Vibration Isolation of Railway Vehicle Car body using Semi-active Suspension

In the past, the magnetorheological (MR) suspension for railway vehicle has obtained great attention for the isolation of vibrations. This work presents a numerical approach to analyse skyhook and ground hook semi-active control methods for railway vehicle suspension. A 10 DoF model of the railway vehicle system is formulated for the comparative analysis between conventional passive and semi-active suspension control in the present study. The non-linear analysis is investigated in time and frequency domain for the sinusoidal excitations from the track.

Design of Static Output Feedback and Structured Controllers for Active Suspension with Quarter-Car Model

This paper presents a method to design active suspension controllers for a 7-Degree-of-Freedom (DOF) full-car (FC) model from controllers designed with a 2-DOF quarter-car (QC) one. A linear quadratic regulator (LQR) with 7-DOF FC model has been widely used for active suspension control. However, it is too hard to implement the LQR in real vehicles because it requires so many state variables to be precisely measured and has so many elements to be implemented in the gain matrix of the LQR. To cope with the problem, a 2-DOF QC model describing vertical motions of sprung and unsprung masses is adopted for controller design. LQR designed with the QC model has a simpler structure and much smaller number of gain elements than that designed with the FC one. In this paper, several controllers for the FC model are derived from LQR designed with the QC model. These controllers can give equivalent or better performance than that designed with the FC model in terms of ride comfort. In order to use available sensor signals instead of using full-state feedback for active suspension control, LQ static output feedback (SOF) and linear quadratic Gaussian (LQG) controllers are designed with the QC model. From these controllers, observer-based controllers for the FC model are also derived. To verify the performance of the controllers for the FC model derived from LQR and LQ SOF ones designed with the QC model, frequency domain analysis is undertaken. From the analysis, it is confirmed that the controllers for the FC model derived from LQ and LQ SOF ones designed with the QC model can give equivalent performance to those designed with the FC one in terms of ride comfort.

Design of and Research into a Multiple-Fuzzy PID Suspension Control System Based on Road Recognition

By analyzing the shortcomings of the traditional fuzzy PID(Abbreviation for Proportional, Integral and Differential) control system (FPID), a multiple fuzzy PID suspension control system based on road recognition (MFRR) is proposed. Compared with the traditional fuzzy PID control system, the multiple fuzzy control system can identify the road grade and take changes in road conditions into account. Based on changes in road conditions and the variable universe and secondary adjustment of the control parameters of the PID controller were carried out, which makes up for the disadvantage of having too many single input parameters in the traditional fuzzy PID control system. A two degree of freedom 1/4 vehicle model was established. Based on the suspension dynamic parameters, a road elevation algorithm was designed. Road grade recognition was carried out based on a BP neural network algorithm. The experimental results showed that the sprung mass acceleration (SMA) of the MFRR was much smaller than that of the passive suspension system (PS) and the FPID on single-bump and sinusoidal roads. The SMA, suspension dynamic deflection (SDD) and tire dynamic load (TDL) of the MFRR were significantly less than those of the other two systems on roads of each grade. Taking grade B road as an example, compared with the PS, the reductions in the SMA, SDD and TDL of the MFRR were 40.01%, 34.28% and 32.64%, respectively. The control system showed a good control performance.

Vibration Control of Semi-Active Suspension System using Modified Skyhook with Advanced Firefly Algorithm

The semi-active suspension (SAS) system is a partial suspension device used in the vehicle system to improve the ride comfort and road handling. Due to the high non-linearity of the road profile disturbances plus uncertainties derived from vehicle dynamics, a conventional Skyhook controller is not deemed enough for the vehicle system to improve the performance. A major problem of the implementation of the controller is to optimize a proper parameter as this is an important element in demanding a good controller response. An advanced Firefly Algorithm (AFA) integrated with the modified skyhook (MSky) is proposed to enhance the robustness of the system and thus able to improve the vehicle ride comfort. In this paper, the controller scheme to be known as MSky-AFA was validated via MATLAB simulation environment. A different optimizer based on the original firefly algorithm (FA) is also studied in order to compute the parameter of the MSky controller. This control scheme to be known as MSky-FA was evaluated and compared to the proposed MSky-AFA as well as the passive suspension control. The results clearly exhibit more superior and better response of the MSky-AFA in reducing the body acceleration and displacement amplitude in comparison to the MSky-FA and passive counterparts for a sinusoidal road profile condition.

Research on Siding Mode Controller of High-Speed Maglev Train Under Aerodynamic Load

The high-speed maglev train will be subjected to extremely obvious aerodynamic load during operation, it will also be subjected to instantaneous aerodynamic impact load in the case of passing, which will bring extreme challenges to the suspension stability and safe operation of the train. It is necessary to consider the influence of aerodynamic load and shock wave in the design of suspension control algorithm. Traditional proportion integration differentiation (PID) control cannot follow the change of vehicle parameters or external disturbance, which is easy to cause suspension fluctuation and instability. In order to improve the suspension stability and vibration suppression of high-speed maglev train under aerodynamic load and impact, a controller based on sliding mode technique is designed in this paper, and in this controller, the deformation of the primary suspension is introduced to replace the aerodynamic load on the electromagnet. In order to verify the control performance of the designed controller, the dynamic simulation model of train with three vehicles is established, and the dynamic response of the train under the condition of passing in open air is calculated. Compared with the PID controller, it is verified that the sliding mode control (SMC) method proposed in this paper can effectively restrain the electromagnet fluctuation of the train under aerodynamic load.

Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.