To improve the control performance of pneumatic muscle (PM) antagonistic joint systems under input saturation constraints, a Takagi-Sugeno (T-S) fuzzy model predictive control (MPC) strategy based on a cost function with saturation constraints was proposed. Firstly, based on the dynamic model of the pneumatic artificial muscle, a T-S fuzzy model was established through fuzzy approximation. After discretization, the T-S fuzzy prediction model was constructed. Secondly, the saturation constraint of the pneumatic muscle input was considered. The saturation constraint was converted into a saturation deviation function and introduced into the cost function. A cost function integrating output error, control input, and saturation constraint terms was established. The sequential quadratic programming method was used to iteratively solve the optimal control law. Finally, to verify the effectiveness of the control method, simulations and physical experiments were conducted. The results show that the method effectively constrains control input within the prediction horizon and avoids exceeding the physical pressure limits. The designed controller significantly improves dynamic response speed and tracking accuracy.