'g' of the simulator platform: Typically, to find position, the low-pass filter (in a continuous-time setting) is represented in the s-domain with the following transfer function: The inputs to the high-pass filter are then calculated according to the following equation: where are the force inputs. The high-pass filter may then be represented according to (for example) the following series: The two integrators in this series represent the integration of acceleration into velocity, and velocity into position, respectively. , and represent the filter parameters. It is evident that the output of the filter will vanish in steady state, preserving the location of the open-loop equilibrium points. This means that while transient inputs will be "passed", steady-state inputs will not, thus fulfilling the requirements of the filter. The present practice for empirically determining the parameters within the washout filter is a trial and error subjective tuning process whereby a skilled evaluation pilot flies predetermined maneuvers. After each flight the pilot's impression of the motion is communicated to a washout filter expert who then adjusts the washout filter coefficients in an attempt to satisfy the pilot. Researchers have also proposed using a tuning paradigm and the capturing of such using an expert system. Nonlinear washout filter This washout filter can be regarded as the result of a combination of an Adaptive and an Optimal washout filter. A nonlinear approach is desired to further maximize the available motion cues within the hardware limitations of the motion system, therefore resulting in a more realistic experience. For example, the algorithm described by Daniel and Augusto computes a gain, α, as a function of the system states; thus, the washout is time varying. The 'α' gain will increase as the platform states increase their magnitude, making room for a faster control action to quickly washout the platform to its original position. The opposite outcome occurs when the magnitude of the platform states is small or decreasing, prolonging the motion cues which will be sustained for longer durations. Likewise, the work of Telban and Cardullo added an integrated perception model that includes both visual and vestibular sensation to optimize the human's perception of motion. This model as shown to improve pilot's responses to motion cues. Adaptive washout filter This adaptive approach was developed at NASA Langley. It is made up of a combination of empirically determined filters in which several of the coefficients are varied in a prescribed