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The purpose of this paper is to present the static and dynamic characteristics of the rehabilitation joint.

The rehabilitation joint is driven by pneumatic muscle actuators (PMAs). Rehabilitation robot is normally composed of several rehabilitation joints. The static and dynamic characteristics of the rehabilitation joint are important for control of the rehabilitation robot. Analysis and modeling of the rehabilitation joint is based on experiments.

The static model of the PMA is obtained by the method of curve fitting and achieved better precision compared to the existing representative models. A second‐order model fits the dynamic characteristic of the rehabilitation joint better than a first order one.

The rehabilitation joint and the patient's joint combine to make an independent system, and the unstable factors of the patient's joint make it difficult in precisely modeling the rehabilitation joint.

The characteristics of the rehabilitation joint are all based on the data that were recorded in a series of with experiments, the same with modeling of the rehabilitation joint.

The purpose of this paper is to present the control methods of the exoskeleton robotic arm for stroke rehabilitation.

The robotic arm is driven by the pneumatic muscle actuators. The control system provides independent control for the robot. The joint axes of the robotic arm are arranged to mimic the natural upper limb workspace.

Findings are the classification of training modes and control methods of rehabilitation training, and the characters of both the instant spasm and the sustaining one.

This paper is a preliminary step in the control system and the kinematical characteristics should be analyzed to achieve high precision of movement.

Based on a hierarchical structure, the control system allows the execution of sequence of switching control methods: position, force, force/position and impedance. Patient‐active‐robot‐passive and patient‐passive‐robot‐active (PPRA) training modes are also presented in this paper. In PPRA mode, the robotic arm can provide pre‐specified resistances on the patient's arm. Both instant and sustaining spasms are taken into account for safety.

This paper reviews the current status of devices for use as exoskeletons for assisting or constraining human movements. Applications include teleoperation and force augmentation to allow people to operate more easily or more efficiently in a variety of situations, including military and emergency service applications.

Manipulators in one form or another have been in existence for many hundreds of years with the design usually motivated by the task to be undertaken. Most often in robots this has led to a simple two fingered claw mechanism which has been adequate for many tasks but when the problem domain includes a varied range of delicate or easily damaged objects, one approach is to emulate those attributes of the human hand which make it such a versatile end‐effector. This paper will study two aspects of manipulator design: the construction of a dextrous hand with multi degree of control prehension, and the actuation systems to drive this hand that will make use of complaint drives to produce a “soft” but highly flexible mechanism to handle delicate products and materials.

The purpose of this paper is to estimate the deformation of soft manipulators caused by obstacles accurately and the contact force and workspace can be also predicted.

The continuum deformation of the backbone of the soft manipulator under contact is regarded as two constant curvature arcs and the curvatures are different according to the fluid pressure and obstacle location based on piecewise constant curvature framework. Then, this study introduces introduce the moment balance and energy conservation equation to describe the static relationship between driving moment, elastic moment and contact moment. Finally, simulation and experiments are carried out to verify the accuracy of the proposed model.

For rigid manipulators, environmental contact except for the manipulated object was usually considered as a “collision” which should be avoided. For soft manipulators, an environment is an important tool for achieving manipulation goals and it might even be considered to be a part of the soft manipulator’s end-effector in some specified situations.

There are also some limitations to the presented study. Although this paper has made progress in the static modeling under environmental contact, some practical factors still limit the further application of the model, such as the detection accuracy of the environment location and the deformation of the contact surface.

Based on the proposed kinematic model, the bending deformation with environmental contact is discussed in simulations and has been experimentally verified. The comparison results show the correctness and accuracy of the presented SCC model, which can be applied to predict the slender deformation under environmental contact without knowing the contact force.

The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN).

The robot system has characters of high order, time delay, time variation and serious nonlinearity. The classical PID controller cannot achieve satisfactory performance in control of such a complex system. This paper combined the fuzzy control with neural networks and established the FNN controller and applied it in control of the robot.

The experimental results showed that the FNN controller had excellent performances in position control of the rehabilitation robotic arm such as fast response, small overshoot and small vibration.

This work is focused on the static FNN algorithm by updating the second and fifth layers of the membership functions. The performance can be improved further if the third layer (reasoning layer) can be updated online.

Based on a hierarchical structure of the FNN controller, this paper designed the FNN controller and applied it in control of the rehabilitation robot driven by pneumatic muscles.