Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG) is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT) and linear discriminant analysis (LDA) classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patient's EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patient's motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery-based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.
Each wheel torque can be controlled independently, so four-wheel-drive electric vehicle can not only control the vehicle stability through hydraulic braking pressure regulation, but also through controlling the motor driving and braking force to generate yaw moment, which are different with the conventional vehicles. 4WD Evs have potential applications in control engineering. Both in-wheel motors and the EHB are actuators for vehicle stability control. In this paper, a vehicle co-simulation platform is constructed through the application of AMEsim and Simulink, additionally, a fuzzy controller is designed to generate yaw moment so as to compensate for deviations between CG slip angles and yaw rate. The simulation results show that the stability control system with motors and a mechanical load brake system can effectively improve the handling stability of the vehicle.
The most significant feature of EV with In-Wheel-Motor is that, the torque of each wheel can be controlled independently. [. When EVs turn, controlling torque of steering wheel independently can generate torque difference around the kingpin to reduce the driver's steering force and improve the steering Portability [. At first, the theory and structure of Driving Force Power Steering (DFPS) are discussed and a vehicle dynamic model is built with AMESim software. Based on the control strategy and algorithm a control model is built using Matlab/Simulink. At last, a simulation is performed. The results show that the DFPS system can provide steering power and assist steering efficiently.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.