Dynamic Nonlinear Gradient Observer Based Extremum Seeking Control for Optimum Braking

Chaudhari, Sushant; Shendge, P. D.; Phadke, S. B.

doi:10.1109/tie.2021.3065612

Cited by 12 publications

(1 citation statement)

References 37 publications

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“…In recent years, some decent observer methods have been proposed for the active braking control system, such as high gain observers [21], disturbance observers [22], and inertial delay observer [23]. However, it is difficult to achieve a good compromise between the estimation accuracy and noise sensitivity for the above observer method.…”

Section: Introductionmentioning

confidence: 99%

Prescribed Performance Active Braking Control with Reference Adaptation for High-Speed Trains

et al. 2021

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Active braking control systems are vital for the safety of high-speed trains by leading the train operation at its maximum adhesion state. The train adhesion is a nonlinear function of the slip ratio and varies with the uncertain wheel-rail contact conditions. A nonlinear active braking control with rapid and accurate tracking performance is highly required for train braking systems. This paper proposes a novel prescribed performance active braking control with reference adaptation to obtain the maximum adhesion force. The developed feedback linearization controller employs a prescribed performance function that specifies the convergence rate, steady-state error, and maximum overshoot to ensure the transient and steady-state control performance. Furthermore, in the designed control approach, a continuous-time unscented Kalman filter is introduced to estimate the uncertainty of wheel-rail adhesion. The estimation is utilized to represent uncertainty and compensate for the prescribed performance control law. Finally, based on the estimated wheel-rail adhesion, an on-line optimal slip ratio generation algorithm is proposed for the adaptation of the reference wheel slip. The stability of the system is provided, and experiment results validate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%