Adaptive intelligent vision-based control of a flexible-link manipulator

• Published in: Electrical engineering 2023-10, Vol.105 (5), p.3263-3281
• Main Authors: Sahu, Umesh Kumar, Patra, Dipti, Subudhi, Bidyadhar
• Format: Article
• Language: English
• Subjects: Adaptive control; Controllers; Decision making; Economics and Management; Electrical Engineering; Electrical Machines and Networks; Energy Policy; Engineering; Field of view; Flexible manipulators; Original Paper; Position measurement; Power Electronics; Robot arms; Robots; Robust control; Servocontrol; Space robots; Tracking control; Vision; Visual control; Visual fields
• ISSN: 0948-7921; 1432-0487
• DOI: 10.1007/s00202-023-01875-7

Present space robots such as planetary robots and flexible robots have structural flexibility in their arms and joints that leads to an error in the tip positioning owing to tip deflection. The flexible-link manipulator (FLM) is a non-collocated system that has unstable and inaccurate system performance. Thus, tip-tracking of FLM possesses difficult control challenges. The purpose of this study is to design adaptive intelligent tip-tracking control strategy for FLMs to deal with this control challenges of FLM. A vision sensor is utilized in conjunction with a traditional mechanical sensor to directly measure tip-position in order to address the aforementioned problem. Image-based visual servoing (IBVS), one of several visual servoing control techniques, is more efficient. However, the IBVS scheme faces numerous difficulties that impair the system’s performance in real-time applications, including singularities in the interaction matrix, local minima in trajectory, visibility issues. To address the issues with the IBVS scheme, a novel adaptive intelligent IBVS (AI-IBVS) controller for tip-tracking control of a two-link flexible manipulator (TLFM) is designed in this study. In particular, this paper addresses the IBVS issues along-with retention of visual features in the field-of-view (FOV). First, in order to retain object within the camera FOV, an intelligent controller with off-policy reinforcement learning (RL) is proposed. Second, a composite controller for TLFM is developed to combine RL controller and IBVS controller. The simulation has been conducted to examine the effectiveness and robustness of the proposed controller. The obtained results show that the AI-IBVS controller developed here possesses the capabilities of self-learning and decision-making for robust tip-tracking control of TLFM. Further, a comparison with other similar approach is presented.