Augmented Tank-Based Control Guarantees Passive Individual Interaction Environment for Multiuser Haptic-Enabled Robotic Systems
Cui Wang, Yudong Liu, Chenyang Sun, Ping Li, Yi-Feng Chen, Mingjie Dong, Zhenhong Li, Lu Liu, Mingming Zhang
AI summary
Problem
Scalable control in multiuser haptic systems fails when human operators exhibit nonpassive behaviors, as existing methods struggle with complex stability conditions and interaction coupling as user count grows. Current approaches either assume passive operators or require impractical impedance modeling, limiting real-world multiuser applications.
Approach
The authors isolate each operator’s workspace into an Individual Interaction Environment (IIE) to decouple control design, then apply an augmented tank-based controller that uses real-time energy regulation and adaptive gains to neutralize passivity violations without degrading haptic feedback.
Key results
- Theoretical proof that partner assistance and variable impedance violate individual interaction environment passivity
- Novel augmented tank-based controller with energy-related power regulation and time-varying gain
- Experimental validation on a three-robot system demonstrating guaranteed passivity and high rendering accuracy across four scenarios
- Demonstrated scalability and robustness independent of operator count or exact impedance boundaries
Why it matters
Enables safe, scalable, and high-fidelity multiuser haptic collaboration for critical applications like surgical training, rehabilitation, and shared virtual environments.
Abstract
Despite extensive investigations into the multiuser haptic-enabled robotic system (M-Hers), achieving scalable control design in the presence of nonpassive human operators remains a key challenge. This is primarily due to the increasing complexity of stability conditions and interaction coupling as the number of operators grows. In this study, we address this challenge in two steps. First, we introduce the individual interaction environment (IIE) to isolate the passivity violations, which facilitates the inde- pendent control design for each human–robot subsystem, thereby enhancing the scalability with respect to the number of subsystems. Second, within the IIE framework, we identify passivity-violating components caused by partners’ active behaviors and propose a novel augmented tank-based controller (ATBC) to guarantee passive IIE while maintaining high rendering accuracy. Specifi- cally, the ATBC employs an energy-related power regulation strat- egy to enhance interaction safety and a time-varying control gain to mitigate the negative effects of power regulation on rendering Received 18 August 2025; revised 13 November 2025; accepted 9 December 2025. Date of publication 12 January 2026; date of current version 23 January 2026. This work was supported in part by the National Key R&D Program of China under Grant 2023YFF1205200, in part by the Shenzhen Medical Research Fund under Grant B2502031, in part by the National Natural Sci- ence Foundation of China under Grant 62222318, Grant 62273173, and Grant 62373010, in part by the Guangdong Major Project of Basic Research under Grant 2025B0303000003, in part by the Shenzhen Science and Technology Program under Grant JCYJ20240813094403005, and in part by the Research Grants Council of the Hong Kong Special Administrative Region of China under Project CityU/11210424. This article was recommended for publication by Associate Editor Elena D. Momi and Editor M. Schwager upon evaluation of the reviewers’ comments. (Corresponding author: Mingming Zhang.) Cui Wang is with the Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China, and also with the Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China (e-mail: 12250105@mail.sustech.edu.cn). Yudong Liu, Chenyang Sun, Yi-Feng Chen, and Mingming Zhang are with the Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shen- zhen 518055, China (e-mail: 12132635@mail.sustech.edu.cn; 12131149@mail. sustech.edu.cn; chenyf6@sustech.edu.cn; zhangmm@sustech.edu.cn). Ping Li is with the School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China (e- mail: lip3@sustech.edu.cn). Mingjie Dong is with the College of Mechanical and Energy Engi- neering, Beijing University of Technology, Beijing 100124, China (e-mail: dongmj@bjut.edu.cn). Zhenhong Li is with the School of Engineering, University of Manchester, 100124 Manchester, U.K. (e-mail: zhenhong.li@manchester.ac.uk). Lu Liu is with the Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, SAR, China (e-mail: luliu45@cityu.edu.hk). This article has supplementary downloadable material available at https://doi.org/10.1109/TRO.2026.3651680, provided by the authors. Digital Object Identifier 10.1109/TRO.2026.3651680 fidelity. We validated the proposed method through collaborative haptic tasks on a customized M-Hers composed of three robots in four different scenarios. Comparative studies demonstrate that our approach effectively ensures IIE passivity in the presence of active human behaviors, while ensuring high reproducibility and achieving a favorable balance between passivity and rendering accuracy.