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Mingming Dong - One of the best experts on this subject based on the ideXlab platform.
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a novel nonlinear Road Profile classification approach for controllable suspension system simulation and experimental validation
Mechanical Systems and Signal Processing, 2019Co-Authors: Xiaolin Tang, Mingming Dong, Nong Zhang, Chuan HuAbstract:Abstract Driven by the increasing requirement for Road conditions in the field of the controllable suspension system, this paper presents a novel nonlinear Road-excitation classification procedure for arbitrary suspension control strategy. The proposed procedure includes four steps: the definition of controller parameters, selection of insensitive frequency ranges, calculation of superior features, and generation of the classifier. To better illustrate the proposed procedure, the clipped optimal control strategy is taken as an example in the simulation part. Simulation results reveal that the proposed method can accurately estimate Road excitation level for various controller parameters, vehicle speeds, and vehicle models. Three contributions have been made in this paper: (1) A Road classification procedure that can be used for Road adaptive suspension control with any control algorithm is developed; (2) In order to improve classification accuracy, the concept of insensitive index which is based on the time-frequency analysis is proposed; (3) Experimental validation with a quarter vehicle test rig is performed, which has verified the effectiveness of the proposed method for the adaptive controllable suspension system.
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Road Profile estimation for suspension system based on the minimum model error criterion combined with a kalman filter
Journal of Vibroengineering, 2017Co-Authors: Zhenfeng Wang, Mingming Dong, Yechen QinAbstract:This paper presents a novel approach for improving the estimation accuracy of the Road Profile for a vehicle suspension system. To meet the requirements of Road Profile estimation for Road management and reproduction of system excitation, previous studies can be divided into data-driven and model based approaches. These studies mainly focused on Road Profile estimation while seldom considering the uncertainty of parameters. However, uncertainty is unavoidable for various aspects of suspension system, e.g., varying sprung mass, damper and tire nonlinear performance. In this study, to improve the estimation accuracy for a varying sprung mass, a novel algorithm was derived based on the Minimum Model Error (MME) criterion and a Kalman Filter (KF). Since the MME criterion method utilizes the minimum value principle to solve the model error based on a model error function, the MME criterion can effectively deal with the estimation error. Then, the proposed algorithm was applied to a 2 degree-of-freedom (DOF) suspension system model under ISO Level-B, ISO Level-C and ISO Level-D Road excitations. Simulation results and experimental data obtained using a quarter-vehicle test rig revealed that the proposed approach achieves higher Road estimation accuracy compared to traditional KF methods.
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Road Profile estimation for semi active suspension using an adaptive kalman filter and an adaptive super twisting observer
Advances in Computing and Communications, 2017Co-Authors: Yechen Qin, Zhenfeng Wang, Changle Xiang, Reza Langari, Mingming DongAbstract:A novel Road estimation method using an adaptive Kalman filter and an adaptive super-twisting observer (AKF-ASTO) is presented, which can meet the requirements for Road excitation information of advanced suspension system. A Kalman filter is utilized to estimate the velocity of unsprung mass and control force, and the covariance matrixes of both process noise and measurement noise are adaptively tuned by a novel Road classifier. The estimated variable and control force are then processed by an adaptive super-twisting observer to reconstruct the Road Profile and the convergence of the ASTO is ensured by a Lyapunov analysis. Simulation results for a quarter vehicle model show that AKF-ASTO can estimate both the Road Profile and the system states with higher accuracy compared to the existing method. The proposed method can be used for the varying International Standardization Organization (ISO) Road levels, solely requiring the measurement of the accelerations of the sprung and unsprung masses.
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adaptive hybrid control of vehicle semiactive suspension based on Road Profile estimation
Shock and Vibration, 2015Co-Authors: Yechen Qin, Reza Langari, Mingming Dong, Jifu GuanAbstract:A new Road estimation based suspension hybrid control strategy is proposed. Its aim is to adaptively change control gains to improve both ride comfort and Road handling with the constraint of rattle space. To achieve this, analytical expressions for ride comfort, Road handling, and rattle space with respect to Road input are derived based on the hybrid control, and the problem is transformed into a MOOP (Multiobjective Optimization Problem) and has been solved by NSGA-II (Nondominated Sorting Genetic Algorithm-II). A new Road estimation and classification method, which is based on ANFIS (Adaptive Neurofuzzy Inference System) and wavelet transforms, is then presented as a means of detecting the Road Profile level, and a Kalman filter is designed for observing unknown states. The results of simulations conducted with random Road excitation show that the efficiency of the proposed control strategy compares favourably to that of a passive system.
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Road Profile classification for vehicle semi-active suspension system based on Adaptive Neuro-Fuzzy Inference System
2015 54th IEEE Conference on Decision and Control (CDC), 2015Co-Authors: Mingming Dong, Reza Langari, Feng Zhao, Liang GuAbstract:To meet the requirements of excitation information for semi-active suspension control, a new Road classification method with application of Adaptive Neuro-Fuzzy Inference System (ANFIS) was presented. Due to distinct system responses for different Road levels, the sprung mass acceleration signal was utilized for classification. To analyze the properties of various Road inputs from different perspectives, the acceleration signal was first decomposed into 5 categories via wavelet transform, and 11 statistic features were calculated for each category. Then, an improved distance evaluation technique was applied to remove irrelevant features. With the extracted superior features, a new 2-layers ANFIS classifier was implemented to calculate overall Road level. Simulation results revealed that the proposed classifier had significantly improved performance compared to all 1-layer ANFIS classifiers for individual category, and can accurately classify Road level with negligible time delay.
Luc Dugard - One of the best experts on this subject based on the ideXlab platform.
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lpv force observer design and experimental validation from a dynamical semi active er damper model
IFAC-PapersOnLine, 2019Co-Authors: Thanh-phong Pham, Olivier Sename, Luc DugardAbstract:Abstract This paper presents an LPV damping force observer of Electro Rheological (ER) dampers for a real automotive suspension system, taking the dynamic characteristic of damper into account. First, an extended nonlinear quarter-car model is considered, where the time constant representing the damper dynamics is varying according to the control level. This is rewritten as an LPV model which is used to design an LPV observer. The objective of the LPV observer is to minimize the effects of bounded unknown input disturbances (unknown Road Profile and measurement noises) on the state estimation errors through an H∞ criterion, while the damper nonlinearity is bounded using a Lipschitz condition. Two low-cost accelerometers (the sprung mass and the unsprung mass accelerations) are used as inputs for the proposed methodology only. To experimentally assess the proposed approach, the observer is implemented on the 1/5-scaled real vehicle-INOVE testbench of GIPSA-lab. Experiments shows the ability of the observer to estimate the damper force in real-time, face to unknown inputs disturbance and sensor noises.
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design and experimental validation of an h observer for vehicle damper force estimation
IFAC-PapersOnLine, 2019Co-Authors: Thanh-phong Pham, Olivier Sename, Luc DugardAbstract:Abstract The real-time estimation of damper force is crucial for control and diagnosis of suspension systems in Road vehicles. In this study, we consider a semi-active electrorheological (ER) suspension system. First, a nonlinear quarter-car model is proposed that takes the nonlinear and dynamical characteristics of the semi-active damper into account. The estimation of the damper force is developed through an H∞ observer whose objectives are to minimize the effects of bounded unknown Road Profile disturbances and measurement noises on the estimation errors of the state variables and nonlinearity through a Lipschitz assumption. The considered measured variables, used as inputs for the observer design, are the two accelerometers data from the sprung mass and the unsprung mass of the quarter-car system, respectively. Finally, the observer performances are assessed experimentally using the INOVE platform from GIPSA-lab (1/5-scaled real vehicle). Both simulation and experimental results emphasize the robustness of the estimation method against measurement noises and Road disturbances, showing the effectiveness in the ability of estimating the damper force in real-time.
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Road Profile estimation using an adaptive youla kucera parametric observer comparison to real Profilers
Control Engineering Practice, 2017Co-Authors: Moustapha Doumiati, Luc Dugard, Olivier Sename, John J Martinez, Daniel LechnerAbstract:Abstract Road Profile acts as a disturbance input to the vehicle dynamics and results in undesirable vibrations affecting the vehicle stability. An accurate knowledge of this data is a key for a better understanding of the vehicle dynamics behavior and active vehicle control systems design. However, direct measurements of the Road Profile are not trivial for technical and economical reasons, and thus alternative solutions are needed. This paper develops a novel observer, known as a virtual sensor, suitable for real-time estimation of the Road Profile. The developed approach is built on a quarter-car model and uses measurements of the vehicle body. The Road roughness is modeled as a sinusoidal disturbance signal acting on the vehicle system. Since this signal has unknown and time-varying characteristics, the proposed estimation method implements an adaptive control scheme based on the internal model principle and on the use of Youla–Kucera (YK) parameterization technique (also known as Q-parameterization). For performances assessment, estimations are comparatively evaluated with respect to measurements issued from Longitudinal Profile Analyzer (LPA) and Inertial Profiler (IP) instruments during experimental trials. The proposed method is also compared to the approach provided in Doumiati, Victorino, Charara, and Lechner (2011) , where a stochastic Kalman filter is applied assuming a linear Road model. Results show the effectiveness and pertinence of the present observation scheme.
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Adaptive Road Profile Estimation in Semiactive Car Suspensions
IEEE Transactions on Control Systems Technology, 2015Co-Authors: Juan C. Tudon-martinez, Soheib Fergani, John Jairo Martinez, Ruben Morales-menendez, Olivier Sename, Luc DugardAbstract:The enhancement of passengers' comfort and their safety are part of the\nconstant concerns for car manufacturers. Semiactive damping control\nsystems have emerged to adapt the suspension features, where the Road\nProfile is one of the most important factors determining the automotive\nvehicle performance. Because direct measurements of the Road Profile\nrepresent expensive solutions and are susceptible to contamination (e.g.\nusing laser and other visual sensors), this paper proposes a novel Road\nProfile estimator that offers the essential information (Road roughness\nand its frequency) for the adjustment of the vehicle dynamics using\nconventional sensors, such as accelerometers or displacement/velocity\nsensors easy to mount, cheap, and useful to estimate all suspension\nvariables. Based on the Q-parametrization approach, an adaptive observer\nestimates the dynamic Road signal; afterward, a Fourier analysis is used\nto compute the Road roughness condition online and to perform an\nInternational Organization for Standardization (ISO) 8608\nclassification. Experimental results on the rear-left corner of a 1: 5\nscale vehicle, equipped with electro-rheological (ER) dampers, have been\nused to validate the proposed Road Profile estimation method. Different\nISO Road classes evaluate the performance of the proposed algorithm,\nwhose results show that any Road can be identified successfully at least\n70% of the time with a false alarm rate lower than 5%; the general\naccuracy of the Road classifier is 95%. A second test with variable\nvehicle velocity shows the importance of the online frequency estimation\nto adapt the Road estimation algorithm to any driving velocity; in this\ntest, the Road is correctly estimated in 868 of 1042 m (an error of\n16.7%). Finally, the adaptability of the parametric Road estimator to\nthe semiactiveness property of the ER damper is tested at different\ndamping coefficients.
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adaptive control scheme for Road Profile estimation application to vehicle dynamics
IFAC Proceedings Volumes, 2014Co-Authors: Moustapha Doumiati, John Jairo Martinez, Olivier Sename, Sebastian Erhart, Luc DugardAbstract:Road Profile is considered as an essential input that affects the vehicle dynamics. An accurate information of this data is fundamental for a better understanding of the vehicle behavior and vehicle control systems design. The present paper presents a novel algorithm (observer) suitable for real-time estimation of vertical Road Profile. The developed approach is based on a quarter-car model, and on elementary measurements delivered by potentially integrable sensors. The Road elevation is modeled as a sinusoidal disturbance signal affecting the vehicle system. Since this signal has unknown and time-varying characteristics, the proposed estimation method implements an adaptive control scheme based on the internal model principle and on the use of the Youla-Kucera parametrization technique (also known as Q-parametrization). For performances assessment, estimations are comparatively evaluated with respect to measurements issued from the LPA (Longitudinal Profile Analyzer) Profiler during experimental trials. Further, this new method is compared to the approach provided in (Doumiati et al. (2011)), where a Kalman filter is applied assuming a linear Road model. Results show the validity and efficiency of the present observer scheme.
Liang Gu - One of the best experts on this subject based on the ideXlab platform.
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Road Profile classification for vehicle semi-active suspension system based on Adaptive Neuro-Fuzzy Inference System
2015 54th IEEE Conference on Decision and Control (CDC), 2015Co-Authors: Mingming Dong, Reza Langari, Feng Zhao, Liang GuAbstract:To meet the requirements of excitation information for semi-active suspension control, a new Road classification method with application of Adaptive Neuro-Fuzzy Inference System (ANFIS) was presented. Due to distinct system responses for different Road levels, the sprung mass acceleration signal was utilized for classification. To analyze the properties of various Road inputs from different perspectives, the acceleration signal was first decomposed into 5 categories via wavelet transform, and 11 statistic features were calculated for each category. Then, an improved distance evaluation technique was applied to remove irrelevant features. With the extracted superior features, a new 2-layers ANFIS classifier was implemented to calculate overall Road level. Simulation results revealed that the proposed classifier had significantly improved performance compared to all 1-layer ANFIS classifiers for individual category, and can accurately classify Road level with negligible time delay.
Yves Delanne - One of the best experts on this subject based on the ideXlab platform.
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second order sliding mode observer for estimation of Road Profile
International Workshop on Variable Structure Systems, 2006Co-Authors: Abdelhamid Rabhi, Nk Msirdi, Leonid Fridman, Yves DelanneAbstract:This paper deals with an approach to estimate the Road Profile, by use of second order sliding mode observer. The method is based on a robust observer designed with a nominal dynamic model of vehicle. The estimation accuracy of observer has been validated experimentally using a trailer equiped with position sensors and accelerometers
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Road Profile input estimation in vehicle dynamics simulation
Vehicle System Dynamics, 2006Co-Authors: Hocine Imine, Yves Delanne, Nk MsirdiAbstract:Vehicle motion simulation accuracy, such as in accident reconstruction or vehicle controllability analysis on real Roads, can be obtained only if valid Road Profile and tire–Road friction models are available. Regarding Road Profiles, a new method based on sliding mode observers has been developed and is compared with two inertial methods. Experimental results are shown and discussed to evaluate the robustness of our approach.
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Road Profile inputs for evaluation of the loads on the wheels
Vehicle System Dynamics, 2005Co-Authors: Hocine Imine, Yves Delanne, Nk MsirdiAbstract:This paper presents a method for estimating the loads on the wheels using Road Profiles. Regarding Road Profiles, a new method based on sliding mode observers has been developed and is compared with longitudinal Profile analyser measurements. Experimental results are shown and discussed to evaluate the robustness of our approach.
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adaptive observers and estimation of the Road Profile
SAE transactions, 2003Co-Authors: Hocine Imine, Nk Msirdi, Yves DelanneAbstract:In this paper, we present an adaptive observer to estimate the unknown parameters of a vehicle. The system unknown inputs, representing the Road Profile variations, are estimated using sliding mode observers. First, we present some results related to the validation of a full car modelization, by means of comparisons between simulations results and experimental measurements (coming from a Peugeot 406 as a test car). Because, we don’t know exactly pneumatic parameters and because these parameters can be changed, an other sliding mode observer is developed to estimate the longitudinal forces (which depend on these parameters) acting on the wheels. The estimated Road Profile is compared to the measured one coming from the LPA (longitudinal Profile analyser ) in order to test the robustness of our approach.
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advantages and limits of different Road roughness Profile signal processing procedures applied in europe
Transportation Research Record, 2001Co-Authors: Yves Delanne, Paulo A A PereiraAbstract:Three Road Profile signal-processing methods that give parameters or indices used for the detection of Road unevenness in Europe are compared: international roughness index (IRI) analysis, power spectral density analysis, and constant percent bandwidth spectrum analysis. This comparison is carried out with Road rideability prediction models, car vibration prediction models, and careful diagnosis and definition of rehabilitation work. The IRI meets the profiling needs with regard to the assessment of Road condition, the Road serviceability level, and the setting of priorities for planning for Road maintenance and repair. This index is not suitable for specification of rehabilitation work or for diagnosis of possible problems with Road sections. Power spectral density (PSD) analysis by the International Organization for Standardization's Standard 8608 is a suitable method with regard to the evaluation of evenness level only if, on the basis of the averaging length, the Road Profile can be considered a stationary stochastic process and if a single regression line can be fitted. PSD is valuable for the detection of periodic defects in the Road Profile. Constant percent bandwidth spectrum analysis of consecutive segments along the section under investigation is well adapted to both the Road serviceability evaluation and the diagnosis of possible problems with Road sections.
Yechen Qin - One of the best experts on this subject based on the ideXlab platform.
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Speed independent Road classification strategy based on vehicle response: Theory and experimental validation
Mechanical Systems and Signal Processing, 2019Co-Authors: Yechen Qin, Zhenfeng Wang, Changle Xiang, Ehsan Hashemi, Amir Khajepour, Yanjun HuangAbstract:Abstract This paper presents a speed-independent Road classification strategy (SIRCS) based on sole measurement of unsprung mass acceleration. The new method provides an easy yet accurate classification methodology. To this purpose, a classification framework with two phases named off-line and online is proposed. In the off-line phase, the transfer function from unsprung mass acceleration to the Road excitation is firstly formulated, and a random forest-based frequency domain classifier is then generated according to the standard Road definition of ISO 8608. In the online phase, unsprung mass acceleration and vehicle velocity are firstly combined to calculate the equivalent Road Profile in the spatial domain, and then a two-step Road classifier attributes the Road excitation to a certain level based on the power spectral density (PSD) of the equivalent Road Profile. Simulations are carried out for different classification intervals, varying velocity, system uncertainties and measurement noises. Road experiments are finally performed in a production vehicle to validate the proposed SIRCS. Measurement of only unsprung mass acceleration to identify Road classification and less rely on the training data are the major contributions of the proposed strategy.
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Road Profile estimation for suspension system based on the minimum model error criterion combined with a kalman filter
Journal of Vibroengineering, 2017Co-Authors: Zhenfeng Wang, Mingming Dong, Yechen QinAbstract:This paper presents a novel approach for improving the estimation accuracy of the Road Profile for a vehicle suspension system. To meet the requirements of Road Profile estimation for Road management and reproduction of system excitation, previous studies can be divided into data-driven and model based approaches. These studies mainly focused on Road Profile estimation while seldom considering the uncertainty of parameters. However, uncertainty is unavoidable for various aspects of suspension system, e.g., varying sprung mass, damper and tire nonlinear performance. In this study, to improve the estimation accuracy for a varying sprung mass, a novel algorithm was derived based on the Minimum Model Error (MME) criterion and a Kalman Filter (KF). Since the MME criterion method utilizes the minimum value principle to solve the model error based on a model error function, the MME criterion can effectively deal with the estimation error. Then, the proposed algorithm was applied to a 2 degree-of-freedom (DOF) suspension system model under ISO Level-B, ISO Level-C and ISO Level-D Road excitations. Simulation results and experimental data obtained using a quarter-vehicle test rig revealed that the proposed approach achieves higher Road estimation accuracy compared to traditional KF methods.
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Road Profile estimation for semi active suspension using an adaptive kalman filter and an adaptive super twisting observer
Advances in Computing and Communications, 2017Co-Authors: Yechen Qin, Zhenfeng Wang, Changle Xiang, Reza Langari, Mingming DongAbstract:A novel Road estimation method using an adaptive Kalman filter and an adaptive super-twisting observer (AKF-ASTO) is presented, which can meet the requirements for Road excitation information of advanced suspension system. A Kalman filter is utilized to estimate the velocity of unsprung mass and control force, and the covariance matrixes of both process noise and measurement noise are adaptively tuned by a novel Road classifier. The estimated variable and control force are then processed by an adaptive super-twisting observer to reconstruct the Road Profile and the convergence of the ASTO is ensured by a Lyapunov analysis. Simulation results for a quarter vehicle model show that AKF-ASTO can estimate both the Road Profile and the system states with higher accuracy compared to the existing method. The proposed method can be used for the varying International Standardization Organization (ISO) Road levels, solely requiring the measurement of the accelerations of the sprung and unsprung masses.
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adaptive hybrid control of vehicle semiactive suspension based on Road Profile estimation
Shock and Vibration, 2015Co-Authors: Yechen Qin, Reza Langari, Mingming Dong, Jifu GuanAbstract:A new Road estimation based suspension hybrid control strategy is proposed. Its aim is to adaptively change control gains to improve both ride comfort and Road handling with the constraint of rattle space. To achieve this, analytical expressions for ride comfort, Road handling, and rattle space with respect to Road input are derived based on the hybrid control, and the problem is transformed into a MOOP (Multiobjective Optimization Problem) and has been solved by NSGA-II (Nondominated Sorting Genetic Algorithm-II). A new Road estimation and classification method, which is based on ANFIS (Adaptive Neurofuzzy Inference System) and wavelet transforms, is then presented as a means of detecting the Road Profile level, and a Kalman filter is designed for observing unknown states. The results of simulations conducted with random Road excitation show that the efficiency of the proposed control strategy compares favourably to that of a passive system.
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the use of vehicle dynamic response to estimate Road Profile input in time domain
Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization;, 2014Co-Authors: Yechen Qin, Reza LangariAbstract:A new method for Road Profile estimation in time domain with the application of vehicle system response was presented in this paper, and the problem was transformed as a system identification issue for an inverse nonlinear quarter vehicle model. Firstly, the inverse vehicle dynamic model was trained with specifically chosen white noise signal, and then eight different types of membership functions (MF) for Adaptive Neuro Fuzzy Inference System (ANFIS) were compared. Finally, the comparison of three different methods: ANFIS, Recursive Least Square (RLS) and Group Method of Data Handling (GMDH) were researched with different vehicle speeds and different Road levels in the simulation part. The results showed that ANFIS is better in comparison with RLS and GMDH and this method can be further applied for vehicle system analysis.Copyright © 2014 by ASME
