Optimization Analysis of the Structural Design and Stability Parameters of a Rehabilitation Robot

In this paper, a lower limb rehabilitation robot, suitable for stroke patients, is designed to meet the needs of the lower limb training in a later stage of rehabilitation. The rehabilitation robot is composed of a gantry structure, a driving system, a weight support system, and a human-computer interaction system. Such a robot can assist the patients to stand and walk on the ground. Because of the weakness of the lower limbs on the affected side, stroke patients find it difficult to maintain their own body balance. The patients may fall due to a change in body posture caused by insufficient body function. Therefore, it is necessary to evaluate the stability of the rehabilitation robot after being impacted by the patient\u27s fall during use. This paper presents a method for the analysis of robot stability and develops an approximate mathematical model of the rehabilitation robot stability based on the response surface method. Optimal structural design parameters for the rehabilitation robot under impact are determined based on the response surface mathematical model. Finally, a stability experiment of the rehabilitation robot under the optimal structural parameters is performed. The experimental results demonstrate that the universal wheel maintains a close force contact with the ground, which proves the reliable stability of the robot

Autonomous motion and control of lower limb exoskeleton rehabilitation robot

Introduction: The lower limb exoskeleton rehabilitation robot should perform gait planning based on the patient’s motor intention and training status and provide multimodal and robust control schemes in the control strategy to enhance patient participation.Methods: This paper proposes an adaptive particle swarm optimization admittance control algorithm (APSOAC), which adaptively optimizes the weights and learning factors of the PSO algorithm to avoid the problem of particle swarm falling into local optimal points. The proposed improved adaptive particle swarm algorithm adjusts the stiffness and damping parameters of the admittance control online to reduce the interaction force between the patient and the robot and adaptively plans the patient’s desired gait profile. In addition, this study proposes a dual RBF neural network adaptive sliding mode controller (DRNNASMC) to track the gait profile, compensate for frictional forces and external perturbations generated in the human-robot interaction using the RBF network, calculate the required moments for each joint motor based on the lower limb exoskeleton dynamics model, and perform stability analysis based on the Lyapunov theory.Results and discussion: Finally, the efficiency of the APSOAC and DRNNASMC algorithms is demonstrated by active and passive walking experiments with three healthy subjects, respectively

Smooth Control the Coaxial Self-Balance Robot Under Impact Disturbances

The purpose of this paper is to propose a systematic smooth control method for improving the stability of the two‐wheeled self‐balance robot under impact disturbances. For enhancing the robtot stability, a blend controller based on states feedback control embedded with the PID speed synchronization is estabilished, as well as a hybrid filter composes of RC network and Kalman algorithm. With the hybrid filter, disturbance signals are maximally attenuated or directly eliminated, and the system sensitivity to the impact disturbances significantly declines ; under the blend motion controller, the robot can not only keep balance under impacts but also achieve synchronization of the two driving wheels. The dynamic model, the blend controller, hybrid filter, and experimental results including application to transport are described, both of the simulation and experimental results are provided to verify the analysis

A Construction Method of Lower Limb Rehabilitation Robot with Remote Control System

In response to the rehabilitation needs of stroke patients who are unable to benefit from conventional rehabilitation due to the COVID-19 epidemic, this paper designs a robot that combines on-site and telerehabilitation. The objective is to assist the patient in walking. We design the electromechanical system with a gantry mechanism, body-weight support system, information feedback system, and man-machine interactive control system. The proposed rehabilitation robot remote system is based on the client/server (C/S) network framework to realize the remote control of the robot state logic and the transmission of patient training data. Based on the proposed system, doctors can set or adjust the training modes and control the parameters of the robot and guide remote patient rehabilitation training through video communication. The robotic system can further store and manage the rehabilitation data of the patient during training. Experiments show the human-computer interaction system of the lower limb rehabilitation robot has good performance, can accurately recognize the information of human motion posture, and achieve the goal of actively the following motion. Experiments confirm the feasibility of the proposed design, the information management of stroke patients, and the efficiency of rehabilitation training. The proposed system can reduce the workload of the doctors in practical training

Smooth Control the Coaxial Self-Balance Robot under Impact Disturbances

The purpose of this paper is to propose a systematic smooth control method for improving the stability of the two-wheeled self-balance robot under impact disturbances. For enhancing the robtot stability, a blend controller based on states feedback control embedded with the PID speed synchronization is estabilished, as well as a hybrid filter composes of RC network and Kalman algorithm. With the hybrid filter, disturbance signals are maximally attenuated or directly eliminated, and the system sensitivity to the impact disturbances significantly declines; under the blend motion controller, the robot can not only keep balance under impacts but also achieve synchronization of the two driving wheels. The dynamic model, the blend controller, hybrid filter, and experimental results including application to transport are described, both of the simulation and experimental results are provided to verify the analysis

Electric locomotive electrical control training based on virtual reality technology

Abstract The recent rapid development of the computer‐aided design technologies has provided concrete support for the education and research in the field of railway transportation. Virtual reality (VR) technology is becoming particularly important in the field of experimental teaching due to its low cost, low restriction and immersion. In this work, we have designed a virtual simulation untried system of the electric joint control in electric locomotives based on VR technology. Based on the user's understanding of the three circuit principles in the experiment, together with the recognition and operation ability to control appliances and equipment, the system assessment system has been scored and evaluated accordingly. The tests of the virtual simulation system have shown the experimental teaching can reduce the cost while effectively improving the learning effect