Nonlinear state estimation for suspension control applications: a Takagi-Sugeno Kalman filtering approach

Pletschen, Nils; Diepold, Klaus

doi:10.1016/j.conengprac.2016.05.013

Cited by 23 publications

(26 citation statements)

References 40 publications

(50 reference statements)

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“…Also, the ISO level-B, ISO Level-C and ISO Level-D were calculated and used as the road excitation [29]. Note that it was assumed the tire did not lose contact with the ground [36][37][38][39][40].…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…These nonlinear behaviors are accounted for in the state-dependent damping nonlinearity ( ) and varying spring mass ( ). Since the estimation performance of the state observers for the vertical vehicle dynamics is particularly sensitive to deviations in the vehicle body mass [35,36], variations in the sprung mass are the main focus in this paper. The equation of motion for the quarter vehicle suspension sprung mass is ( ) = ( ) + ∆ ( ).…”

Section: Augmented Suspension System Modelmentioning

confidence: 99%

“…7. Second, an additional body load ∆ (∆ = 180 kg) was studied [36] under ISO level-C excitation in Fig. 8.…”

Section: Case 2: Iso Level-c Excitation Simulationmentioning

confidence: 99%

“…10. Second, an additional body load ∆ ( ∆ = 180 kg) was studied [36] under ISO level-D excitation in Fig. 11 The corresponding results of the KF and MME&KF are illustrated in Fig.…”

Section: Case 3: Iso Level-d Excitation Simulationmentioning

confidence: 99%

See 3 more Smart Citations

Road profile estimation for suspension system based on the minimum model error criterion combined with a Kalman filter

Wang¹,

Dong²,

Qin³

et al. 2017

J. vibroeng.

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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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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Augmented Suspension System Modelmentioning

confidence: 99%

“…7. Second, an additional body load ∆ (∆ = 180 kg) was studied [36] under ISO level-C excitation in Fig. 8.…”

Section: Case 2: Iso Level-c Excitation Simulationmentioning

confidence: 99%

“…10. Second, an additional body load ∆ ( ∆ = 180 kg) was studied [36] under ISO level-D excitation in Fig. 11 The corresponding results of the KF and MME&KF are illustrated in Fig.…”

Section: Case 3: Iso Level-d Excitation Simulationmentioning

confidence: 99%

See 2 more Smart Citations

Road profile estimation for suspension system based on the minimum model error criterion combined with a Kalman filter

Wang¹,

Dong²,

Qin³

et al. 2017

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…In many practical systems, the stochastic nonlinear functions are commonly encountered due to the unreliability of the communication network [18][19][20][21][22][23]. Hence, it is necessary to deal with the stochastic nonlinear functions to improve the accuracy of system state estimation.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filtering Algorithm for Systems with Stochastic Nonlinearity Functions, Finite-Step Correlated Noises, and Missing Measurements

You

Jiang

Huang

et al. 2018

Discrete Dynamics in Nature and Society

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The locally optimal filter is designed for a class of discrete-time systems subject to stochastic nonlinearity functions, finite-step correlated noises, and missing measurements. The multiplicative noises are employed to describe the random disturbances in the system model. The phenomena of missing measurements occur in a random way and the missing probability is characterized by Bernoulli distributed random variables with known conditional probabilities. Based on the projection theory, a class of Kalman-type locally optimal filter is constructed and the filtering error covariance matrix is minimized in the sense of minimum mean square error principle. Also, by solving the recursive matrix equation, we can obtain the filter gain. Finally, two examples are provided: one is a numerical example to illustrate the feasibility and effectiveness of the proposed filtering scheme; the other is to solve the problem of target estimation for a tracking system considering networked phenomena.

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Cascade Takagi–Sugeno fuzzy observer design for nonlinear uncertain systems with unknown inputs: A sliding mode approach

Nguyen

Delprat

2022

Intl J Robust & Nonlinear

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This article investigates the design of Takagi–Sugeno (TS) fuzzy model‐based observers for nonlinear systems with parametric uncertainties and unknown inputs. To address this challenging problem, two observers are constructed in cascade. Based on the sliding mode technique, the first observer allows to examine a new system whose both state and output equations are subject to uncertainties but without unknown inputs. The second Luenberger‐type observer is designed for the new system where the effects of uncertainties on the estimation error can be canceled. The TS fuzzy observer design is recast as optimization problems under linear matrix inequalities, which can be effectively solved using convex optimization technique. The new cascade observer structure enables a simultaneous estimation of the system states, the unknown inputs and the uncertainties of the original nonlinear system. The effectiveness and advantage of the proposed estimation method is demonstrated via two numerical examples including a nonlinear vehicle application.

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Nonlinear state estimation for suspension control applications: a Takagi-Sugeno Kalman filtering approach

Cited by 23 publications

References 40 publications

Road profile estimation for suspension system based on the minimum model error criterion combined with a Kalman filter

Road profile estimation for suspension system based on the minimum model error criterion combined with a Kalman filter

Kalman Filtering Algorithm for Systems with Stochastic Nonlinearity Functions, Finite-Step Correlated Noises, and Missing Measurements

Cascade Takagi–Sugeno fuzzy observer design for nonlinear uncertain systems with unknown inputs: A sliding mode approach

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