Development of discrete event system (DES)-based controller for robot-assisted upper limb rehabilitation platform - Fatai, Sado (Kulliyyah of Engineering, International Islamic University Malaysia, 2015)
The importance of robotic devices for upper-limb rehabilitation therapy has been underscored by clinical evaluation of some existing robotic platforms. The devices have been used largely with the aid of expert human therapists who are required to specify appropriate therapy placement and adjust the therapy in consonant with the patient physical recovery, active participation, or motor control performance. A survey on the control strategies, however, for most of the rehabilitation robots reveals that, although the robotic systems allow several task specifications aimed at inducing motor plasticity, their control strategies can be considered static or “low-level” in that they do not adapt control parameters to the physical recovery condition of the patients. Hence, with increase in the population of stroke patients compared with the few available therapists, it is crucial to have an adaptive system that can assume closely the role of an expert therapist by sensing the patients' muscle tone, physical recovery condition, or sensorimotor control performance to specify appropriate therapy and to provide assessment. A “high-level” adaptive hybrid impedance controller implemented under a combined novel discrete event system (DES) framework and Modified Ashworth Scale (MAS) assessment criteria for rehabilitation of the upper extremity of post-stroke patients is therefore proposed and developed in this research work. The framework adopts the hybrid impedance control strategy to allow the control of the dynamic interaction (tracking of desired force and/or position) between the patient and the robot for robot-patient compliant motion. In order to adapt the control parameters to the patients' recovery condition, an autoregressive exogenous (ARX) impedance model of the upper-limb is developed and implemented by means of a recursive polynomial model estimator block to estimate the patients' upper-limb impedance parameters (inertia, stiffness, and damping factors) as a measure of physical recovery or sensorimotor control performance. The impedance information is then used by the novel combination of the DES and MAS framework implemented under a hybrid automata framework to update the control parameters and to provide assessment of the patient's recovery. To test the ability of the controller for robot-patient compliant motion (force and position), simulation studies for a simple flexion range of motion exercise were carried out and experimental validation was performed. In addition, two healthy subjects were recruited, as an initial feasibility study based on clinical standard, to mimic two cases of severe and mild upper-limb hemiparesis in order to test the adaptability of the control framework. Results obtained show that the controller is able to track the desired force and position trajectories with a maximum RMSE of 0.37N and 0.024m respectively to ensure compliant motion, and sufficiently adapt the control parameters to the subject's physical recovery condition and motor control performance.