The accumulation of ice on an airplane in flight is one of the leading contributing factors to general aviation accidents. To date, only relatively sophisticated methods based on detailed empirical data and flight data exist for its analysis. This paper develops a methodology and simulation tool for preliminary safety and performance evaluations of airplane dynamic response and climb performance in icing conditions. The important aspect of dynamic response sensitivity to pilot control input with the autopilot disengaged is also highlighted. Using only basic mass properties, configuration, propulsion data, and known icing data from a similar configuration, icing effects are applied to the dynamics of a non-real-time, six degree-of-freedom simulation model of a different, but similar, light airplane. Besides evaluating various levels of icing severity, the paper addresses distributed icing which consists of wing alone, horizontal tail alone, and unequal distributions of combined wing and horizontal tail icing. Results presented in the paper for a series of simulated climb maneuvers with various levels and distributions of ice accretion show that the methodology captures the basic effects of ice accretion on pitch response and climb performance, and the sensitivity of the dynamic response to pilot control inputs. Nomenclature= arbitrary stability and control derivative C A iced = arbitrary stability and control derivative with icing effectshe has been with Texas A&M University, where he is Associate Professor of aerospace engineering, and Director of the Flight Simulation Laboratory. He teaches courses on digital control, nonlinear systems, flight mechanics, aircraft design, and coteaches a short course on digital flight control for the University of Kansas Division of Continuing Education. Current research interests include control of morphing air and space vehicles, multi-agent systems, intelligent autonomous control, vision-based navigation systems, and fault-tolerant adaptive control. He is an Associate Fellow of AIAA, past Chairman and current member of the Atmospheric Flight c = mean geometric chord D = carry through matrix f ice = icing factor g = gravitational acceleration h = integration step size I yy = airplane moment of inertia about y axis i = index, imaginary component j = imaginary component k 0 C A = coefficient icing factor constant L = roll angular acceleration M = pitch angular acceleration; modal matrix N = yaw angular acceleration nframes = number of time steps P = airplane body-axis roll rate p = perturbed airplane body-axis roll rate Q = airplane body-axis pitch rate q = perturbed airplane body-axis pitch rate _ q = airplane body-axis pitch acceleration q = dynamic pressure R = airplane body-axis yaw rate r = perturbed airplane body-axis yaw rate S = wing area t = time U = airplane velocity in the body-axis x direction U = control input vector u = stability axis airplane velocity in the x direction _ u = incremental change in the stability axis airplane velocity in the x direction V = airplane...

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Casting the problem of morphing a microair vehicle as a reinforcement-learning problem to achieve desired tasks or performance is a candidate approach for handling many of the unique challenges associated with such small aircraft. This paper presents an early stage in the development of learning how and when to morph a micro air vehicle by developing an episodic unsupervised learning algorithm using the Q-learning method to learn the shape and shape change policy of a single morphing airfoil. Reinforcement is addressed by reward functions based on airfoil properties, such as lift coefficient, representing desired performance for specified flight conditions. The reinforcement learning as it is applied to morphing is integrated with a computational model of an airfoil. The methodology is demonstrated with numerical examples of an NACA type airfoil that autonomously morphs in two degrees of freedom, thickness and camber, to a shape that corresponds to specified goal requirements. Because of the continuous nature of the thickness and camber of the airfoil, this paper addresses the convergence of the learning algorithm given several discretizations. Convergence is also analyzed with three candidate policies: 1) a fully random exploration policy, 2) a policy annealing from random exploration to exploitation, and 3) an annealing discount factor in addition to the annealing policy. The results presented in this paper show the inherent differences in the learned action-value function when the state-space discretization, policy, and learning parameters differ. It was found that a policy annealing from fully explorative to almost fully exploitative yielded the highest rate of convergence as compared to the other policies. Also, the coarsest discretization of the state-space resulted in convergence of the action-value function in as little as 200 episodes.

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Crossover frequency is a parameter often used to characterize the pilot-vehicle system behavior for continuous control tasks with known forcing functions. However, for many tasks, such an input is not known, and in these cases cutoff frequency can be applied. While cutoff frequency is a useful parameter, it does not necessarily reflect pilot effort. Power frequency, derived from cutoff frequency via wavelet analysis, is introduced herein as a parameter that relates the frequency of pilot input with the intensity of that input. Scalogram-based time-varying counterparts to both the cutoff frequency and power frequency show how this relationship evolves through a given task. Both the cutoff and the power frequency are calculated for flight test data from an offset approach and landing task. The pilot ratings recorded for each evaluation are then compared to both the cutoff frequency and power frequency to determine if a correlation exists. Results show that there is significant scatter in the ratings versus cutoff frequency data and a strong correlation between ratings versus pilot input power frequency.pilot describing function Greek = sideslip a = aileron position a l = aileron position limited position ac = aileron position command ac rl = rate limited aileron position command c = surface command s = stick position = roll attitude e = effective system time delay ! = frequency ! c = crossover frequency ! cutoff = cutoff frequency ! G = power frequency = integral of the power spectral density Abbreviations EP = evaluation pilot FFT = fast fourier transform FREDA = frequency domain analysis PIO = pilot induced oscillation PSD = power spectral density RMS = root mean square SP = safety pilot STI = Systems Technology, Inc. VSS = variable stability system