Improved Nonlinear Extended State Observer-Based Sliding-Mode Rotary Control for the Rotation System of a Hydraulic Roofbolter

Abstract

This paper develops a sliding-mode control with an improved nonlinear extended state observer (SMC-INESO) for the rotation system of a hydraulic roofbolter with dead-zones, uncertain gain, and disturbances, with the purpose of improving tracking performance. Firstly, the rotation system is modeled to compensate for dead-zone nonlinearity. Then, we present an improved nonlinear extended state observer to estimate disturbances of the rotation system in real time. Moreover, a proportional-integral-differential sliding-mode surface is introduced and an improved sliding-mode reaching law is designed. Based on this, a sliding-mode control law is developed. In order to eliminate the influence of estimation error and uncertain gain, we design two adaptation laws based on the sliding-mode surface and the estimated states. Finally, the effectiveness of the proposed SMC-INESO is verified through comparative simulation studies.

A neural network sliding mode method for nonlinear motors

“…In Equation (), $$$ c={\left[{c}_1,{c}_2,\dots, {c}_{\left(n-1\right)},1\right]}^T $$$ satisfies Hurwitz's polynomial, namely, Equation () 22–24 $$$$ {p}^{\left(n-1\right)}+{c}_{n-1}{p}^{\left(n-2\right)}+\cdots +{c}_2p+{c}_1.$$…”

Input channel gain adaptive active disturbance rejection control based on robust adaptive finite‐time parameter estimation

“…Gao [24] proposes a linear active disturbance rejection controller (LADRC) containing the linear extended state observer (LESO) and the PD controller to simplify the parameter setting problem. In recent years, adaptive mechanisms [17,20], such as fuzzy logic [18], radial basis function (RBF) neural networks [19], sliding mode control [25], model reference adaptive (MRA) [26], have been widely introduced to online estimate and adjust the parameters of ESO and NLSEF.…”

“…To actively suppress disturbances and adapt to actuator faults, Zhang et al [18] presented a BPNNbased adaptive ESO (AESO) and fuzzy logic-based NLSEF. Zhang et al [25] proposed two adaptive laws to eliminate the influence of disturbance observation error and ICG uncertainty. To tackle the estimation error of ESO, Wang et al [30] created a sliding mode control integrated with active disturbance rejection (SMCDR).…”

“…Zhang et al. [25] proposed two adaptive laws to eliminate the influence of disturbance observation error and ICG uncertainty. To tackle the estimation error of ESO, Wang et al.…”

Extended State Observer Based-Backstepping Control for Virtual Synchronous Generator

“…Nevertheless, ESO was presented for tracking order states and dynamic errors in nonlinear systems with unknown disturbances [32][33][34][35][36][37]. An active power controller for smooth power tracking for a grid-connected VSG was established based on linear active disturbance rejection control to deal with power oscillations in [34].…”