Adaptive Model-Free Control With Nonsingular Terminal Sliding-Mode for Application to Robot Manipulators

Abstract

An adaptive model-free control with nonsingular terminal sliding-mode (AMC-NTSM) is proposed for high precision motion control of robot manipulators. The proposed AMC-NTSM employs onesample delayed measurements to cancel nonlinearities and uncertainties of manipulators and to subsequently obtain sufficiently simple models for easy control design. In order to maintain high gain controls even when the joint angles are close to the reference target values and accordingly achieve high precision and fast response control, a nonlinear sliding variable is also adopted instead of a linear one, asymptotically stabilizing controls by guaranteeing even a finite-time convergence. In addition, sliding variables are reflected on control inputs to support fast convergence while achieving uniform ultimate boundedness of tracking errors. The control gains of the proposed AMC-NTSM are adaptively adjusted over time according to the magnitude of the sliding variable. Such adaptive control gains become high for fast convergence or low for settling down to steady motion with better convergence precision, when necessary. The switching gains of the proposed AMC-NTSM are also adaptive to acceleration such that inherent time delay estimation (TDE) errors can be suppressed effectively regardless of their magnitudes. The simulation and experiment show that the proposed AMC-NTSM has good tracking performance. INDEX TERMS Adaptive control, time-delay control, time-delay estimation, nonsingular terminal slidingmode, robot manipulator.

Model-free adaptive control based on prescribed performance and time delay estimation for robotic manipulators subject to backlash hysteresis

“…The controller has fast convergence speed and strong robustness. 27 These strategies achieve good tracking accuracy and steady-state performance.…”

Robust Prescribed Trajectory Tracking Control of a Robot Manipulator Using Adaptive Finite-Time Sliding Mode and Extreme Learning Machine Method

“…From the system modeling perspective, the Lagrange method is commonly used to model rigid-link manipulators [6]. However, these multivariable systems are highly coupled and nonlinear, meaning that an accurate model representation is hardly obtained for such systems [7,8]. These issues can accommodate by designing a reliable controller that satisfies precision, fastresponse rate, robustness, and adaptiveness properties by which the uncertain dynamics and disturbances are handled properly.…”

A Robust Observer for Sensor Faults Estimation on n-DOF Manipulator in Constrained Framework Environment

“…As the highly coupled nonlinear characteristics, the actuator fault causes obvious effects on the system dynamics; therefore, they can be easily detected through monitoring abnormal system performances. Following the literature of actuator fault detection and isolation, and estimation, such fault can be considered as matched or mismatched disturbances and observed by using observers such as extended state observers (ESO) [15], [18], [19], disturbance observers (DO) [12], [20]- [22], uncertainty and disturbance estimator (UDE) [23], [24], time-delayed estimation (TDE) [25]- [27], super-twisting algorithm [28], Kalman estimators [29], unknown input observer [30], [31], etc. Then, Fault-tolerant control (FTC) that combines these observers compensation with advanced control algorithms such as active disturbance rejection control (ADRC) [32], [33], adaptive algorithms [34]- [36], or high robust gains can be employed to address the influence of the actuator fault.…”