Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

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-in-the-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 is substantial. Some adjustments to the maximum likelihood method are proposed to reduce this bias.

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Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

This article proposes two Kalman-filter-based online identification schemes for permanent magnet synchronous machines (PMSMs), where the nonlinearity of a voltage-source inverter (VSI) is taken into account. One is formulated from an extended Kalman filter; the other uses a dual extended Kalman filter. They are generally formulated and can be applied to any identifiable electrical parameter combinations. The proposed schemes are further implemented on an industrial embedded control system. Their performance tests are conducted on a PMSM under static and dynamic conditions and compared with the extended Kalman filter without VSI nonlinearity compensation. The effectiveness of the proposed approaches is proved by the experimental results. Furthermore, a sensitivity analysis of the initial setup of parameter estimates has shown that the proposed estimators are robust against poor initial value choices. Real-time feasibility of the proposed estimators up to $\text{20}\;\text{kHz}$ is demonstrated via experiments.

General Formulation of Kalman-Filter-Based Online Parameter Identification Methods for VSI-Fed PMSM

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.

Use of Pitch and Heave Motion Cues in a Pitch Control Task

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 semiautomatic control.

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Manual Control Cybernetics: State-of-the-Art and Current Trends

This paper introduces a two-step identification method of human multichannel perception and control. In the first step, frequency response functions are identified using linear time-invariant models. The analytical predictions of bias and variance in the estimated frequency response functions are validated using Monte Carlo simulations of a closed-loop control task and contrasted to a conventional method using Fourier coefficients. For both methods, the analytical predictions are reliable, but the linear time-invariant method has lower bias and variance than Fourier coefficients. It is further shown that the linear time-invariant method is more robust to higher levels of pilot remnant. Finally, both methods were successfully applied to experimental data from closed-loop control tasks with pilots.

Modeling Human Multichannel Perception and Control Using Linear Time-Invariant Models

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 used simulators. In the current study, an experiment was performed in the SIMONA Research Simulator at 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.

Peter M. T. Zaal

Papers

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Modeling Human Multichannel Perception and Control Using Linear Time-Invariant Models

Use of Pitch and Heave Motion Cues in a Pitch Control Task

Manual Control Cybernetics: State-of-the-Art and Current Trends

Effects of Heave Washout Settings in Aircraft Pitch Disturbance Rejection