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Abstract A four‐degree‐of‐freedom upper limb exoskeleton rehabilitation robot system with a gravity compensation device is constructed. The objective is to address the rehabilitation training needs of patients with upper limb motor dysfunction. A BP neural network adaptive control method based on particle swarm optimization is proposed. First, the degrees of freedom of the human body are analyzed, and a Lagrange method is employed to construct a dynamic model. Second, a particle swarm optimization back propagation neural network adaptive control algorithm based on particle swarm optimization is presented. Subsequently, the range of motion of the upper limbs is analyzed with reference to muscle anatomy and a three‐dimensional motion capture system. And the robot structure design is analyzed in detail. Finally, simulation experiments were conducted, and the results demonstrated that the proposed method exhibited high effectiveness and accuracy.

In the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, without the clinical background of physiatrist. However, in clinical practice, the experience of physiatrist plays an important role in the rehabilitation evaluation of patients. Therefore, this paper designs a 5 degrees of freedom (DoFs) upper limb (UL) rehabilitation robot and proposes a rehabilitation evaluation model based on Belief Rule Base (BRB) which can add the expert knowledge of physiatrist to the rehabilitation evaluation. The motion data of stroke patients during active training are collected by the rehabilitation robot and signal collection system, and then the upper limb motor function of the patients is evaluated by the rehabilitation evaluation model. To verify the accuracy of the proposed method, Back Propagation Neural Network (BPNN) and Support Vector Machines (SVM) are used to evaluate. Comparative analysis shows that the BRB model has high accuracy and effectiveness among the three evaluation models. The results show that the rehabilitation evaluation model of stroke patients based on BRB could help physiatrists to evaluate the UL motor function of patients and master the rehabilitation status of stroke patients.

Commercial vehicle disk brakes operate at a high-temperature and in a heavy-load environment within the braking system. The primary cause of failure is the cracking of the brake disk. In order to study its fatigue damage and service life, a finite element model of disk brake fatigue life was established, and thermal stress coupling simulation analysis was carried out from a practical problem. Based on the temperature and stress fields of the brake disk under emergency braking conditions obtained from the simulation results, the effects of vehicle load, initial speed, temperature, and other factors on brake fatigue life are explored. The fatigue life of the hazardous node can be calculated using the Manson–Coffin model, and then the strain–life (ɛ–N) curve of the material can be fit at high temperature. The fatigue life of brake disks was predicted using the fatigue analysis software FE-SAFE and verified by testing. The results showed that the maximum stress on the surface of the disk brake was the same as the area of the minimum fatigue life, accurately analyzing the fatigue life of the region and predicting the location of fatigue cracks. The results of the research can provide a reference for the design of disk brake engineering and fatigue failure.

This paper presents a bionic cable-driven mechanism to simulate the motion of human wrist which is suitable for human forearm–wrist rehabilitation. It fulfills workspace of the human forearm–wrist and it can train the joint in active and passive. With three degrees of freedom, it completes the supination/pronation of the forearm, the radial/ulnar deviation, and flexion/extension of the wrist. In addition to the movement of single degree of freedom of the forearm–wrist, it can also complete circumduction of the wrist. The mechanism consists of revolving platform, parallel mechanism, supporting mechanism, and movable table. Especially, in the parallel mechanism, a spring is added between the fixed and moving platform, and the moving platform is designed in the shape of a hemispherical shell. Utilizing the resilient properties of the extension spring and the support of the hemispherical shell, the problem of slack in the cable is solved in this mechanism. Since the spring is a passive component and cannot be calculated directly, a method combining kinematics and statics is proposed to calculate the relationship between the pose of the moving platform and the cable. Meanwhile, the kinematics, statics, and workspace solution of the mechanism are derived. Then, the simulation results demonstrate the accurateness and feasibility of the inverse kinematics and workspace derivation of the mechanism. Finally, the experiments are analyzed to verify the mechanism suitable for forearm–wrist rehabilitation tasks.

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

This paper presents an assisted upper limb rehabilitation robot (ULRR) for patients who have experienced stroke who are in the middle and late stages of rehabilitation and have certain muscle strength. The ULRR can complete adduction and abduction motion of the shoulder joint (SJ) in the frontal plane in one step, which can save time and improve the efficiency of rehabilitation training. Based on the principles of ergonomics and rehabilitation medicine, the freedom degree of the upper limb and the motion range of each joint are determined, and the structure of the shoulder, the elbow, and the wrist joint of ULRR are designed. The kinematics model of the robot is established, and the kinematics equations are derived. Meanwhile, the simulation analysis and the workspace analysis of the robot are carried out, and the different movement forms of SJ adduction and abduction are compared and analyzed. Then, the trajectory of the robot is planned to complete the act of drinking water. Finally, an experimental platform is built to complete the ULRR to help participants complete the experiments of drinking water and active training. The experiments verify that the robot is suitable for rehabilitation tasks.

This research introduces the knee exoskeleton system that assists in knee joint rehabilitation, which is centered on human wearing comfort. According to the bionic principle, this paper proposes a bionic knee exoskeleton structure based on a cross four-bar linkage mechanism. The cross four-bar linkage mechanism is used to simulate the internal cruciate ligament of the human knee joint to realize the instantaneous rotation center movement of the knee joint. The motor drives the telescopic rod to simulate the movement of the exoskeleton of the knee joint by the thigh muscle of the human body. The auxiliary limit locking structure simulates the knee joint patella to prevent hyperextension of the exoskeleton of the knee joint. The particle swarm-based algorithm is used to optimize the size and position of the connecting rod of the cross four-bar linkage to follow the motion of the human knee joint better. The results show that the optimized and synthesized cross four-bar linkage mechanism has a small average error value, which can better reproduce the anthropomorphic motion characteristics of the human knee joint, achieve an ideal match between the motion form of the human knee joint and the exoskeleton, and improve coordination and adaptability with human joint movement. Through the wearer test, it is found that the structure has a variable instantaneous center of rotation trajectory. Under the condition of satisfying the flexion angle and torque of the human body, the knee joint movement could be simulated with the optimal trajectory to achieve the consistency with the human knee joint movement, so as to alleviate the discomfort of the wear movement of the patients in the auxiliary rehabilitation process, and it provides an advantage for the wear comfort of the human rehabilitation movement.

Due to insufficient muscle strength, a novel rehabilitation mechanism of the shoulder joint exoskeleton is proposed and analyzed based on gear and rack transmission, which solves the problem that stroke patients need repetitive exercise rehabilitation training. First, the range of motion angle of the shoulder joint is determined, and the mechanical structure of shoulder joint rehabilitation is designed. The arc rack is engaged with the output shaft gear of the reducer, and the servo motor is utilized as the driving force to realize the internal/external rotation movement of the shoulder joint. The motor connects the horizontal connecting rod and the rotating rod through the cross roller bearing to realize the abduction/adduction and flexion/extension movement of the shoulder joint. Second, the kinematics and dynamics of the shoulder joint exoskeleton are analyzed, investigated, and verified for the rehabilitation mechanism. The simulation analysis of the mechanism is completed in the virtual prototype. The changes in the joint angle and end trajectory and joint moment with time and angular velocity under the given working conditions are obtained, which directly reflect the movement of each joint of the rehabilitation mechanism. Finally, aiming at the nonlinear disturbance in the trajectory tracking control of the rehabilitation mechanism of the shoulder joint exoskeleton, the trajectory tracking approach of the mechanism is achieved aided with the closed-loop PD iterative learning control method. The results demonstrate that the actual trajectories are in good agreement with the desired trajectories, which can achieve effective tracking and improve the control quality of the system.

According to the requirements of the reconnaissance robot for the ability to adapt to a complex environment and the in-depth study of the obstacle climbing mechanisms, a planetary wheel-leg-combined mechanism capable of adapting to complex terrains is proposed. According to the proposed planetary wheel-leg-combined mechanism, the land part of the air-ground amphibious reconnaissance robot is designed. Considering the obstacle and fast marching performance, four groups of combined wheel-leg mechanisms are adopted in the land bank. Under the action of three kinds of obstacles, the stability and the movement ability of the robot are analyzed by using the static method. The parameter model of the reconnaissance robot is built by a virtual prototype dynamics software MSC.ADMAS. The kinematic characteristic curves of each component and the whole prototype are obtained, which provides a theoretical basis for the design and numerical calculation of the robot structure. Finally, the climbing ability tests of the reconnaissance robot prototype verify the reliability and practicability of the body structure of the reconnaissance robot.

In this paper, a three-order Taylor-type numerical differentiation formula is firstly utilized to linearize and discretize constrained conditions of model predictive control (MPC), which can be generalized from lower limb rehabilitation robots. Meanwhile, a new numerical approach that projected an active set conjugate gradient approach is proposed, analyzed, and investigated to solve MPC. This numerical approach not only incorporates both the active set and conjugate gradient approach but also utilizes a projective operator, which can guarantee that the equality constraints are always satisfied. Furthermore, rigorous proof of feasibility and global convergence also shows that the proposed approach can effectively solve MPC with equality and bound constraints. Finally, an echo state network (ESN) is established in simulations to realize intention recognition for human–machine interactive control and active rehabilitation training of lower-limb rehabilitation robots; simulation results are also reported and analyzed to substantiate that ESN can accurately identify motion intention, and the projected active set conjugate gradient approach is feasible and effective for lower-limb rehabilitation robot of MPC with passive and active rehabilitation training. This approach also ensures computational when disturbed by uncertainties in system.

To help people with impairment of lower extremity movement regain the ability to stand and walk, and to enhance limb function, this study proposes an anthropomorphic design of an electrically driven, lower-limb exoskeleton rehabilitation robot. The angular range of the robot’s motion was determined according to the characteristics of the targeted lower-limb joints; the robot was given an active–passive anthropomorphic design with 12 degrees of freedom. The multi-degree-of-freedom hip exoskeleton, bionic artificial knee exoskeleton and passive rigid-flexible coupling ankle exoskeleton can assist patients in rehabilitation exercises with better wear comfort and exercise flexibility. A kinetic model of the seven-rod lower-limb exoskeleton rehabilitation robot was built, and data analysis of the dynamically captured motion trajectory was conducted. These provided a theoretical basis for gait planning and the control system of the lower-limb exoskeleton rehabilitation robot. The results show that the lower-limb exoskeleton rehabilitation robot system possesses sound wearing comfort and movement flexibility, and the degree of freedom of movement of the exoskeleton robot matches well with that of human movement. The robot can thus provide effective assistance to patients’ standing and walking rehabilitation training.

This paper proposes a bionic flexible wrist parallel mechanism to simulate human wrist joints, which is characterized by a rope-driven, compression spring-supported hybrid mechanism. Specifically, to realize the movement of the wrist mechanism, a parallel structure is adopted to support the mobile platform and is controlled by a cable, which plays the role of wrist muscles. Because the compression spring is elastic, it is difficult to directly solve inverse kinematics. To address this problem, the external force acting on the moving platform is firstly equivalent to the vector force and torque at the center of the moving platform. Then, based on inverse kinematic and static analyses, the inverse motion of the robot model can be solved according to the force and torque balance conditions and the lateral spring bending equation of the compression spring. In order to verify the proposed method, kinematics, statics, and parallel mechanism workspace are further analyzed by the software MATLAB. The obtained results demonstrate the effectiveness and feasibility of the designed parallel mechanism. This work offers new insights into the parallel mechanism with flexible joints in replicating the movements of the human wrist, thus promoting the development of rehabilitation robots and rope-driven technology to some extent.

Nowadays, patients with mild and moderate upper limb paralysis caused by cerebral apoplexy are uncomfortable with autonomous rehabilitation. In this paper, according to the “rope + toothed belt” generalized rope drive design scheme, we design a utility model for a wearable upper limb rehabilitation robot with a tension mechanism. Owing to study of the human upper extremity anatomy, movement mechanisms, and the ranges of motion, it can determine the range of motion angles of the human arm joints, and design the shoulder joint, elbow joint, and wrist joint separately under the principle of ensuring the minimum driving torque. Then, the kinematics, workspace and dynamics analysis of each structure are performed. Finally, the control system of the rehabilitation robot is designed. The experimental results show that the structure is convenient to wear on the human body, and the robot’s freedom of movement matches well with the freedom of movement of the human body. It can effectively support and traction the front and rear arms of the affected limb, and accurately transmit the applied traction force to the upper limb of the joints. The rationality of the wearable upper limb rehabilitation robot design is verified, which can help patients achieve rehabilitation training and provide an effective rehabilitation equipment for patients with hemiplegia caused by stroke.

This paper presents a humanoid robotic arm massage system with an aim toward satisfying the clinical requirements of pain relief on the waist and legs of older patients during Chinese medicinal massage. On the basis of an in-depth analysis regarding the characteristics of arm joints of the human body and Chinese medicinal massage theory, a humanoid robotic arm massage system was designed by adapting a bottom to top modular method. The combined finite element and kinematic analysis led to an improved performance according to repeated positioning accuracy, massage strength accuracy, and massage effect. The developed humanoid robotic arm was characterized by a compact structure, high precision, light quality, and good stiffness, achieving a good bearing capacity. Due to the PID controller, the numerical simulations and experimental results provided valuable insight into the development of Chinese medicinal massage robots and massage treatments for patients who suffer from lumbar muscle strain.

In the process of turning, pointing at the uncertain phenomenon of cutting which is caused by the disturbance of random factors, for determining the uncertain scope of cutting force, the integrated symmetric fuzzy number and the finite element method (FEM) are used in the prediction of cutting force. The method used symmetric fuzzy number to establish fuzzy function between cutting force and three factors and obtained the uncertain interval of cutting force by linear programming. At the same time, the change curve of cutting force with time was directly simulated by using thermal-mechanical coupling FEM; also the nonuniform stress field and temperature distribution of workpiece, tool, and chip under the action of thermal-mechanical coupling were simulated. The experimental result shows that the method is effective for the uncertain prediction of cutting force.

We present two new families of iterative methods for obtaining simple roots of nonlinear equations. The first family is developed by fitting the model m(x)=epx(Ax2+Bx+C) to the function f(x) and its derivative f′(x), f″(x) at a point xn. In order to remove the second derivative of the first methods, we construct the second family of iterative methods by approximating the equation f(x)=0 around the point (xn,f(xn)) by the quadratic equation. Analysis of convergence shows that the new methods have third-order or higher convergence. Numerical experiments show that new iterative methods are effective and comparable to those of the well-known existing methods.