Cascade Extended State Observer for Active Disturbance Rejection Control Applications under Measurement Noise

Abstract

The extended state observer (ESO) plays an important role in the design of feedback control for nonlinear systems. However, its high-gain nature creates a challenge in engineering practice in cases where the output measurement is corrupted by non-negligible, high-frequency noise. The presence of such noise puts a constraint on how high the observer gains can be, which forces a trade-off between fast convergence of state estimates and quality of control task realization. In this work, a new observer design is proposed to improve the estimation performance in the presence of noise. In particular, a unique cascade combination of ESOs is developed, which is capable of fast and accurate signals reconstruction, while avoiding over-amplification of the measurement noise. The effectiveness of the introduced observer structure is verified here while working as a part of an active disturbance rejection control (ADRC) scheme. The conducted numerical validation and theoretical analysis of the new observer structure show improvement over standard solution in terms of noise attenuation.

44 pages, 9 figures, 4 tables

References in corpus (4)

• Half-Gain Tuning for Active Disturbance Rejection Control
• Robust output-feedback VFO-ADR control of underactuated spatial vehicles in the task of following non-parametrized paths
• The necessary and sufficient condition for the uncertain control gain in active disturbance rejection control
• ESO architectures in the trajectory tracking ADR controller for a mechanical system: a comparison

Cited by in corpus (4)

• Active Disturbance Rejection Control Design with Suppression of Sensor Noise Effects in Application to DC-DC Buck Power Converter
• Robust converter-fed motor control based on active rejection of multiple disturbances
• Parallel Multi-Extended State Observers based {ADRC} with Application to High-Speed Precision Motion Stage
• Tuning of extended state observer with neural network-based control performance assessment