Flight simulators provide a flexible, efficient, and safe environment for research and training at much lower costs than real flight. The ultimate validity of any simulation would be achieved when – for a particular task – human cognitive and psychomotor behavior in the simulator corresponds precisely to the behavior in the aircraft being simulated. However, it has been shown that for skill-based aircraft control tasks, pilot performance and control behavior are significantly affected by simulator motion cueing settings. Current technology centered fidelity metrics do not reflect to what extent a simulator is able to induce real flight pilot behavior, as they do not incorporate knowledge about human perception and control processes. This warrants the development of a new fidelity metric that determines the simulator’s ability to induce real-flight pilot control behavior. At the Faculty of Aerospace Engineering of Delft University of Technology a research project is dedicated to develop such a behavioral fidelity metric using a cybernetic approach. A prerequisite for developing this fidelity metric is to know exactly how pilot control behavior is affected by the limited physical motion stimuli that are typically provided in a simulator. Furthermore, the knowledge on pilot control behavior in a real aircraft – the baseline – needs to be greatly expanded. The project consists of five steps. In the first step of the project, new identification techniques and methodologies are developed to accurately identify multimodal control behavior. In the second step, pilot control behavior is determined in real flight. The third step constitutes the identification of control behavior in the simulator under an array of different motion cueing conditions. This allows for a systematic comparison of control behavior between real and simulated flight. In the fourth step of the project, this knowledge is used to trace behavioral discrepancies back to the way motion stimuli are presented in the simulator, and improve motion cueing algorithms to increase simulator behavioral fidelity. In the fifth and final step, standards and metrics for behavioral fidelity are developed. This thesis work covers the first three steps of the project. The final goal of this thesis is then to determine how pilot control behavior is affected by the limited motion cues provided in a simulator by comparing control behavior in the simulator under different motion cueing conditions to control behavior in the aircraft. The research will be limited to the effect of different motion cueing settings in a pitch control task. Steps four and five of the project will be covered in another thesis by ir. D.M. Pool. Using a cybernetic approach, pilot control behavior can be characterized by estimating the parameters of quasi-linear pilot models. In previous studies, this approach was used to compare pilot control behavior between real and simulated flight. However, in all these studies only a single, lumped, pilot response function was identified, without distinguishing between the contributions of different perceptual modalities, for example, visual and vestibular. In a multi-sensory environment, such as a motion-base flight simulator, this may obscure the pilot’s ability to adopt a different control strategy by a different use of perceptual modalities. Therefore, to compare control behavior between real and simulated flight adequately, multimodal pilot models need to be identified that are able to model the pilot’s use of modalities separately. The identification of these models requires a combined target-following disturbance-rejection task, as multiple forcing functions need to be inserted at different locations in the control loop, to allow for accurate estimation of the model parameters. At the start of this project, multimodal pilot control behavior had never been identified in real flight. The requirement for a combined target-following disturbance-rejection task complicates the setup of the in-flight experiments significantly. In the aircraft, the introduction of a single target tracking signal is relatively straightforward, as it can be visualized on a display in the cockpit. In-flight disturbance-rejection tasks are much more difficult to perform however, as introducing a deterministic disturbance on the stick-free aircraft motion requires an additional input other than the pilot’s control actions to be sent through the flight control system of the aircraft. To facilitate the experiments for the identification of in-flight multimodal pilot control behavior as part of this thesis work, a novel fly-by-wire system was developed for the Cessna Citation II laboratory aircraft of the Delft University of Technology. This fly-by-wire system allows for the disturbance of the aircraft motion by adding a disturbance signal to the pilot control signal. The system is novel in its design due to the use of the existing electric automatic control system in the aircraft, limiting the modifications to the aircraft. As the variations in pilot model parameters between different experiment conditions are often subtle and the aircraft measurements are relatively noisy, the model parameters should be estimated with the highest accuracy. Traditional two-step parameter estimation methods – used in many previous experiments – often produce less accurate results, as the inaccuracies from the frequency response identification step propagate to the parameter estimation step. To increase the accuracy of the multimodal pilot model parameter estimates, a new parameter estimation technique for multimodal pilot models was developed and addressed in this thesis in Chapter 2. The technique is based on the well-known concept of maximum likelihood estimation. Due to the relatively high levels of remnant noise in experiment data and the presence of redundant parameters in the pilot model, the maximum likelihood parameter optimization problem is very complex with many local minima. To increase the likelihood that the global optimum solution of the parameter vector is found, a genetic algorithm is combined with a more common gradient-based Gauss-Newton algorithm. The advantage of the new identification technique is that it operates solely in the time domain, increasing the accuracy of the parameter estimates compared to the two-step methods traditionally used in this type of research. Using the new identification technique, the parameters of a multimodal pilot model are estimated most accurately when as many forcing functions are inserted into the closed-loop control task at different locations, as the number of modalities to be identified. However, the power requirements of the different forcing functions were not known. In addition, little was known about how pilot control behavior is affected by using multiple forcing functions in a control task as opposed to a single forcing function. The first experiment in this thesis, discussed in Chapter 3, was therefore performed to investigate these two unknowns. The results of the experiment showed that multimodal pilot control behavior is significantly affected by the relative power settings of the target and disturbance forcing functions. When the power of the target forcing function is increased – simultaneously reducing the power of the disturbance forcing function – the cue conflict between the visual and physical motion cues increases, as the target forcing function – as opposed to the disturbance forcing function – is only presented visually. This causes pilots to control with a lower visual gain, while the visual perception time delay becomes higher. In addition, pilots reduce their visual lead and increase their vestibular gain when the power of both forcing functions becomes similar. The result of this change in control strategy is a reduction in tracking performance and control activity. It was found that multimodal pilot control behavior can be evaluated by using a combined target-following disturbance-rejection task with an additional signal with relatively small magnitude. In contrast to frequency-domain identification methods, the maximum likelihood based parameter estimation method has no strict requirement for the use of multi-sine signals as forcing functions in a closed-loop control task. This allows for an exploration into new types of signals to be used in manual control experiments. New types of target signals allow for manual control tasks that are more comparable to real piloting tasks. For example, roll ramp or step target signals introduce a task that is similar to flying a turn maneuver. The second experiment in this thesis (Chapter 4) was developed to investigate the identifiability of multimodal pilot control behavior using ramp and step target signals. In addition, the effect of these signals on pilot performance and control behavior itself was investigated. The experiment revealed that, in terms of performance, a task with ramp target inputs is comparable to a task without a target input. The step target inputs introduce a large increase in error variance, due to the transient behavior at the location of the steps. The step target inputs also result in significantly different response functions of the modalities of the pilot compared to the multi-sine and ramp target inputs, which induce comparable response functions. Based on the findings of the experiment, ramp signals as target forcing function are the best alternative to multi-sine target signals in keeping the ability to separate the pilot response functions for different modalities, while creating a task that is more equivalent to an actual piloting task. The results of the first two experiments were used to optimize the experiments in the remainder of the thesis. Before the actual in-flight and simulator comparison experiments were performed, several preliminary studies were undertaken to get insight into how pilot control behavior is affected by the different motion components that make up the total aircraft motion. In a pitch control task, the total aircraft motion at the pilot station can be decomposed into pitch rotational motion, pitch heave motion, and center of gravity heave motion. Pitch heave is the linear acceleration induced by the pitch rotation of the aircraft and the relative position of the pilot station in front of the center of gravity. Center of gravity heave results from relatively slow changes in aerodynamic lift due to the change in aircraft angle of attack while pitching. In conventional hexapod simulators, the center of gravity heave component is the most problematic to simulate accurately, as its low-frequency high-amplitude characteristics drive the simulator motion system to its limits. Therefore, in most simulator applications, the linear accelerations are heavily attenuated by a motion filter. By increasing the knowledge on how the different motion components are used by the pilot to form a control action, the individual components could be filtered more efficiently to increase behavioral fidelity in future simulator applications. The third experiment, discussed in Chapter 5 of this thesis, was set up to increase this knowledge on how pitch rotational motion, pitch heave motion, and center of gravity heave motion are used by a pilot performing a pitch control task. The results of the experiment indicated that – in a pitch target-following disturbance-rejection task – pitch motion significantly improved tracking performance, with an increased cross-over frequency for the disturbance open-loop. The increase in performance is a result of an increased visual gain and a reduction in visual lead, resulting in a lower effective time delay for the disturbance open-loop. For the Cessna Citation dynamics used, pitch heave motion showed effects similar to pitch rotational motion, but less strongly in part due to the relative short distance of the pilot station to the center of gravity and the motion filter that was used in the experiment. The presence of the center of gravity heave motion cue was found to have no significant effect on performance, however, visual lead significantly increased. This indicates that pilots reduce the use of motion cues in exchange for visual cues in the presence of center of gravity heave motion. A follow-up study focused on the effects of the filtering of pitch heave motion on pilot control behavior. The fourth experiment described in this thesis (Chapter 7) was the first experiment to identify multimodal pilot control behavior – separating the pilot’s visual and vestibular responses – in real flight. The experiment was performed with the new fly-by-wire system in the Cessna Citation II laboratory aircraft and was designed to gain more insight into the control system limitations and the optimal use of the system in future in-flight experiments on the identification of multimodal pilot control behavior. The required deterministic disturbance of the aircraft motion was facilitated by adding a disturbance forcing function to the fly-by-wire control signal. Accurate pilot model parameter estimation results could be obtained using multi-sine and ramp target signals in a pitch and roll task, despite some limiting of the fly-by-wire control signals. The limiting of control signals introduces nonlinearities in the closed control loop. However, the achieved accuracy and the multimodal pilot model parameter values were comparable to estimates from previous flight simulator experiments. These results allowed for the optimization of the final experiment discussed in his thesis in which multimodal pilot control behavior between real and simulated flight was compared. In the final experiment, discussed in Chapter 8, pilots performed a pitch target-following disturbance-rejection task in a simulator under different motion cueing settings, in addition to performing the task in an aircraft in flight, the baseline condition. Except for the applied variation in motion fidelity, differences in experimental setup between the aircraft and the simulator were kept as small as possible. Pilot performance and control behavior were slightly affected by differences in the display and sidestick setup. However, the effects introduced by motion fidelity were far more apparent. For the pitch target-following disturbance-rejection task performed, pilot performance and multimodal pilot control behavior were significantly affected by simulator motion fidelity. For improved motion fidelity towards the full-motion condition in the aircraft, pilot disturbance rejection improved. For higher levels of motion fidelity, the visual lead and lag time constants decreased, while visual and vestibular time delays increased. The lead and lag time constants approximate the characteristic time constants of the controlled aircraft dynamics much better when physical motion is present in the simulator and in flight, revealing the importance of simulator motion for these skill-based tracking tasks. From the limited number of motion conditions tested in this thesis, multimodal pilot control behavior in the simulator motion condition with full pitch motion and filtered pitch and c.g. heave motion best approximates in-flight pilot control behavior. The cybernetic approach proved to be a valuable concept in assessing simulator motion fidelity. Distinct variations in multimodal pilot model parameters were found between conditions with different motion fidelity showing the pilots’ adaption to the supplied motion cues, while the pilots rated these conditions the same on a motion fidelity rating scale. As a recommendation for future work, to investigate the effect of reduced motion fidelity more accurately, experiments should be performed on a single apparatus capable of large motion displacement that allows for simulation of full aircraft motion. This would eliminate the effects of differences in experimental setup experienced in this thesis work. Future research should also be more geared towards simulator fidelity as a whole, as the reduced fidelity of simulator systems other then the motion system (for example, out of the window visual systems) have also shown to influence pilot control behavior. Finally, more research should be devoted to identification techniques that are capable of separating more perceptual modalities and techniques for modeling pilot control behavior in tasks that are more comparable to real piloting tasks.