In this paper, we propose a novel iterative learning control (ILC) scheme for precise state tracking of pneumatic muscle (PM) actuators. Two critical issues are considered in our scheme: 1) state constraints on PM position and velocity; 2) uncertainties of the PM model. Based on the three-element form, a PM model is constructed that takes both parametric and nonparametric uncertainties into consideration. By introducing the composite energy function (CEF) approach incorporated with a barrier Lyapunov function (BLF), full state constraints of PM will not be violated and uncertainties are effectively compensated. Through rigorous analysis, we show that under proposed ILC scheme, uniform convergence of PM state tracking errors are guaranteed. Simulation results validate the performance of the proposed scheme.

EMG-driven musculoskeletal model has been broadly used to detect human intention in rehabilitation robots. This approach computes muscle-tendon force and translates it to the joint kinematics. However, the muscle-tendon parameters of the musculoskeletal model are difficult to measure in vivo and varied across subjects. In this study, a direct collocation (DC) method is proposed to optimize the subject-specific parameters in a wrist musculoskeletal model. The resultant optimized parameters are used to estimate the wrist flexion/extension motion. The estimation performance is compared with the parameters optimized by the genetic algorithm. Experiment results show that the DC methods have a similar performance compared with GA, in which the mean correlation are 0.96 and 0.93 for the genetic algorithm and DC method respectively. But the direction collocation method requires less optimization time.

Dynamic simulation of manipulators interacting directly with human is crucial for predicating their performance before the actual use. This paper describes the dynamic analysis and simulation of a novel multi-function mobility assistive device for elderly. The device is designed to help patients who suffer from degradation of the lower limbs muscular strength. Rather than one function device, the proposed device is intended to assist in different lower limb activities, namely, sit to stand and walking activities as well as transfer paralyzed patients from bed to wheelchair and help them stand in upright position to improve blood circulation. The device is based on a non-conventional structure of a 3-RPR planer parallel manipulator which has high rigidity due to the parallel structure. It has some interesting kinematic advantages that facilitate structure and control design. Dynamic model for the assistive manipulator is derived using the first type of Lagrange's equations. A PD-computed torque controller is tuned using genetic algorithm. Simulation results prove high performance of the proposed device in terms of the low tracking error and the proper actuator force.