Suspension Model Research Articles

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay.

The uncertain delay characteristic of actuators is a critical factor that affects the control effectiveness of the active suspension system. Therefore, it is crucial to develop a control algorithm that takes into account this uncertain delay in order to ensure stable control performance. This study presents a novel active suspension control algorithm based on deep reinforcement learning (DRL) that specifically addresses the issue of uncertain delay. In this approach, a twin-delayed deep deterministic policy gradient (TD3) algorithm with system delay is employed to obtain the optimal control policy by iteratively solving the dynamic model of the active suspension system, considering the delay. Furthermore, three different operating conditions were designed for simulation to evaluate the control performance: deterministic delay, semi-regular delay, and uncertain delay. The experimental results demonstrate that the proposed algorithm achieves excellent control performance under various operating conditions. Compared to passive suspension, the optimization of body vertical acceleration is improved by more than 30%, and the proposed algorithm effectively mitigates body vibration in the low frequency range. It consistently maintains a more than 30% improvement in ride comfort optimization even under the most severe operating conditions and at different speeds, demonstrating the algorithm's potential for practical application.

Dynamic Responses of 8-DoF Vehicle with Active Suspension: Fuzzy-PID Control

The driving smoothness of vehicles is heavily influenced by their suspension system, and implementing active suspension control can effectively minimize the vibration movement of the vehicle and ensure a comfortable driving experience. An 8-DoF active suspension model of the full vehicle is established, and a fuzzy-PID controller is designed to autonomously regulate the parameters of the PID controller. Using the MATLAB/Simulink environment, a simulation model for suspension is created, and the vibration characteristics of passive, PID control, and fuzzy-PID control suspensions are compared with the help of the continuous crossing road hump model and C-level road model as road inputs. The results show that the utilization of fuzzy-PID control considerably diminishes the vertical, pitch, and roll oscillations of the suspension body and modifies the suspension dynamic deflection and tire dynamic load in contrast to the other two scenarios, thus enhancing ride comfort. Fuzzy-PID control led to a decrease of approximately 40% in acceleration, 25% in suspension workspace, and 30% in tire deflection compared to passive suspension. In addition, the reduction in acceleration is about 20%, the reduction in suspension workspace is approximately 10%, and the reduction in tire deflection is about 15% compared to the PID control suspension system.

Multi-Sensor Graph Transfer Network for Health Assessment of High-Speed Rail Suspension Systems

Suspension systems are significant for safe and comfort operation of high-speed trains. Health assessment is a useful tool to schedule maintenance plans of suspension systems, and furthermore ensure safety operation of high-speed railway transportation. In real operating condition, two problems, i.e. data imbalance and shortage of labelled data, result in difficult for health assessment of the suspension system using deep learning. In this work, a multi-sensor information fusion method, called as multi-sensor graph transfer network (MSGTN), is proposed in basis of deep transfer learning and graph neural network. In the proposed method, a domain-share multi-sensor graph neural network (MSGNN) is firstly proposed to extract features from vibration signals collected from three different positions in train vehicles. A graph-based fusion layer in MSGNN is proposed to fuse multi-sensor information by combining frequency response curves of the suspension system. The MSGTN mainly includes two parts in source and target domains respectively. In the source domain, a simple physical dynamic model of high-speed rail suspension system is built to generate labelled simulation datasets to pre-train MSGNN. In the target domain, the initial hyper-parameters of MSGNN are that of the pre-train model in the source domain. The labelled data in the target domain is fed to fine-tune MSGNN and then the final model for health assessment can be obtained by minimizing the loss function. The effectiveness of the proposed method was verified using real-work operation data.

A revisited compression flow model for concentrated suspensions with fiber-friction interactions

A revisited compression flow model for concentrated suspensions with fiber-friction interactions

Superposition of populations in multi-objective evolutionary optimization of car suspensions

The design of the suspension model of road cars is extremely important for both driver comfort and safety. Road traffic injuries are currently the eighth leading cause of death worldwide and are predicted to become the seventh leading cause of death by 2030, according to the World Health Organization (WHO). Among other factors, road infrastructure is one of the most important factors to consider when designing the suspension model, especially as autonomous driving becomes more popular. In addition, there is a growing need to test and study the impact of road roughness and pavement type on autonomous vehicles (AVs). This work provides the following contributions: a platform for suspension system optimization–flexible, highly parameterizable and rich in applications. Our solution implements, tests and optimises the 2, 3 and 4 degrees of freedom (DOF) quarter-car models (QCM) with linear seat suspension and the 5 DOF half-car model (HCM), respectively, based on random road profiles generated according to ISO 8608 standards. In addition, many multi-objective optimization (MOO) algorithms have been implemented and applied in isolation to see their comparative performance (in terms of Pareto fronts, hypervolume, coverage, spread, and epsilon), but most importantly we have also introduced a meta-optimization technique by super-positioning Non-dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2, where each of the algorithms, running concurrently, produces a percentage of the total number of individuals in a generation, percentage that is calculated according to the solution quality of the individuals in the previous generation. The experimental results were obtained by simulating the optimal configurations generated by the MOO heuristic algorithms on different categories of cars (small/medium). Finally, the Taguchi method and regression analysis were used to evaluate the effects of specific parameters on system responses such as sprung mass acceleration and displacement. The Taguchi simulation was performed to experimentally validate the results obtained by metaheuristic optimization methods.

EFFECTS OF LOADING MASS ECCENTRICITIES ON UNSPRUNG MASS ROTATIONAL DYNAMIC RESPONSES OF HEAVY VEHICLE ON IRREGULAR ROAD PAVEMENT

Using individual independent unsprung masses in suspension system modeling is not a realistic reflection of vehicle dynamics in response to eccentric wheel kinematics and asymmetric heavy vehicle loading. This is because the use of such masses does not accurately represent the unsprung mass system's influence on heavy vehicles' dynamics, including its impact on the sprung masses or wheel pavement dynamic loading. This study aims to examine how heavy vehicle asymmetric loading and wheels' eccentric kinematic excitation affect the rotational dynamic response of the unsprung masses. A total of 450 models were used to investigate various asymmetrical cases, from which 9 3D 24 DOF heavy vehicles' 4 DOF rear tandem drive axle suspension systems were selected for analysis using SIMWISE 4D. These vehicles were tested for a pothole depth of 25 mm and a vehicle translational speed of 60 km/h. The study found that the nature of the pavement distress factor (kinematic excitation) determines the suspension's rotational dynamic response and, consequently, the sprung mass. Moreover, the load distribution on the sprung mass also affects the rotational dynamic response of the tandem beams

Determination of optimal battery locations for ride comfort in electric automobiles using a nonlinear half-vehicle suspension model

Batarya elektrikli araçların ağırlığına katkıda bulunan en büyük kalemlerden biridir ve konumu süspansiyon sisteminin performansını doğrudan etkilemektedir. Bu makalenin amacı tekerlek içi motorlu elektrikli otomobillerde sürüş konforu açısından optimal batarya konumlarının lineer olmayan bir taşıt süspansiyon modeli kullanılarak belirlenmesidir. Analizlerde tüm yay ve amortisörlerin lineer karakteristiklerine ilaveten kübik doğrusalsızlıklarının da hesaba katıldığı yedi serbestlik dereceli bir yarım taşıt süspansiyon modeli kullanılmıştır. Modelde yay ve amortisör doğrusalsızlıklarına ek olarak tüm trigonometrik doğrusalsızlıklar da dikkate alınmıştır. Sinüzoidal formda 48 farklı yol profili ve 3 farklı ilerleme hızı ile toplamda 144 farklı sürüş senaryosu oluşturulmuş ve her bir senaryo için aracın boylamasına ekseni boyunca 36 batarya konumu test edilerek optimal olanı bulunmuştur. Optimizasyon kriteri, sürücü ve koltuğunun dikey ivmesinin kök ortalama kare değerinin minimizasyonudur. Gerçekleştirilen 5184 analiz neticesinde optimal batarya konumunun 0,2 ila 5 m arasındaki dalga boylarına sahip yol profilleri için aracın orta kısmı; 10 ila 30 m arasındaki dalga boylarına sahip yol profilleri içinse aracın arka kısmı olduğu görülmüştür.

Modelling and Dynamic Analysis of Adaptive Neuro-Fuzzy Inference System-Based Intelligent Control Suspension System for Passenger Rail Vehicles Using Magnetorheological Damper for Improving Ride Index

The ride comfort and safety of passenger rail vehicles depend on the performance of the suspension system in attenuating vibrations induced by track irregularities. This paper investigates the effectiveness of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based semi-active controlled suspension system using a magnetorheological fluid damper in reducing nonlinear lateral vibrations of a passenger rail vehicle. A complete rail vehicle model is developed, including the carbody, front and rear bogies, and the passive suspension system’s nonlinear stiffness and damping characteristics are considered from experimental data. The passive suspension model is validated through experiments, and an ANFIS-based controller is incorporated with the secondary vertical suspension system to improve ride behavior. Three semi-active suspension strategies are considered under varying speeds and track irregularities, and their effectiveness is compared to the nonlinear passive suspension system in terms of rms acceleration, rms displacement, ride quality, and comfort. The results shows that the ANFIS-based semi-active suspension system with a magnetorheological fluid damper outperforms the passive suspension system and semi-active strategies in all tested conditions. There is a reduction in rms acceleration by approximately 11.11% to 23.64% and rms displacement by about 5.36% to 32.06%. Moreover, it significantly improves ride quality (9.20% to 31.02%) and comfort (9.96% to 31.50%). The rms acceleration and displacement are reduced, and the Sperling ride index and Percentage Reduction Index values demonstrate that the ANFIS-based semi-active suspension effectively minimizes the impact of rail irregularities and vibrations, resulting in a significant gain in ride quality and passenger comfort.

Time-varying nonlinear parameters identification of high-speed train suspension system based on WMA

The parameters identification of suspension components provides a foundation for health assessment of the high-speed train. Considering the time-varying nonlinearity of suspension parameters caused by changing temperature, this work identifies the time-varying nonlinear parameters based on the wavelet multiresolution analysis (WMA) combined with the proposed improved Akaike information criterion (AIC). Firstly, the parameters variation law against time-temperature are fitted based on test data and the vibration responses of a two-degree-of-freedom suspension model are obtained. Secondly, the time-varying nonlinear parameters are expanded into scale coefficients using WMA. Thus, the identification of time-varying nonlinear parameters is transformed into the identification of time-invariant wavelet scale coefficients. Then the improved AIC is utilized to select the optimal basis functions and decomposition scales in noisy environments. The identified parameters vectors are used by the improved AIC as evaluation index of identification model to reduce the effect of noise. Finally, the time-varying nonlinear parameters are reconstructed based on the optimal wavelet scale coefficients. The results show that the time-varying nonlinear stiffness and damping coefficients can be accurately identified based on the WMA combined with the proposed improved AIC.

Fabrication of Electromagnetic Suspension System

The power generator electromagnetic suspension system is a system that converts vehicle bump generated linear motion & vibration, into electricity to be used in battery charging. General vehicle shock absorbers are used to simply absorb this energy without converting it to electricity. So here we put forth a way to use this free energy and store it for further needs such as vehicle lights, cooling, indicator lights etc. The devised prototype Suspension System uses the energy to generate electricity which is otherwise dissipated. In this work, process of arriving at the fabricated model of Suspension System for Power Generation is elaborated. The preliminary and existing techniques which help in regenerating energy dissipation in suspensions are discussed and the way they led to contrive the concept of prototype Suspension System is elucidated. With the concept, the derivation of specifications and the process of designing the Suspension System is delineated. The Suspension System generated a potential difference in the range of 125 mV to 350 mV.

Control of Quarter-Car Active Suspension System Based on Optimized Fuzzy Linear Quadratic Regulator Control Method

Vehicle suspension systems, which affect driving performance and passenger comfort, are actively researched with the development of technology and the insufficient quality of passive suspension systems. This paper establishes the suspension model of a quarter of the car and active control is realized. The suspension model was created using the Lagrange–Euler method. LQR, fuzzy logic control (FLC), and fuzzy-LQR control algorithms were developed and applied to the suspension system for active control. The purpose of these controllers is to improve car handling and passenger comfort. Undesirable vibrations occur in passive suspension systems. These vibrations should be reduced using the proposed control methods and a robust system should be developed. To enhance the performance of the fuzzy logic control (FLC) and fuzzy-LQR control methods, the optimal values of the coefficients of the points where the feet of the member functions touch are calculated using the particle swarm optimization (PSO) algorithm. Then, the designed controllers were simulated in the computer environment. The success of the control performance of the applied methods concerning the passive suspension system was compared in percentages. The results are presented and evaluated graphically and numerically. Using the integral time-weighted absolute error (ITAE) criterion, the methods were compared with each other and with the studies in the literature. As a result, it was found that the proposed control method (fuzzy-LQR) is about 84.2% more successful in body motion, 90% in car acceleration, 84.5% in suspension deflection, and 86.7% in tire deflection compared to the studies in the literature. All these results show that the car’s ride comfort has been significantly improved.

Recent Progress in the Viscosity Modeling of Concentrated Suspensions of Unimodal Hard Spheres

The viscosity models for concentrated suspensions of unimodal hard spheres published in the twenty-first century are reviewed, compared, and evaluated using a large pool of available experimental data. The Pal viscosity model for unimodal suspensions is the best available model in that the predictions of this model agree very well with the low (zero)-shear experimental relative viscosity data for coarse suspensions, nanosuspensions, and coarse suspensions thickened by starch nanoparticles. The average percentage error in model predictions is less than 6.5%. Finally, the viscous behavior of concentrated multimodal suspensions is simulated using the Pal model for unimodal suspensions.

Evaluating the Isolation Performance of Three Seat Suspension Models of Off-Road Vehicles

<div>Three suspension structures including the parallel vertical suspension (PVS), the horizontal parallel suspension (HPS), and the negative stiffness element added into suspension (NSES) of the driver’s seat are proposed to improve the driver’s ride comfort of off-road vehicles. Based on the dynamic models of the PVS, HPS, and NSES established and simulated under the same random excitations of the cab floor, the effect of the design parameters of the proposed models is analyzed, and the design parameters are then optimized to evaluate their isolation performance. The indexes of the root-mean-square (r.m.s) accelerations of the vertical seat direction, pitching seat angle, and rolling seat angle are used as the objective functions. The study results indicate that the dynamic parameters of the PVS, HPS, and NSES greatly affect the driver’s ride comfort while their geometrical dimensions insignificantly affect the driver’s ride comfort. With the dynamic parameters of the PVS, HPS, and NSES optimized, the r.m.s seat acceleration in the vertical direction with the NSES is strongly reduced by 74.0% in comparison with the HPS; while the r.m.s accelerations of the pitching seat angle and rolling seat angle with the PVS are greatly decreased by 99.1% and 99.8% compared to the NSES. Therefore, the ride comfort of the driver’s seat is remarkably improved by using the NSES while the driver’s seat shaking is obviously ameliorated by using the PVS. To enhance the ride comfort and reduce the shaking of the driver’s seat, a combination of the PVS and NSES should be applied to the seat suspension of off-road vehicles.</div>

Rheology of Suspensions of Solid Particles in Liquids Thickened by Starch Nanoparticles

The rheology of suspensions of solid particles in aqueous matrix liquids thickened by starch nanoparticles (SNP) was investigated. The SNP concentration varied from 9.89 to 34.60 wt% based on the aqueous matrix phase. The solids concentration of suspensions varied from 0 to 47 wt% (0 to 56 vol%). The suspensions at any given SNP concentration were generally Newtonian at low solids concentrations. At high solids concentrations, the suspensions were non-Newtonian shear-thinning. With the increase in the SNP concentration, the suspensions become non-Newtonian at a lower solids concentration. The rheological behavior of non-Newtonian suspensions could be described adequately with a power-law model. The consistency index of the suspension increased with the increase in solids concentration of the suspension at any given SNP concentration. The flow behavior index of suspensions was well below unity at high solids concentrations, indicating non-Newtonian shear-thinning behavior. The value of the flow behavior index decreased with the increase in solids concentration indicating an enhancement of shear-thinning in suspensions. The experimental viscosity and consistency data for Newtonian and non-Newtonian suspensions showed good agreement with the predictions of the Pal viscosity model for suspensions.

Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension

In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system.

LQR force command planning–based sliding mode control for active suspension system

In this article, a linear quadratic regulator (LQR)-based sliding model control strategy was proposed for the commercial vehicle seat suspension system. First, the multiple degrees of freedom mechanical dynamics model of a quarter suspension system was built. Then, the LQR force command planner was designed based on mechanical dynamics to ensure driver comfort. Second, considering the mechanical dynamics augmented with hydraulic actuator dynamics, a proportional-integral sliding mode control strategy was developed to track the reference force, which was calculated by the LQR force command planner in real time. Taking the problems of noise disturbances and unavailable full states feedback into consideration, the Kalman filter and tracking differentiator were designed and integrated into the control algorithm. Finally, the quarter suspension AMESim model was built, and the proposed control strategy was implemented and verified in the AMESim-Matlab/Simulink co-simulation environment. Comprehensive simulations were carried out. The simulation results, both from time domain and frequency domain perspective, indicated that the seat ride comfort can be effectively improved with the proposed method.

Influence of the Suspension Model in the Simulation of the Vertical Vibration Behavior of the Railway Vehicle Car Body

The evaluation of the vibration behavior of railway vehicle car bodies based on the results of numerical simulations requires the adoption of an appropriate theoretical model of the suspension which considers the important factors that influence the vibration level of the car body. In this paper, the influence of the secondary suspension model on the vertical vibration behavior of the railway vehicle car body is investigated, based on the results of numerical simulations on the frequency response functions of the acceleration, the power spectral density of the acceleration and the root mean square of the acceleration of the car body. Numerical simulation applications are developed based on a rigid-flexible coupled vehicle model with seven degrees of freedom, corresponding to car body vibration modes: bounce, pitch, and first vertical bending mode, and bogie vibration modes: bounce and pitch. Four different models of secondary suspension are integrated into the vehicle model, namely a reference model and four analysis models. Analysis models include systems through which the pitch vibration of the bogies is transmitted to the car body, influencing its vibration behavior and, respectively, a system that takes the relative angular displacement between the car body and the bogie and a system that models the transmission system of the longitudinal forces between the bogie and the car body are analyzed. The effects of these two systems on the vibration behavior of the railway vehicle car body are analyzed both for each system separately and together. In the conclusions of the paper, the influence of the secondary suspension model on the vibration level at the resonance frequencies of the vertical bending of the car body and the pitch of the bogie is pointed out. It also highlights the important contribution of the transmission system of the longitudinal forces between the bogie and the car body in transmitting pitch vibrations of the bogies to the car body, with effects on the vibration level of the car body at high speeds.

Dynamic Model-Based Safety Margins for High-Density Matrix Headlight Systems

Real-time masking of vehicles in a dynamic road environment is a demanding task for adaptive driving beam systems of modern headlights. Next-generation high-density matrix headlights enable precise, high-resolution projections, while advanced driver assistance systems enable detection and tracking of objects with high update rates and low-latency estimation of the pose of the ego-vehicle. Accurate motion tracking and precise coverage of the masked vehicles are necessary to avoid glare while maintaining a high light throughput for good visibility. Safety margins are added around the mask to mitigate glare and flicker caused by the update rate and latency of the system. We provide a model to estimate the effects of spatial and temporal sampling on the safety margins for high-and low-density headlight resolutions and different update rates. The vertical motion of the ego-vehicle is simulated based on a dynamic model of a vehicle suspension system to model the impact of the motion-to-photon latency on the mask. Using our model, we evaluate the light throughput of an actual matrix headlight for the relevant corner cases of dynamic masking scenarios depending on pixel density, update rate, and system latency. We apply the masks provided by our model to a high beam light distribution to calculate the loss of luminous flux and compare the results to a light throughput approximation technique from the literature.

Mathematical modeling of spatial air suspension of a car with a combined damping system

A mathematical model of a spatial four-support air suspension of a car with a combined damping system, including a low-power hydraulic shock absorber, an inertial-friction shock absorber, and a dynamic damper, is presented. Keywords: air suspension, hydraulic shock absorber, inertia-friction shock absorber, dynamic absorber, combined damping system, car. nvv_60@mail.ru, avp24897@mail.ru, chda1991@yandex.ru

Coupling Suspension Force Regulator Considering Time-Varying Characteristic for a Bearingless Switched Reluctance Motor

The 12/14 bearingless switched reluctance motor (BSRM) was designed to restrain the coupling of torque current to suspension system. However, the robustness of the suspension system is reduced due to the time-varying current and displacement stiffness coefficients, thereby hindering its high reliable application in the on-board flywheel battery for vehicle. A coupling suspension force regulator (CSFR) that consists of a position decoupling suspension force model (PDSFM) and a dynamic coupling observer (DCO) is designed to solve the above-mentioned problem. The time-varying model of the suspension force is established based on the Maxwell stress method and finite element analysis, which is further decomposed into PDSFM and time-varying coupling suspension force component. The PDSFM is used for the feedback model of suspension system to construct a steady suspension system. The DCO is designed based on the PDSFM to capture the time-varying coupling suspension force component as an external disturbance and feed forward compensation to the suspension control system. The time-varying suspension system is effectively transformed into a time-invariant steady suspension system through the effective cooperation of the PDSFM and DCO. The validity of the CSFR is verified through simulations and experiments.