A plausible explanation about the acquisition and realization of beliefs by the central nervous system (CNS) when issuing control actions to counteract external perturbations, is to employ mechanisms aiming to minimize sensory conflict and muscle effort while maintaining biomechanical stability. However, existing head–neck postural control models fail to explicitly integrate this plausible CNS objective within their stabilization mechanisms. This study proposes a novel Model Predictive Control (MPC)-based framework to replicate CNS postural stabilization by incorporating the minimization of sensory conflict as a primary control objective through the MPC cost function. The MPC is integrated in a simplified biomechanical head–neck structure, using a prediction model and sensory feedback to optimize control actions over a finite time horizon within biomechanical constraints. Two human experiments measuring head motion with unpredictable seat and trunk perturbations were used to evaluate and validate different configurations of sensory feedback pathways. During anterior–posterior translational trunk perturbations, the results illustrated that the configuration with vestibular feedback improved head position prediction while muscle effort and partial somatosensory feedback alone, achieved superior results in head pitch prediction. Meanwhile, muscle effort and partial somatosensory feedback were sufficient to stabilize the head during trunk rotational (pitch) perturbations. Finally, a multi-scenario optimization demonstrated that a single set of MPC weights could generalize stabilization across both perturbation types. The results demonstrate the effectiveness of MPC in replicating CNS-inspired postural adjustments, indicating that controlling a simplified biomechanical head–neck model provides a computationally efficient and accurate alternative to complex multi-segment approaches.

The goal of this paper is to contribute to the accurate prediction of human body motion by proposing a novel head-neck model for dynamic driving scenarios with complex vehicle motions. While automated vehicles are considered a potential solution to several transportation issues, there are still significant challenges that need to be addressed, including fundamental questions regarding motion comfort and postural stability. Existing standards fail to accurately describe motion comfort, and current head-neck models have limitations, such as their inability to accurately capture human head responses to dynamic perturbations and lack of adaptability to different perturbations, amplitudes, and individual characteristics. To address these challenges, the authors propose a 3D double inverted pendulum model (DIPM) with a total of 6 degrees of freedom (DoF) as an approximation of head-neck system. The proposed model uses Model Predictive Control (MPC) to derive optimal control inputs for head-neck stabilization. The study validates the proposed model against experimental data of anterior-posterior seat translation and rotation from the literature. The results indicate that the model fitted the experimental data with a variance accounted for 82.80 % in translation and 73.15 % in rotation (pitch). The proposed model paves the path for the accurate assessment of occupants’ postural stability in automated vehicles.