Luiz Carlos Sandoval Góes

Adaptive Stochastic Filtering for Online Aircraft Flight Path Reconstruction

State and parameter estimation using flight test data is highly affected by process and measurement noises, especially with noises displaying time varying statistical properties. Hence, if an estimation problem is to be solved, an adaptive filtering approach is recommended. It is also desirable to obtain the estimates online, simultaneously with flight execution, aiming at a maneuver validation before concluding the flight. Indeed, it is more expensive to put the aircraft back in the air than to extend a little the flight and repeat a test point. Flight path reconstruction is a technique which produces a consistent flight test data set from noisy measurements as a preprocessing scheme to a parameter identification routine. Air data can also be calibrated simultaneously if the problem is formulated properly. This work proposes a methodology to deal with time varying noise statistical properties using a new approach for an adaptive extended Kalman filter. Besides the main filter, two other Kalman filters are proposed to run in parallel, to estimate the process and measurement noise statistics based on the main filter residuals. The proposed adaptive method is derived from the covariance matching technique, by employing filter residuals to adjust the noise statistical properties. Because the method has a low computational cost and is recursive, it is suitable for online applications. The method is validated in a flight path reconstruction application, with simultaneous air data calibration for angle of attack, angle of sideslip, and static pressure sensors. A 100 samples Monte Carlo simulation and real flight test data analysis are used for performance evaluation. Because the proposed approach adequately estimates the statistical noise properties, improved performance is obtained.

Results of Short-Period Helicopter System Identification Using Output-Error and Hybrid Search-Gradient Optimization Algorithm

This article focuses on the problem of parameter estimation of the uncoupled, linear, short-period aerodynamic derivatives of a “Twin Squirrel” helicopter in level flight and constant speed. A flight test campaign is described with respect to maneuver specification, flight test instrumentation, and experimental data collection used to estimate the aerodynamic derivatives. The identification problem is solved in the time domain using the output-error approach, with a combination of Genetic Algorithm (GA) and Levenberg-Marquardt optimization algorithms. The advantages of this hybrid GA and gradient-search methodology in helicopter system identification are discussed.

Flight path reconstruction and parameter estimation using output-error method

This work describes the application of the output-error method using the Levenberg-Marquardt optimization algorithm to the Flight Path Reconstruction (FPR) problem, which constitutes an important preliminary step towards the aircraft parameter identification. This method is also applied to obtain the aerodynamic and control derivatives of a regional jet aircraft from flight test data with measurement noise and bias. Experimental results are reported, employing a real jet aircraft, with flight test data acquired by smart probes, inertial sensors (gyrometers and accelerometers) and Global Positioning Systems (GPS) receivers.

Aircraft Parameter Estimation Experiment Design Considering Measurement Colored Residuals

This work deals with the optimization of ∞ight test maneuvers for aerodynamic parameter estimation considering that the measurements are contaminated with colored residuals. The colored residuals consideration is important to give the direct and realistic assessment of the parameter estimation uncertainty levels prior to ∞ight tests. The optimization technique is based on the concept of ∞ight test data information content and Cramer-Rao lower bound. The discrete autocorrelation matrix of the measurement noise is used in order to compose the optimization criteria considering colored residuals. Some results of a ∞ight test campaign of the CEA-205 CB.9 Curumim aircraft are discussed. The advantages of the proposed maneuvers optimization technique are presented, stressing the easiness of implementation of the signals and the strong improvement in the estimation procedures made possible with the application of the optimized maneuver signals.

Aircraft parameter estimation using output-error methods

Certification requirements, optimization and minimum project costs, design of flight control laws and the implementation of flight simulators are among the principal applications of inverse problem applications in the aeronautical industry. The problem of aircraft identification and parameter estimation demands for accurate mathematical model of the aerodynamics and adequate experimental flight data gathering and processing. The aircraft dynamic modeling is characterized by aerodynamic and control derivatives whose values can be directly determined from flight test data. This work describes the application of the output-error method using the Nelder–Mead (NM) and Levenberg–Marquardt (LM) algorithms to obtain the aerodynamic and control derivatives of a regional jet aircraft. Unlike others identification methods based on equation-error the output-error method gives unbiased parameter estimation in the presence of measurement noise. In this work, experimental results for estimation of the lateral directional aerodynamic derivatives, using flight test data provided by EMBRAER, are presented.

Positional Control of a Flexible Structure using Neural Networks

This paper presents an adap tive inverse c ontrol approach for the positional control of an unconstrained multibody system with flexible appendages. The approach is called Feedback-ErrorLearning and it i s based on the output of a feedback controller with fixed pa rameters to adap t a n eural network which a cts as a feedforward controller. The results are demonstrated b y simulations using a high fidelity dynamic model of a experimental setup available at the ITA-IEMP Dynamics Laboratory.

Control of Limit Cycle Oscillation in a Three Degrees of Freedom Airfoil Section Using Fuzzy Takagi-Sugeno Modeling

This work presents a strategy to control nonlinear responses of aeroelastic systems with control surface freeplay. The proposed methodology is developed for the three degrees of freedom typical section airfoil considering aerodynamic forces from Theodorsen’s theory. The mathematical model is written in the state space representation using rational function approximation to write the aerodynamic forces in time domain. The control system is designed using the fuzzy Takagi-Sugeno modeling to compute a feedback control gain. It useds Lyapunov’s stability function and linear matrix inequalities (LMIs) to solve a convex optimization problem. Time simulations with different initial conditions are performed using a modified Runge-Kutta algorithm to compare the system with and without control forces. It is shown that this approach can compute linear control gain able to stabilize aeroelastic systems with discontinuous nonlinearities.

Aircraft longitudinal stability and control derivatives identification by using life cycle and Levenberg–Marquardt optimization algorithms

In this work, two optimization algorithms are investigated to accomplish the parameter identification of the longitudinal motion of a real aircraft by using the output error method. The first algorithm is the nature-inspired algorithm named the life cycle model, which is a composed strategy based on other heuristics such as genetic algorithms and particle swarm optimization. The second one is the gradient-based technique named Levenberg–Marquardt algorithm, which is a variant of the Gauss–Newton method. Flight test data, performed with a training jet aircraft (Xavante AT-26), were used to feed the output error method. In this context, both optimization algorithms were tested, in solo performance and in a cascade-type approach. Results are reported, aiming to illustrate the success of using the proposed methodology.

Identification of a Non-Linear Landing Gear Model Using Nature-Inspired Optimization

This work deals with the application of a nature-inspired optimization technique to solve an inverse problem represented by the identification of an aircraft landing gear model. The model is described in terms of the landing gear geometry, internal volumes and areas, shock absorber travel, tire type, and gas and oil characteristics of the shock absorber. The solution to this inverse problem can be obtained by using classical gradient-based optimization methods. However, this is a difficult task due to the existence of local minima in the design space and the requirement of an initial guess. These aspects have motivated the authors to explore a nature-inspired approach using a method known as LifeCycle Model. In the present formulation two nature-based methods, namely the Genetic Algorithms and the Particle Swarm Optimization were used. An optimization problem is formulated in which the objective function represents the difference between the measured characteristics of the system and its model counterpart. The polytropic coefficient of the gas and the damping parameter of the shock absorber are assumed as being unknown: they are considered as design variables. As an illustration, experimental drop test data, obtained under zero horizontal speed, were used in the non-linear landing gear model updating of a small aircraft.

The use of Gramian matrices for aeroelastic stability analysis

Most of the established procedures for analysis of aeroelastic flutter in the development of aircraft are based on frequency domain methods. Proposing new methodologies in this field is always a challenge, because the new methods need to be validated by many experimental procedures. With the interest for new flight control systems and nonlinear behavior of aeroelastic structures, other strategies may be necessary to complete the analysis of such systems. If the aeroelastic model can be written in time domain, using state-space formulation, for instance, then many of the tools used in stability analysis of dynamic systems may be used to help providing an insight into the aeroelastic phenomenon. In this respect, this paper presents a discussion on the use of Gramian matrices to determine conditions of aeroelastic flutter. The main goal of this work is to introduce how observability gramian matrix can be used to identify the system instability. To explain the approach, the theory is outlined and simulations are carried out on two benchmark problems. Results are compared with classical methods to validate the approach and a reduction of computational time is obtained for the second example.