This paper proposes a new adaptive time- delay control (ATDC) scheme for cable-driven manipulat- ors. The proposed ATDC scheme contains three elements, i.e., time-delay estimation (TDE), injected dynamics and adaptive laws. The TDE utilizes intentionally time-delayed signals to estimate the unknown system dynamics and brings a fascinating model-free nature. The injected dyna- mics employ a fractional-order nonsingular terminal sliding mode (FONTSM) manifold and a fast-TSM-type reaching law, thus it can ensure fast dynamical response and high tracking accuracy in both sliding mode and reaching phases. The adaptive laws are utilized to obtain good robustness and effective suppression of the chatters simultaneously. Moreover, the continuous and chatter-free adaptive gains are used to enhance the control performan- ce under time-varying disturbances. The tracking error is proved to be uniformly ultimately bounded (UUB) using Lyapunov approach. Finally, the superiorities of the propo- sed ATDC scheme are demonstrated by three experiments using a cable-driven manipulator.

A portable electrical impedance tomography (EIT) system has been developed with Red Pitaya STEMlab for biomedical applications. The Red Pitaya STEMlab is a portable device to realize voltage generation and data acquisition for the EIT system. The EIT system includes a modified howland circuit as a voltage-controlled current source, a high-speed analogy multiplexer module, an 8-electrode array, and a personal computer. The generalized vector sampled pattern matching algorithm and the Tikhonov regularization algorithm are used to reconstruct the image generated by the EIT system. The reconstructed images by using Red Pitaya STEMlab are compared with a commercial impedance analyzer IM3570 within the frequencies of f = 100 KHz. The results show that the maximum difference of the image correlation between Red Pitaya STEMlab and IM3570 is 5.36%. Finally, the EIT system is used to image the conductivity of eggs during heating process. These results verified that the developed portable EIT system with Red Pitaya STEMlab could measure the biological tissue in a high accuracy at low cost.

In this paper, we propose a novel adaptive time-delay control (ATDC) for accurate trajectory tracking of cable-driven robots. The designed ATDC utilizes time- delay estimation (TDE) to estimate the lumped dynamics of the system, and provides an attractive model-free structure. Then, a robust control is designed for ATDC with fractional-order nonsingular terminal sliding mode (FONTSM) dynamics. Afterwards, a novel nonlinear adaptive law is proposed for the control gains to improve the control performance. Thanks to TDE and FONTSM dynamics, the proposed ATDC is model-free and highly accurate. Benefiting from the proposed nonlinear adaptive law, suppression of chattering issue and enhanced control performance have been obtained simultaneously. Stability is analyzed based on Lyapunov approach. Then, practical experiments have been performed to illustrate the advantages of our ATDC.

Exoskeleton robots are an exciting potential solution for patients with motor dysfunction to restore their daily activities. This letter introduces a variable stiffness exoskeleton robot (VSA-EXO) with variable stiffness actuators and a voluntary control strategy based on sEMG sensing. A sEMG-driven musculoskeletal model estimates joint torque and quasi-stiffness of the human joint through the sEMG signals of some muscles related to the joint. The voluntary control strategy adjusts the degree of assistance according to these detected voluntary efforts of the subjects, and transfers the stiffness adjustment of the human joints to the exoskeleton joints. Unlike the traditional assist-as-needed (ANN), this voluntary control strategy does not need to define a trajectory. Feasibility is demonstrated experimentally using three healthy subjects. Calibration experiments for the musculoskeletal model show that the NRMSE of the estimated torque and the actual torque of the three subjects are lower than 7.21%, proving the effectiveness of the musculoskeletal model. When using the exoskeleton assistance, with an assistance ratio of 0.3, the subjects’ effort is reduced by up to 25.32%.

Robotic exoskeleton assistance is an effective method for the treatment of patients with movement disorders. First, this article presents a variable stiffness knee exoskeleton robot, which can independently control stiffness and position and has a large stiffness range under low preload. Then, combined with the designed variable stiffness exoskeleton, a physical human–robot interaction (pHRI) control scheme based on joint torque estimation is proposed. Different from previous studies, this method uses the mechanical properties of the exoskeleton to achieve pHRI without complex control algorithms and force/torque sensors. Furthermore, the joint torque is estimated based on the surface electromyography signal and the Hill-based muscle model, and the exoskeleton can realize assist-as-needed function: when the subject trains with less effort, the exoskeleton maintains a high output physical impedance to provide high tracking accuracy; and when the subject trains with greater effort, the exoskeleton maintains a low output impedance to provide high physical compliance. Finally, we conducted experiments on three healthy subjects and two subjects with lower limb motor dysfunction to verify the effectiveness of the torque estimation method and the pHRI control scheme.