Human biomechanical movements are complex physiological tasks which are efficiently regulated by the central nervous system (CNS). Proprioceptors (muscle spindles) provide feedback of fascicle length and velocity from a joint to CNS, which then control the entire movement. These feedbacks have delays which are accounted for by the required output command. In this study, we used a four-link sagittal plane nonlinear biomechanical model with three joint angles, to simulate human sit-to-stand (STS) movement in the presence of these physiological latencies. Ankle, knee and hip joint angles have delays for angular and velocity feedbacks. We linearized the whole model using padé approximation at sitting and standing positions which resulted in two eighteenth order linear systems. We integrated these local models into a fuzzy model with Gaussian membership function. The knee flexion angle during sit to stand movement provides the criterion for determining the weights of fuzzy membership functions. We developed a H2 dynamic optimal controller for each local linear model and integrated with the fuzzy model. This controller computed the joint's torque or inputs for biomechanical STS task. We also introduced a reference trajectory to track the knee flexion angle error for smooth and physiological relevant sit to stand transfer. Simulation results of angular profiles and kinematics variables demonstrate the applicability of the fuzzy modeling with H2 controller in the presence of feedback latencies.