Cyber-Physical System Optimization Techniques in Mechatronic Redundant Accelerometer Array Devices (RAAD)

A modeler's approach to optimization greatly improved the calibration of redundant accelerometer array devices (RAADs) for batch estimates of stochastic parameters for zero bias (V) and sensitivity (V/g). Each device under test (DUT) has an orientation relative to a shared rigid-body frame. The strategy offers cyber-physical systems a reference-free approach for geometry. A function minimizes estimation errors of intrinsic parameters across a gamut of DUTs in array. The algorithmic goal reduces the population average standard deviation (σ̅
_g̅
) estimate of static gravity (g) measurements across more than or equal to three static world poses. RAAD achieves 72 sensor placements in three poses. Comparisons are made to root-mean-square-error (RMSE) approaches in previous studies. The optimization model solves a nonlinear system with logical constraints using matlab's fmincon() function. Precision is achieved for 48 design variables. The RAAD empirical value for σ̅
_g̅
, initially 0.1042g, improves to 0.0027g and RMSE improves from 0.0282RMSE to 0.0002RMSE. The algorithm is tested in simulation with results yielding a high accuracy in estimates for zero bias, ±0.35%, and sensitivity in the range -0.68% to 2.46%. Simulated orientation results are accurate in the range -0.17% to 0.61% across a deterministic grid. Method comparisons indicate that the σ̅
_g̅
algorithm is superior to RMSE for the calibration of intrinsic parameters.