Active Compression on Unknown Disturbance and Uncertainty via Extended State Observer

Abstract

Extended state observer (ESO) is heavily limited by the unknown disturbance and its derivative, which requires high observing gains to decrease estimating error, resulting in serious noise sensitivity. To modify the disturbance estimation characteristics encountered by the observer, the active compression extended state observer (ACESO) is proposed in this study. The ACESO decreases the bound of residual lumped disturbance and its derivative by actively compressing the initial lumped disturbance, without relying on prior knowledge. The stability constraint and convergence results of ACESO are analyzed and compared with ESO theoretically. The results show that the ACESO mitigates the trade-off between noise sensitivity and high-gain observation. Benefiting from active compression, the ACESO has substantially less noise sensitivity than the ESO, while obtaining the same and even better estimating performance than the ESO. In addition, the nonlinear ACESO is discussed, which automatically balances the contradiction between estimation and convergence. Simulations and experiments demonstrate the effectiveness of the proposed methods.