Robust sensorimotor control of human arm model under state-dependent noises, control-dependent noises and additive noises

The robust control of human arm movements is planned by the integration of sensory information, sensorimotor transformation and human brain computation. The human arm is controlled to adopt an acceptable posture in a robust optimal way. Due to the intelligent nature of human judgments, the application of the Takagi-Sugeno (T-S) fuzzy model to human judgments may be useful for emulating intelligent computation in the prefrontal cortex of the human brain in the sensorimotor control of human arm movements. In this study, we aimed to develop a robust fuzzy estimator-based control scheme to mimic the sensorimotor control of realistic planar movements of the human arm. The state variables of the planar model of the human arm are all available based on visual and proprioception information. Using posture (state) estimation based on human sensory information in the human brain, robust fuzzy estimator-based control was introduced to model the sensorimotor reference tracking control of arm movements in the presence of internal noises, state-dependent noises and environmental noises. Based on the fuzzy interpolation of a nonlinear stochastic arm system, the complex noise-tolerant robust control of the human arm tracking problem was simplified by solving a set of linear matrix inequalities using Newton's iterative method via an interior point scheme for convex optimization. Finally, a simulation was conducted to illustrate the control procedure and to validate the performance of robust fuzzy estimator-based sensorimotor control for the human arm system.