fuzzy model predictive control

Endeavor achieving as much as realistic perception of motion in motion cueing design are shifted towards utilizing the general nonlinear models of the motion systems. However, this approach is computationally more demanding. Hence, using efficient computational algorithms are essential to fulfill the requirements of high computing speed and accuracy without any interruption in real-time operations. Takagi - Sugeno fuzzy system as a major sector of soft computing characterized by effectively dealing with nonlinear processes, high computational speed, and easy implementation is used to approximate the nonlinear inverse kinematic of the motion system model by fuzzy interpolation of the set of linear sub models. The augmented linear parameter varying motion cueing model is constructed incorporating the fuzzy approximated inverse kinematics of the motion system along with the human perception of motion. The complete motion cueing model is computed using real-time model predictive control approach considering all physical constraints. The results of simulation show remarkable improvements in terms of less and smooth movements of actuators, hence much efficient use of motion system’s workspace compared to its general nonlinear counterpart, which reveals an effective alternative in real-time applications.