Research On Motion Control Of Human Arm And Robotic Assisted Upper Extremity Repetitive Therapy

With the increasing number of the hemiplegia patient, persons regard the rehabilitation therapy of the arm movement functional disorder as a more important thing. But, the current rehabilitation systems are driven by the motor, and have an open-loop control structure. The active movement consciousness of the patients and the feedback signal during the therapy are not usually been considered. This dissertation is to study the human arm motion control in the sagittal plane and the rehabilitation robot control based on the research results of the human arm movement mechanism.The research work has been done in this dissertation includes the following aspects:The posture control of the human arm in the sagittal plane is discussed. The dynamic characteristic of the human arm in the sagittal plane is analyzed, and then the arm is modeled as a neuro-musculoskeletal model with two degrees of freedom and six muscles. A multilayer perceptron network containing feedforward and feedback control modes is used. The duration of the movement is regulated according as the current feedback states. The Levenberge-Marguardt training algorithm and the resilient back propagation algorithm are compared.Based on cerebellar model articulation controller, fuzzy control, sliding mode control scheme, and the dynamic characteristic and the control requirement of the human arm, a sliding-mode-based fuzzy cerebellar model articulation controller is investigated. The sliding mode function is used to transfer the input variables; a quantization mode based multidimensional receptive field function is presented. The network is trained at two learning stages to guarantee the stability of the control scheme.An evolutionary diagonal recurrent neural network is presented for trajectory tracking control of the human arm in the sagittal plane, in which hybrid genetic algorithm and evolutionary program strategy is applied to optimize the network architecture and an adaptive dynamic back propagation algorithm with momentum is used to obtain the network weights, Lyapunov theory can be implemented to guarantee the convergence of the control system. This dynamical network is more suitable for trajectory tracking control of a multi input multi output nonlinear system, and can be further applied into the rehabilitation robot control.Trajectory tracking control in real time of the human arm in the sagittal plane is further discussed. A sliding-mode-based diagonal recurrent cerebellar model articulation controller is presented, in which recurrent units are introduced in the association layer to add the dynamic mapping ability of the network. Compared with other control methods, this network has a better dynamic and static performance and better robustness.The posture control of the rehabilitation robot for the human arm is investigated. The function and the structure of the Robotic Assisted Upper Extremity Repetitive Therapy (RUPERT) are analyzed, and then the forward simMechanics model, the mathematic model, and an interactive robust controller are presented. The functions of patient's active action are designed depending on the motion ability of their arms. The performance of the controller is tested by simulations and experiments.A multi input multi output sliding mode control scheme is designed for the trajectory tracking control of the multi-joint link robot, the structural model of the RUPERT-hemiplegia arm system.At last, this dissertation is summarized, and the further work is discussed.