Computational Efficiency-Based Adaptive Tracking Control for Robotic Manipulators with Unknown Input Bouc–Wen Hysteresis

Abstract

In order to maintain robotic manipulators at a high level of performance, their controllers should be able to address nonlinearities in the closed-loop system, such as input nonlinearities. Meanwhile, computational efficiency is also required for real-time implementation. In this paper, an unknown input Bouc–Wen hysteresis control problem is investigated for robotic manipulators using adaptive control and a dynamical gain-based approach. The dynamics of hysteresis are modeled as an additional control unit in the closed-loop system and are integrated with the robotic manipulators. Two adaptive parameters are developed for improving the computational efficiency of the proposed control scheme, based on which the outputs of robotic manipulators are driven to track desired trajectories. Lyapunov theory is adopted to prove the effectiveness of the proposed method. Moreover, the tracking error is improved from ultimately bounded to asymptotic tracking compared to most of the existing results. This is of important significance to improve the control quality of robotic manipulators with unknown input Bouc–Wen hysteresis. Numerical examples including fixed-point and trajectory controls are provided to show the validity of our method.

A survey of Bouc-Wen hysteretic models applied to piezo-actuated mechanical systems: Modeling, identification, and control

“…Fung et al (2009) proposed an adaptive backstepping fuzzy controller based on the classical BW model to enhance the positioning accuracy of a Scott-Russell micro-positioning stage. Xie et al (2019) proposed an adaptive controller to deal with the robotic manipulator's unknown BW hysteretic characteristic. Namadchian and Rouhani (2018) adopted adaptive neural control to accomplish a satisfied tracking performance in light of an unidentified BW model.…”

Control system of the six-axis serial manipulator based on active disturbance rejection control

“…5 At present, conventional control strategies for a multi-axis linkage manipulator include proportion-integration-differentiation (PID) control, neural network control (NNC), adaptive control, sliding mode control (SMC), and robust control. [6][7][8][9][10] However, because of the complex mechanical structure of the mechanical arm, different joint-connecting rod systems interact with one another and are highly coupled. The whole system presents nonlinear and strong coupling characteristics, which lead to difficulties for conventional control strategies to control the system quickly and precisely.…”

Quantization-Mitigation-Based Trajectory Control for Euler-Lagrange Systems with Unknown Actuator Dynamics

“…Lin et al [ 29 ] considered the cooperative navigation control of mobile robots in an unknown environment using a neural fuzzy controller. The work of [ 30 ] considered unknown input Bouc-Wen hysteresis control problem and handled it using adaptive control. Although advances have been reported, the majority of the existing works are not designed for Euler-Lagrange systems with unknown quantization.…”