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Yang J, Sun T. Finite-Time Interactive Control of Robots with Multiple Interaction Modes. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22103668. [PMID: 35632080 PMCID: PMC9147656 DOI: 10.3390/s22103668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/08/2022] [Accepted: 05/10/2022] [Indexed: 05/14/2023]
Abstract
This paper proposes a finite-time multi-modal robotic control strategy for physical human-robot interaction. The proposed multi-modal controller consists of a modified super-twisting-based finite-time control term that is designed in each interaction mode and a continuity-guaranteed control term. The finite-time control term guarantees finite-time achievement of the desired impedance dynamics in active interaction mode (AIM), makes the tracking error of the reference trajectory converge to zero in finite time in passive interaction mode (PIM), and also guarantees robotic motion stop in finite time in safety-stop mode (SSM). Meanwhile, the continuity-guaranteed control term guarantees control input continuity and steady interaction modes transition. The finite-time closed-loop control stability and the control effectiveness is validated by Lyapunov-based theoretical analysis and simulations on a robot manipulator.
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Tang J, Xu M, Han J, Liu M, Dai T, Chen S, Ming D. Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling. SENSORS 2020; 20:s20154186. [PMID: 32731432 PMCID: PMC7435370 DOI: 10.3390/s20154186] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 07/23/2020] [Accepted: 07/25/2020] [Indexed: 02/03/2023]
Abstract
The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.
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Affiliation(s)
- Jiabei Tang
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
| | - Minpeng Xu
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
| | - Jin Han
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
| | - Miao Liu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
| | - Tingfei Dai
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
| | - Shanguang Chen
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100094, China
| | - Dong Ming
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; (J.T.); (M.X.); (J.H.); (T.D.); (S.C.)
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
- Correspondence:
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