Measuring the Contribution of the Neuromuscular System During a Pitch Control Task

Abstract

Current pilot models oversimplify the neuromuscular system as a second-order low-pass filter, merely focusing on its role in limiting the stick position bandwidth. However, the neuromuscular system also functions as a fast feedback control system due to reflexive activity and inherent muscle visco-elasticity, allowing pilots to respond intuitively to control column forces, much faster than visual or vestibular cues would allow. Models that neglect this property of the neuromuscular system erroneously attribute its activity to the visual or vestibular system when their parameters are estimated by system identification methods. This paper’s aim is to show a proof-of-concept for a novel method to supplement the currently used pilot models with measurements for the neuromuscular system. These measurements will form the basis for more detailed neuromuscular models, allowing a better description of the contribution of the visual, vestibular, and neuromuscular feedback to the pilot’s control output. In this paper the novel method’s modeling outcome (lumped arm inertia, viscosity and stiffness) will be compared to the conventional neuromuscular estimation (indirect estimation of natural frequency and relative damping). A limited study was performed to provide data for parameter fits of a linearized pilot model for 1 DOF pitch control tasks. In the motion-based SIMONA Research Simulator (SRS) a pilot was instructed to perform a pursuit pitch-tracking task, in face of turbulence on the aircraft, and control force perturbations on the control column. In a novel perturbation method, these three forcing functions (perturbing the visual tracking signal, disturbing the aircraft’s elevator deflection and the control-force cues) were designed to contain power at three different frequency sets, allowing simultaneous identification of three corresponding frequency response functions. Additionally, a separate experiment was done to demonstrate the adaptability of the neuromusculoskeletal system, showing that a pilot can become approximately ten times more stiff or compliant than during relaxed conditions. The parameters in the visual, vestibular and neuromuscular system models are estimated by a combination of model-based system identification techniques in the frequency domain. The novel method provides estimated values for the pilot’s lumped arm dynamics (inertia, viscosity and stiffness) while executing a pitch control task. For this task, the corresponding relative damping and natural frequency are in the same order of magnitude as those estimated in the conventional method.