Second order sliding-mode observer for estimation of vehicle dynamic parameters

M'Sirdi, Kouider; Rabhi, Abdelhamid; Fridman, Leonid; Dávila, Jorge; Delanne, Y

doi:10.1504/ijvd.2008.022576

Cited by 49 publications

(21 citation statements)

References 34 publications

(26 reference statements)

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“…Euler discretization (e.g., [14][15][16]), depends on the sampling interval, as pointed out in [11]. In addition, they are prone to overshoot during the convergence.…”

Section: Journal Of Control Science and Engineeringmentioning

confidence: 99%

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Parameter Selection Guidelines for a Parabolic Sliding Mode Filter Based on Frequency and Time Domain Characteristics

Jin

Kikuuwe

Yamamoto

2012

Journal of Control Science and Engineering

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This paper presents the results of quantitative performance evaluation of an authors' new parabolic sliding mode filter, which is for removing noise from signals in robotics and mechatronics applications, based on the frequency and time domain characteristics. Based on the evaluation results, the paper presents selection guidelines of two parameters of the filter. The evaluation results show that, in the frequency domain, the noise removing capability of the filter is almost the same as that of the second-order Butterworth low-pass filter (2-LPF), but its phase lag is smaller (maximum 150 degree) than that of 2-LPF (maximum 180 degree). Moreover, the filter produces smaller phase lag than a conventional parabolic sliding mode filter with appropriate selection of the parameters. In the time domain, the filter produces smaller overshoot than 2-LPF and the conventional one, while maintaining short transient time, by using an appropriately selected parameter. The presented parameter selection guidelines state that the values of the parameters should be chosen according to some estimated characteristics of the input and some desired characteristics of the output. The effectiveness of the filter and the presented guidelines is validated through numerical examples and their application to a closed-loop, force control of a robot manipulator.

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“…Euler discretization (e.g., [14][15][16]), depends on the sampling interval, as pointed out in [11]. In addition, they are prone to overshoot during the convergence.…”

Section: Journal Of Control Science and Engineeringmentioning

confidence: 99%

“…Thus, as a guideline for selecting F, in the case where the estimates of ω s , U s , ω n , U np , and G p are known in advance, the value of F should be simply set to satisfy (16). With such a value of F, filter (1a)-(1c) passes the signal component while attenuating the noise component down to an amplitude of U np .…”

Section: Parameter Selection Guidelinesmentioning

confidence: 99%

Parameter Selection Guidelines for a Parabolic Sliding Mode Filter Based on Frequency and Time Domain Characteristics

Jin

Kikuuwe

Yamamoto

2012

Journal of Control Science and Engineering

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“…Suggestions for the safety systems' control bandwidth can be concluded from the paper by M'sirdi et al [8], which describes an observer for estimation of vehicle dynamical parameters based on the angular wheel speed sensors of a standard hydraulic ABS. The vehicle state observer exploits a sampling frequency in the range of approximately 130 Hz to 461 Hz for a vehicle speed range between 50 km/h and 164 km/h.…”

Section: Introductionmentioning

confidence: 99%

Frequency and time domain characteristics of digital control of electric vehicle in-wheel drives

Jarzębowicz

Opaliński

2017

Archives of Electrical Engineering

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Abstract:In-wheel electric drives are promising as actuators in active safety systems of electric and hybrid vehicles. This new function requires dedicated control algorithms, making it essential to deliver models that reflect better the wheel-torque control dynamics of electric drives. The timing of digital control events, whose importance is stressed in current research, still lacks an analytical description allowing for modeling its influence on control system dynamics. In this paper, authors investigate and compare approaches to the analog and discrete analytical modeling of torque control loop in digitally controlled electric drive. Five different analytical models of stator current torque component control are compared to judge their accuracy in representing drive control dynamics related to the timing of digital control events. The Bode characteristics and stepresponse characteristics of the analytical models are then compared with those of a reference model for three commonly used cases of motor discrete control schemes. Finally, the applicability of the presented models is discussed.

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“…In these observers, finite time convergence can be theoretically realized in continuous-time analysis. However, in discrete-time implementation, the convergence accuracy depends on the sampling period, typically with finite difference [10,11]. In addition, a dynamic model is also required.…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time Sliding Mode Filter with Adaptive Gain

Jin

Wang

et al. 2016

Applied Sciences

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Abstract:In feedback control of mechatronic systems, sensor signals are usually noisy and uncertain because of measurement errors and environmental disturbances. Such uncertainty and noise of feedback signals may cause instability of the controlled systems. This paper presents a new model-free discrete-time sliding mode filter for effectively removing noise by balancing the tradeoff between the filtering smoothness and the suppression of delay. The presented filter is an extension of a sliding mode filter (Jin et al. Real-time quadratic sliding mode filter for removing noise. Adv. Robot., 2012) by including an adaptive gain, of which value is determined in a similar way to that of a first-order adaptive windowing filter (Janabi-Sharifi et al. Discrete-time adaptive windowing for velocity estimation. IEEE Trans. Control Syst. Technol., 2000). The effectiveness of the presented filter is validated through numerical examples and experiments.

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Second order sliding-mode observer for estimation of vehicle dynamic parameters

Cited by 49 publications

References 34 publications

Parameter Selection Guidelines for a Parabolic Sliding Mode Filter Based on Frequency and Time Domain Characteristics

Parameter Selection Guidelines for a Parabolic Sliding Mode Filter Based on Frequency and Time Domain Characteristics

Frequency and time domain characteristics of digital control of electric vehicle in-wheel drives

Discrete-Time Sliding Mode Filter with Adaptive Gain

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