Nonlinear extended state observer-based output feedback stabilization control for uncertain nonlinear half-car active suspension systems

Du, Miaomiao; Zhao, Ding; Yang, Mengke; Chen, Hao

doi:10.1007/s11071-020-05638-y

Cited by 20 publications

(8 citation statements)

References 47 publications

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“…x and n. However, both systems are different since they assume different structures and the fact that the class of systems (10) includes systems (9) will be detailed later. Let us now focus on system (10). The NPHGO observer design will be performed under the following usual assumptions.…”

Section: The Problem Formulationmentioning

confidence: 99%

“…Before detailing this issue, it should be emphasized that in the case where system (9) is noise free, i.e. v(t) = 0, then it is easy to see that this system coincide with (10) where w(t) = 0 and hence an observer similar to (30) can be designed for system (9). Let us now focus on the case where v(t) = 0, i.e.…”

Section: Design Of a Npfhgomentioning

confidence: 99%

“…the system output is corrupted by the additive noise v(t). One shall show how system (9) can be augmented by a multiple integrator of the corrupted output leading to an augmented system the structure of which similar to that of system (10). As a result a SHGO similar to observer (24) but involving filtering capabilities can be designed.…”

Section: Design Of a Npfhgomentioning

confidence: 99%

“…An intensive research activity has been devoted to the observer design for uniformly observable systems over the last four decades (see for instance [1][2][3][4][5][6][7][8][9][10] and references therein). A particular emphasis has been put on the Standard High Gain Observer (SHGO) design for fundamental as well as simplicity purposes [5].…”

Section: Introductionmentioning

confidence: 99%

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Improved high gain observer design for a class of disturbed nonlinear systems

et al. 2021

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This paper provides a redesigned version of the Standard High Gain Observer (SHGO) to cope with the peaking phenomenon occurring during the transient periods as well as the sensitivity to high frequency measurement noise. The observer design is performed for a class of uniformly observable systems with noise free as well as noisy output measurements and the resulting observer is referred to as Non Peaking Filtered High Gain Observer (NPFHGO). The NPFHGO shares the same structure as its underlying SHGO and differs only by its corrective term which is still parameterized by a unique positive scalar up to an appropriate expression involving nested saturations. Of a fundamental interest, the power of the scalar parameter does not exceed one unlike in the case of the SHGO where this power grows from 1 to the system dimension. Moreover, it is shown that the equations of the NPFHGO become identical to those of the SHGO after a transient time horizon that can made arbitrarily small for sufficiently high values of the design parameter. A particular emphasis is put on the case of systems with noisy output measurements. It is shown how a multiple integrator of the corrupted outputs can be cascaded with the original system leading to an augmented system included in the class of systems for which the NPFHGO has been designed. The performance and main properties of the NPFHGO are highlighted and compared to those of its underlying SHGO through simulation results involving a single link robot arm system.

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Section: The Problem Formulationmentioning

confidence: 99%

Section: Design Of a Npfhgomentioning

confidence: 99%

Section: Design Of a Npfhgomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved high gain observer design for a class of disturbed nonlinear systems

et al. 2021

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“…The proposed algorithm also required less memory space compared with other methods, because the complex calculation process was operated offline. Du [29] proposed an output feedback control method for vehicle suspension systems that used a nonlinear extended state observer to estimated model uncertainties. The proposed approach could effectively estimate the nonlinear dynamics and mismatch disturbances such as sprung mass, unknown friction coefficient, and measurement noise caused by sensors.…”

Section: Introductionmentioning

confidence: 99%

Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle

Zhao

et al. 2020

Sensors

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Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

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