated through computer simulations using the Carsim vehicle dynamic software. The simulation results reveal that the vehicle dynamic states were satisfactorily estimated when the vehicle was driven on a rough road surface. Compared with the small-computational-overhead EKF algorithm, the estimated results of these variables based on the UKF algorithm have higher accuracy. However, the UKF algorithm requires longer computation time compared with the EKF algorithm. The SHSMC control algorithm achieved greater improvement for the vehicle’s drive handling stability in the 6–10-Hz vibration region compared with the NSHSMC control algorithm. In a high-frequency region over 10Hz, the semi-active suspension controlled by the SHSMC method had a more adverse effect on the driving comfort.

Improved disturbance rejection with online adaptive pole-zero compensation on a Φ-shaped PZT active suspension

2009 ◽  
Vol 15 (10-11) ◽  
pp. 1499-1508 ◽  
Author(s):  
Chee Khiang Pang ◽  
Sai Cheong Tam ◽  
Guoxiao Guo ◽  
Ben M. Chen ◽  
Frank L. Lewis ◽  
...  
Keyword(s):  
Disturbance Rejection ◽  
Active Suspension
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