Search

Summary Background Approximately 20% of traumatic cervical spinal cord injuries result in tetraplegia. Neuroprosthetics are being developed to manage this condition and thus improve the lives of patients. We aimed to test the feasibility of a semi-invasive technique that uses brain signals to drive an exoskeleton. Methods We recruited two participants at Clinatec research centre, associated with Grenoble University Hospital, Grenoble, France, into our ongoing clinical trial. Inclusion criteria were age 18–45 years, stability of neurological deficits, a need for additional mobility expressed by the patient, ambulatory or hospitalised monitoring, registration in the French social security system, and signed informed consent. The exclusion criteria were previous brain surgery, anticoagulant treatments, neuropsychological sequelae, depression, substance dependence or misuse, and contraindications to magnetoencephalography (MEG), EEG, or MRI. One participant was excluded because of a technical problem with the implants. The remaining participant was a 28-year-old man, who had tetraplegia following a C4–C5 spinal cord injury. Two bilateral wireless epidural recorders, each with 64 electrodes, were implanted over the upper limb sensorimotor areas of the brain. Epidural electrocorticographic (ECoG) signals were processed online by an adaptive decoding algorithm to send commands to effectors (virtual avatar or exoskeleton). Throughout the 24 months of the study, the patient did various mental tasks to progressively increase the number of degrees of freedom. Findings Between June 12, 2017, and July 21, 2019, the patient cortically controlled a programme that simulated walking and made bimanual, multi-joint, upper-limb movements with eight degrees of freedom during various reach-and-touch tasks and wrist rotations, using a virtual avatar at home (64·0% [SD 5·1] success) or an exoskeleton in the laboratory (70·9% [11·6] success). Compared with microelectrodes, epidural ECoG is semi-invasive and has similar efficiency. The decoding models were reusable for up to approximately 7 weeks without recalibration. Interpretation These results showed long-term (24-month) activation of a four-limb neuroprosthetic exoskeleton by a complete brain–machine interface system using continuous, online epidural ECoG to decode brain activity in a tetraplegic patient. Up to eight degrees of freedom could be simultaneously controlled using a unique model, which was reusable without recalibration for up to about 7 weeks. Funding French Atomic Energy Commission, French Ministry of Health, Edmond J Safra Philanthropic Foundation, Fondation Motrice, Fondation Nanosciences, Institut Carnot, Fonds de Dotation Clinatec.

The Sector Stability Theorem is an intuitive condition for the stability of feedback loops that unifies many lines of research including robust control, passivity theory, dissipativity theory, and integral quadratic constraints (IQC) theory. This theorem plays a central role in deriving a frequency-domain test for general sector bounds and is used to develop efficient numerical algorithms for checking and enforcing sector bounds on linear time-invariant (LTI) systems. The notion of relative and directional indices are also developed. Two application examples illustrate the potential of the tool and techniques discussed in the paper.

Soft Robotics and Variable Stiffness Actuation (VSA) are active research domains in robotics. The aim is to design human-friendly machines that can perform safely dexterous tasks close to or in contact with humans. Whereas most of current works focus on biomimetic VSA by using two antagonistic motors to simultaneously deal with motion control and stiffness adaptation, in this work, we present a control approach that takes advantage of the mechanical reversibility of series elastic actuators and the recent availability of structured H ∞ synthesis. The paper introduces a methodology for the design of a control law that allows to turn a force-sensorless actuator that includes a flexible link into a variable stiffness actuator where the stiffness can be set arbitrarily in some interval. With the proposed framework, it has been possible to accurately control the motion and the stiffness of a cable-screw actuator.

The goal of the CLINATEC® Brain Computer Interface (BCI) Project is to improve tetraplegic subjects' quality of life by allowing them to interact with their environment through the control of effectors, such as an exoskeleton. The BCI platform is based on a wireless 64-channel ElectroCorticoGram (ECoG) recording implant WIMAGINE®, designed for long-term clinical application, and a BCI software environment associated to a 4-limb exoskeleton EMY (Enhancing MobilitY). Innovative ECoG signal decoding algorithms will allow the control of the exoskeleton by the subject's brain activity. Currently, the whole BCI platform was tested in real-time in preclinical experiments carried out in nonhuman primates. In these experiments, the exoskeleton arm was controlled by means of the decoded neuronal activity.

The goal of the CLINATEC® Brain Computer Interface (BCI) Project is to improve tetraplegic subjects' quality of life by allowing them to interact with their environment through the control of effectors, such as an exoskeleton. The BCI platform is based on a wireless 64-channel ElectroCorticoGram (ECoG) recording implant WIMAGINE®, designed for long-term clinical application, and a BCI software environment associated to a 4-limb exoskeleton EMY (Enhancing MobilitY). Innovative ECoG signal decoding algorithms will allow the control of the exoskeleton by the subject's brain activity. Currently, the whole BCI platform was tested in real-time in preclinical experiments carried out in nonhuman primates. In these experiments, the exoskeleton arm was controlled by means of the decoded neuronal activity.

Motor torque control is a key feature in force centered robotic applications such as haptics, force amplification and telemanipulation with force feedback. Most of the actually applied control schemes for this type of applications are based on 3 levels cascade control: high level force/torque loop, inner velocity loop, inner current loop. The two inner loops are respectively of P and PI type. The main goal of the two inner loops is to limit the effect of the counter-electromotive force and linearize the torque transfer by squeezing mechatronics nonlinearities. This paper presents another approach that explicitly deals with the nonlinearities and avoids the use of a velocity loop. This approach consists in adding a lag compensator to the PI current loop to enhance the torque control bandwidth, respectively the controllability of the system from the higher level controller viewpoint. The PI-Lag compensator parametrization based on fixed structure H ∞ synthesis is illustrated. In addition, the performances of the implemented control law are discussed in comparison with other approaches.

The present work focuses on the development of computational algorithms to determine on-line energy-based driving guidance for an Electric Vehicle (EV) endowed with regenerative breaking system capabilities. A predictive decision support system is designed to optimally distribute the energy flow between the instantaneous power demand requested by the driver for the powertrain engine and the different auxiliaries relating to comfort performance, such as the heating system. The proposed methodology uses an on-line iterative optimization process algorithm to search for global optimum relatively to specific objective functions, which take into account the battery autonomy, driving comfort indexes and the travel time. Our methodology has been validated for a heavy motorized quadricycle vehicle using Hardware In the Loop (HIL) simulations, for which the Energy Management System has been implemented in a DSP board communicating through a CAN protocol.