During pitch rotation of the aircraft, a pilot, seated in front of the aircraft center of gravity, is subjected to rotational pitch and vertical heave motion. The heave motion is a combination of the vertical motion of the aircraft center of gravity and the heave motion as a result of the pitch rotation. In a pitch tracking task, all of these cues could potentially have a positive effect on performance and control behavior, as they are all related to the aircraft pitch attitude. To improve the tuning of flight simulator motion filters, a better understanding of how these motion components are used by the pilot is required. First, the optimal use of the different motion components was evaluated using an optimal control analysis. Next, an aircraft pitch attitude control experiment was performed in the SIMONA Research Simulator, investigating the effects of pitch rotation, pitch heave, and center of gravity heave on pilot control behavior. Pilot performance significantly improved with pitch motion, with an increased crossover frequency for the disturbance open loop. The increase in performance was a result of an increased visual gain and a reduction in visual lead, allowed for by the addition of pitch motion. Pitch heave motion showed similar but smaller effects. The center of gravity heave motion, although taking up most of the simulator motion space, was found to have no significant effects on performance and control behavior
This paper presents a new method for estimating the parameters of multi-channel pilot models that is based on maximum likelihood estimation. To cope with the inherent nonlinearity of this optimization problem, the gradient-based Gauss-Newton algorithm commonly used to optimize the likelihood function in terms of output error is complemented with a genetic algorithm. This significantly increases the probability of finding the global optimum of the optimization problem. The genetic maximum likelihood method is successfully applied to data from a recent human-inthe-loop experiment. Accurate estimates of the pilot model parameters and the remnant characteristics were obtained. Multiple simulations with increasing levels of pilot remnant were performed, using the set of parameters found from the experimental data, to investigate how the accuracy of the parameter estimate is affected by increasing remnant. It is shown that only for very high levels of pilot remnant the bias in the parameter estimates
Abstract-Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This 'cybernetic approach' enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training associated with manual control. Current cybernetics theory is primarily based on technology and analysis methods formalized in the 1960s and has shown to be limited in its capability to capture the full breadth of human cognition and control. This paper reviews the current state-of-the-art in our knowledge of human manual control, points out the main fundamental limitations in cybernetics, and proposes a possible roadmap to advance the theory and its applications. Central in this roadmap will be a shift from the current linear time-invariant modeling approach that is only truly valid for human behavior under tightly controlled and stationary conditions, to methods that facilitate the analysis of adaptive, and possibly time-varying, human behavior in realistic control tasks. Examples of key current developments in the field of cybernetics -human use of preview, predictable discrete maneuvering, skill acquisition and training, time-varying human modeling, and neuromuscular system modeling -that contribute to this shift are presented in this paper. The new foundations for cybernetics that will emerge from these efforts will impact all domains that involve humans in manual and semi-automatic control.
In most moving-base flight simulators, the simulated aircraft motion needs to be filtered with motion washout filters to keep the simulator within its limited motion envelope. Translational motion in particular requires filtering, as the low-frequency components of the vehicle motion tend to quickly drive simulators toward their motion bounds. Commonly, linear washout filters are therefore used to attenuate the simulated motion in magnitude and in phase. It is found in many studies that the settings of these washout filters affect pilot performance and control behavior. In most of these studies, no comparison to a case with one-to-one motion cues is performed as a result of the limited motion envelope of the simulators used. In the current study, an experiment was performed in the SIMONA Research Simulator at the Delft University of Technology to investigate the effects of heave washout settings on pilot performance and control behavior in a pitch attitude control task. In addition to rotational pitch motion, heave accelerations at the pilot station that result directly from aircraft pitch were evaluated. This heave motion component could be supplied one-to-one in the simulator due to the modest size of the aircraft model, a Cessna Citation I business jet. The experiment revealed that pilot performance and control activity both increased significantly with increasing heave motion fidelity. An analysis of pilot control behavior using pilot models indicated that the enhanced performance was caused by an increase in the magnitude with which pilots responded to visual and physical motion stimuli and a decrease in the amount of visual lead that was generated by the pilots. Nomenclature A = sinusoid amplitude, deg a z cg = c.g. heave acceleration, m s 2 a z = pitch-heave acceleration, m s 2 e = tracking error signal, deg f d = disturbance forcing function, deg f t = target forcing function, deg H nm = neuromuscular system dynamics H ol = open-loop response H sc = semicircular canal dynamics H p e = pilot visual response H p az = pilot heave motion response H p = pilot pitch motion response Hj! = frequency response function Hs = transfer function H ; e = controlled system dynamics j = imaginary unit, -K = gain, -K m = motion perception gain, -K v = visual perception gain, -k = sinusoid index, -l = pitch-heave arm length, m N = number of points, -n = forcing function frequency integer factor, -S = power spectral density s = Laplace variable T I = visual lag time constant, s T L = visual lead time constant, s T sc 1 , T sc 2 , T sc 3 = semicircular canal model time constants, s t = time, s u = pilot control signal, deg z = vertical position, m Symbols e = elevator deflection, deg = damping factor, -nm = neuromuscular damping, -= pitch angle, deg 2 = variance m = motion perception time delay, s v = visual perception time delay, s = sinusoid phase shift, rad ' m = phase margin, deg ! = frequency, rad s 1 ! c = crossover frequency, rad s 1 ! nm = neuromuscular frequency, rad s 1 ! sp = short period frequency, rad s 1 Subscri...
Improved understanding of human adaptation can be used to design better (semi-)automated systems that can support the human controller when task characteristics suddenly change. This paper evaluates the effectiveness of a model-based adaptive control technique, Model Reference Adaptive Control (MRAC), for describing the adaptive control policy used by human operators while controlling a time-varying system in a pursuit-tracking task. Ten participants took part in an experiment in which they controlled a time-varying system whose dynamics changed twice between approximate single and double integrator dynamics, and vice versa. Our proposed MRAC controller is composed of a feedforward and a feedback controller and an internal reference model that is used to drive an adaptive control policy. MRAC's adaptive control gains, the internal model parameters, and the learning rates were estimated from the experiment data using non-linear optimization aimed at maximizing the quality-of-fit of participants' control outputs. Participants' control behavior rapidly changed when the dynamics of the controlled system changed, in particular for transitions from single to double integrator dynamics. The MRAC model was indeed able to accurately capture the transient dynamics exhibited by the participants when the system changed from an approximate single to a double integrator, however, for the opposite transition the MRAC gains were always adapted too slowly. Therefore, in its current form, our MRAC model can be used to approximate human adaptation in pursuit tracking tasks when a change in the dynamics of the controlled system requires significant (rate) feedback controller adaptation to maintain satisfactory closed-loop control performance.
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