Robust Control of Physiologically Relevant Sit-to-Stand Motion Using Reduced Order Measurements

Biomechanical movements have been an area of interest to the researchers from various disciplines. Using mathematical models of human musculoskeletal structure, motion analysis is done in order to understand and improve body movement mechanism. For people having motion disorders due to disease or aging, sit-to-stand (STS) is the minimum of the motion that may keep them from being fully handicapped. The human factor involved in studying STS is therefore very high. This paper analyzes human STS movement using four-segment nonlinear sixth order model realized in SIMULINK's SimMechanics environment. The role of kinematic variables like joint positions and velocities in human motion is specially studied. To reduce the number of sensors employed, only joint positions are measured. Moreover, the measurements are noise contaminated which makes estimation of true states more challenging. To estimate position and velocity profiles and achieve a smooth human-like STS motion, robust controllers, Linear Quadratic Gaussian (LQG), and H-infinity based compensators are employed and results are compared. The full order state feedback controllers have proved to be robust and thus the STS motion achieved is close to natural motion. All segments contribute to the STS task in physiologically relevant manner. Despite using reduced order measurements an improvement has been made in some results in comparison of similar studies. This scheme bears the potential to achieve the controlled STS motion for diagnosis, rehabilitation and humanoid robotics applications.