Sliding mode nonlinear disturbance observer-based adaptive back-stepping control of a humanoid robotic dual manipulator

Abstract

SUMMARYAn adaptive back-stepping sliding mode controller (ABSMC) algorithm was developed for nonlinear uncertain systems based on a nonlinear disturbance observer (NDO). The developed ABSMC was applied to attitude control for the dual arm of a humanoid robot. Considering the system uncertainty and the unknown external disturbances, the ABSMC scheme was designed to eliminate the chattering phenomenon in the traditional sliding mode control and to reduce the tracking error closer to zero. The ABSMC algorithm solved problems related to the chattering of the system for both uncertainties and disturbances in the humanoid robotic system with an NDO in a two-dimensional environment. The algorithm was designed to work equally well with agents, with higher degrees of freedom in different applications. The method was appropriate for improving tracking performance. The ABSMC algorithm guaranteed global stability and improved the dynamic performance of the system. The algorithm inherited a low computational cost, probabilistic completeness, and asymptotic optimality from the fuzzy sliding mode control. This algorithm has a practical application in the dual arm of a humanoid robot with a circular trajectory. This paper showed the effectiveness and applicability of the proposed methods, which reduced the output of the controller and improved the control performance of the humanoid robotic system. The new combined control algorithm, ABSMC, was able to feasibly and efficiently weaken the chattering on the robot's closed-loop paths, starting and finishing at the same configuration.

Adaptive Backstepping Sliding Mode Control of the Hybrid Conveying Mechanism with Mismatched Disturbances via Nonlinear Disturbance Observers

“…is approach has been extensively employed in various robot control systems [17][18][19][20][21]. Ma et al have proposed a control law combining SMC and backstepping method for flexible-joint manipulator with mismatched disturbances [19].…”

“…Chen et al have combined SMC with backstepping to increase the system robustness against disturbances and uncertainty in the trajectory tracking issue of wheeled mobile robots [20]. In [21], an adaptive backstepping SMC scheme has been constructed for the electrohydraulic elastic manipulator with mismatched uncertainties, which improves the rigidity and antidisturbance capability of the system. However, the chattering phenomenon is the main drawback of the SMC, which has not been well studied in the abovementioned works.…”

Robust and Adaptive Backstepping Control for Hexacopter UAVs

“…In [48], a sliding mode observer based backstepping control was proposed for the hexacopter to estimate and compensate for the effect of wind parameters. In [49], an adaptive back-stepping sliding mode control was developed for the attitude control of a humanoid robot dual manipulator, and excessive chattering was avoided by introducing a nonlinear disturbance observer (NDO) to compensate for uncertainties. In [50], [51], neural networks(NN)-based observers were applied in the backstepping approach to approximate the uncertainties existing in the system dynamics.…”

“…(1) Compared with the neural networks (NN)-based observers [30]- [33], [50], [51] and the nonlinear ESO [59]- [62] requiring the multiple states information and complex nonlinear coefficients, the LESO employed in the control scheme is intuitively designed based on the state feedback, and the tuning operation is rather simple since linear observer parameters are utilized. (2) Rather than the nonlinear disturbances observers [45]- [49], [64]- [67] and other ESO [59]- [63] based on the position feedback, the 2 nd -order LESO with velocity feedback is developed. In practice, the velocity information is easily acquired, which is more accurate and reliable, especially for the multi-rotor UAVs.…”

Based on fuzzy-approximation adaptive backstepping control method for dual-arm of humanoid robot with trajectory tracking

“…24 –28 Iterative learning control can provide high precision for the uncertainty of important equipment in various applications. 29 –33 The advantage of iterative learning control is that it does not need knowledge of system dynamics. However, this will bring a lot of the initial error in early iterations, with the increase in the number of iterations, the convergence rate of the tracking error is very slow.…”