This paper presents a robust adaptive controller based on the backstepping technique using an extended state observer (ESO), implemented on a 2PUS+RR parallel robot, to minimize the trajectory tracking error. The proposed backstepping-ESO controller scheme is designed to compensate for the robot’s structured (parametric) and unstructured (nonlinear friction, external disturbances, and dynamics) uncertainties. The overall stability of the controller is guaranteed by the Lyapunov theory. Cosimulation in MATLAB-Simulink and ADAMS View is presented to validate the results of the ESO and backstepping controller implemented in the virtual and physical prototype. For the virtual prototype, it was determined that the system is stable in 2 s and presents a maximum absolute error of 3.5 × $10^{-6}$ m for the actuator position and 2.8 × $10^{-5}$ rad for mobile platform orientation. Regarding the physical robot, a maximum absolute error of 5 × $10^{-4}$ m for the actuator position and 0.0575 rad for the orientation of the robot mobile platform values do not represent a problem for ankle rehabilitation movements. Experimental results were also presented and compared with ankle motion to demonstrate that the applied control system meets the motion requirements of the ankle rehabilitator.

Exoskeleton robots, which can be worn on human limbs to improve or to rehabilitate their function, are currently of great importance. When these robots are used in rehabilitation, one aspect that must be fulfilled is their capacity to adapt to different patients without significantly varying their performance. This paper describes the application of a relatively new control technique called virtual decomposition control (VDC) to a seven degrees-of-freedom (DOF) exoskeleton robot arm, named ETS-MARSE. The VDC approach mainly involves decomposing complex systems into subsystems, and using the resulting simpler dynamics to conduct control computation, while strictly ensuring global stability and having the subsystem dynamics interactions rigorously managed and maintained by means of virtual power flow. This approach is used to deal with different masses, joint stiffness and biomechanical variations of diverse subjects, allowing the control technique to naturally adapt to the variances involved and to maintain a successful control task. The results obtained in real time on a 7DOF exoskeleton robot arm show the effectiveness of the approach.

To assist physically disabled people with impaired upper limb function, we have developed a new 7-DOF exoskeleton-type robot named Motion Assistive Robotic-Exoskeleton for Superior Extremity (ETS-MARSE) to ease daily upper limb movements and to provide effective rehabilitation therapy to the superior extremity. The ETS-MARSE comprises a shoulder motion support part, an elbow and forearm motion support part, and a wrist motion support part. It is designed to be worn on the lateral side of the upper limb in order to provide naturalistic movements of the shoulder (vertical and horizontal flexion/extension and internal/external rotation), elbow (flexion/extension), forearm (pronation/supination), and wrist joint (radial/ulnar deviation and flexion/extension). This paper focuses on the modeling, design, development, and control of the ETS-MARSE. Experiments were carried out with healthy male human subjects in whom trajectory tracking in the form of passive rehabilitation exercises (i.e., pre-programmed trajectories recommended by a therapist/clinician) were carried out. Experimental results show that the ETS-MARSE can efficiently perform passive rehabilitation therapy.