Road roughness estimation based on discrete Kalman filter with unknown input

Kang

Proceedings of the Institution of Mechanical Engineers, Part D:

et al. 2019

Self Cite

This study presents an improved discrete Kalman filter for simultaneously estimating both all state variables and the unknown road roughness input for a vehicle suspension control system that plays a key role in the ride quality and handling performance while driving the vehicle. The suspension system is influenced by the road roughness input, which causes undesirable vibrations associated with vehicle instability. It is therefore important to estimate the road roughness and state variables information when designing the model-based controller for the vehicle suspension control system associated with the vehicle’s vertical dynamics. However, the implementation of conventional estimation theories for the suspension control system is challenging because the road roughness acts as an unknown input and is difficult to be measured or estimated while driving. This study presents an improved Kalman filter with unknown input, which can simultaneously estimate the state variables and road roughness without any prior information about the vehicle suspension control system. The proposed road roughness input estimator is evaluated by using an in-vehicle test bed with a laser-type profilometer. Finally, the state estimation performance of the proposed estimator for a vehicle suspension control system is validated by using CarSim software.

Section: Design Of Road Roughness Estimatormentioning

confidence: 99%

Section: Experimental Validation Of Unknown Road Input Estimationmentioning

confidence: 99%

Section: Experimental Validation Of Unknown Road Input Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous estimation of state and unknown road roughness input for vehicle suspension control system based on discrete Kalman filter

Kang

Proceedings of the Institution of Mechanical Engineers, Part D:

et al. 2019

Self Cite

Journal of Intelligent Material Systems and Structures

“…where ω = 2 π f = 2 π / T = 2 π V / D , D (0.8 m) represents the width of the bump, V represents the constant vehicle velocity of 3.08 km/h ( V = 0 . 856 m / s ), and A m (0.065 m) represents the bump height. The second excitation signal was a random road profile, which was measured from a non-smooth road (Kang et al, 2019) as illustrated in Figure 4(b).…”

Section: Simulation Resultsmentioning

confidence: 99%

Explicit model predictive control of semi-active suspension systems with magneto-rheological dampers subject to input constraints

Ngoc

Yoon

Choi

et al. 2020

Self Cite

This article presents vibration control of a semi-active quarter-car suspension system equipped with a magneto-rheological damper that provides the physical constraint of a damping force. In this study, model predictive control was designed to handle the constraints of control input (i.e. the limited damping force). The explicit solution of model predictive control was computed using multi-parametric programming to reduce the computational time for real-time implementation and then adopted in the semi-active suspension system. The control performance of model predictive control was compared with that of a clipped linear-quadratic optimal controller, where the damping force was bound using a standard saturation function. Two types of road conditions (bump and random excitation) were applied to the suspension system, and the vibration control performance was evaluated through both simulations and experiments.

Intl J Robust & Nonlinear

Event‐based joint estimation for unknown inputs and states: A distributed recursive filtering method

Liu

Wei

et al. 2023

This article addresses the problem of distributed joint estimation for a class of discrete‐time time‐varying systems subject to random nonlinearities and unknown inputs over sensor networks. For the purpose of energy‐saving, the dynamic event‐triggering mechanism is adopted to govern the signal transmission between the sensor and the local estimator. First, some constraint conditions are introduced to decouple the unknown input to eliminate their impact. Then, by means of mathematical induction, an upper bound of the filtering error covariance is individually obtained for the state and the unknown input by solving coupled Riccati‐like difference equations. Subsequently, the matrix simplification method is adopted to tackle the sparsity problem caused by sensor networks. In addition, the required distributed estimator gains are acquired by minimizing the obtained upper bounds of filtering error covariances. Finally, a numerical simulation is given to illustrate the effectiveness of the proposed joint estimator design scheme.