Robust adaptive dual layer sliding mode controller: Methodology and application of uncertain robot manipulator

Abstract

This paper presents a novel robust adaptive dual layer sliding mode control (ADLSMC) for the problem of high accuracy tracking trajectory of robot manipulator in the presence of uncertainties and external disturbances. This new control scheme has a dual layer structure. The first layer drives the robot manipulator system reaches the global nonlinear sliding surface in finite time, and the second layer tackles the values of the two control gains overestimation problem. Moreover, compared with the traditional super-twisting with time delay estimation algorithm, the proposed controller can realize not only set-point tracking but also dynamic tracking, which is very widely used in practice. The stability of the closed–loop system and the finite time convergence are analyzed using Lyapunov techniques. The effectiveness of the proposed method is demonstrated by simulations and experimental studies.

Second-order adaptive robust control of proportional pressure-reducing valves using phenomenological model

“…In order to complement the strengths of these two methods, the adaptive robust control (ARC) proposed by Yao and Tomizuka (1997) provides a rigorous theoretic framework for the nonlinear system by considering the particular nonlinearities and model uncertainties. The ARC approach has been applied to many nonlinear control systems, such as electro-hydraulic servo systems (Feng and Yan, 2020; Guo et al, 2015; Zang et al, 2022) and robot manipulator (Arteaga-Pérez et al, 2020; Babaghasabha et al, 2015; Ma et al, 2022).…”

Fixed-time nonsingular terminal sliding mode control for trajectory tracking of uncertain robot manipulators

“…Despite advancements in robotics technology, accurately tracking trajectories remains a challenge for robot manipulators. To address this issue, various control methods have been proposed, including neural network control (Li et al, 2015), time delay control (Jin et al, 2017), feedback linearization (Shojaei et al, 2011), and sliding mode control (SMC) (Baek et al, 2016; Ma et al, 2022). Among which, SMC is particularly advantageous for uncertain nonlinear systems due to its strong robustness and fast transient response.…”

Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators

“…As a type of advanced mechatronic system, robot manipulators have attracted intensive research attention, especially for the control strategies (Anjum et al, 2021; Rahmani et al, 2020; Sun et al, 2019; Zhang et al, 2021). State-of-the-art control schemes such as sliding mode control (SMC), backstepping control, and soft computing methods have been widely used in the robot control field (Chen et al, 2019; Ma et al, 2022; Rad et al, 2020; Xu et al, 2014). However, with the increasing requirements of lightweight, flexibility, high tracking accuracy, and fast convergence, the design of control schemes has brought great importance to the disposal of adaptive capability with the modeling uncertainties, time-varying parametric, and extraneous disturbances under ambient working environment.…”