Unstable Aircraft Research Articles

Longitudinal Aerodynamic Parameter Estimation Using Neural Network and Gauss-Newton Method

A new parameter estimation method based upon Feed Forward Neural Network is proposed. The proposed method utilizes the universal mapping capability of Feed Forward Neural Network to develop flight dynamic model of aircraft. Gauss-Newton method is used to obtain values of aerodynamic parameters by minimizing a chosen error cost function. The method has then been validated using flight data pertaining to longitudinal dynamics of aircraft. Proof of match approach has been followed to verify the estimated model by the proposed method. The results obtained using the proposed method have also been compared with those obtained using wind tunnel tests, and Filter Error method. Unlike, most of the conventional methods, the proposed method does not require a priori description of the model. It also bypasses the requirement of solving the equations of motion. This feature may have special significance in handling flight data of an unstable aircraft.

Unstable Aircraft Parameter Estimation Using Neural Partial Differentiation

An approach based on neural partial differentiation is suggested, to overcome the numerical problems faced by classical methods, for the parameter estimation of an aerodynamically unstable aircraft. Theoretical analysis of the neural modeling, the parameter estimation process, and the nature of the estimates pertaining to unstable aircraft dynamics using the neural partial differential method, are discussed. Equation for the relative standard deviation, which is equivalent to the Cramer-Rao bound in the method like output error approach, is derived using the neural partial differential method and verified through numerical simulation. The aerodynamic derivatives are derived for the simulated and real longitudinal flight data of an unstable aircraft, and the estimates obtained using the neural partial differentiation are compared with the classical methods such as the equation error and the output error methods. The parameter estimates from the simulated noisy data are also presented to assess and support the theoretical developments presented in this paper. The theoretical analysis and the results presented in this paper make the neural partial differential approach more reliable and widely applicable.

Unstable tilt-rotor maximum likelihood wavelet-based identification from flight test data

PurposeThe purpose of this paper is to present the methodology that was used to perform system identification of a dynamically unstable tilt-rotor from flight test data. The method incorporated wavelet transform into the maximum likelihood principle formulation, emphasizing both time and frequency responses. Using wavelets allowed to additionally filter noise in the data, and this increased the estimation quality. This approach did not require measurement and process noise modeling in contrast to the Kalman filter usage for parameter estimation.Design/methodology/approachIn the study, lateral-directional stability and control derivatives of an unstable tiltrotor in hover were estimated. This was performed by applying the maximum likelihood output error method. The estimated model response was decomposed using the Mallat pyramid and matched to wavelet coefficients obtained directly from measurements. In addition, a coherence-based weighting function was used to put more emphasis on the most reliable data. For comparison, the same set of data was used to identify a model with the same structure using the maximum likelihood principle with an incorporated Kalman filter.FindingsIt was found that maximum likelihood principle and wavelet transform allowed for estimating aerodynamic coefficients of a dynamically unstable aircraft. The estimation was performed with high accuracy.Practical implicationsThe designed method can be used for system identification of unstable aircraft and when additional noise is present (e.g. when noise due to turbulence was observable during the flight test or higher noise levels were present in the sensors data).Originality/valueThe paper presents verification of a wavelet-based maximum likelihood principle output error method using flight test data.

Incremental Dual Heuristic Dynamic Programming Based Hybrid Approach for Multi-Channel Control of Unstable Tailless Aircraft

Actor-critic based online reinforcement learning control has been proved to be promising method for control of aerial vehicles. However, it is difficult to guarantee high-level success rate of initial training and to tune the large amount of parameters for actors and critics considering unstable multi-input and multi-output (MIMO) aircraft. In order to facilitate and simplify the training of the actor and critic for unstable aircraft, classic stability augmentation system (SAS) is designed for the open-loop aircraft first. Then the online incremental model based dual heuristic dynamic programming (IDHP) method, which has been proposed recently, is extended in application to design a multi-channel robust adaptive controller, and MIMO form network structures are designed and determined for the actors and critics considering the three-channel coupling issues. Consequently, the classic SAS and the IDHP controller make up a novel hybrid control framework. In this control framework, the SAS takes charge of counteracting the unstable eigenvalues of the open-loop aircraft system, and the IDHP takes charge on guaranteeing robust and adaptive performance for high-performance tailless aircraft equipped with the SAS. Specifically, the introduction of the classic control method decreases the difficulty of the initial training for multi-channel IDHP controller. The tuning process for initial parameters of actor and critic neural networks in multiple channels is greatly facilitated. Without the help of SAS, the initial training for multi-channel IDHP controllers of unstable plants is almost impossible to succeed. Finally, the novel hybrid control architecture and method are validated using the Innovative Control Effectors (ICE) model, which has unstable modes in the longitudinal dynamics. Typical aerodynamic model uncertainties are numerically simulated to demonstrate the effectiveness of the proposed control method.

Anti-windup-like Compensator Synthesis for Discrete-Time Quantized Control Systems

This paper addresses the problem of designing an anti-windup like compensator for discrete-time linear control systems with quantized input. The proposed compensator provides a correction signal proportional to the quantization error that is fed to the controller. The compensator is designed to ensure that solutions to the closed-loop systems converge in finite time into a compact set containing the origin that can be tuned by the designer. A numerically tractable algorithm with feasibility guarantees is provided for the design of the compensator. The proposed results are illustrated on an academic example and an open-loop unstable aircraft system.

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

PurposeThe purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm.Design/methodology/approachFirstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives.FindingsNeither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data.Research limitations/implicationsThe proposed method requires iterative calculation and can only identify parameters offline.Practical implicationsThe proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems.Originality/valueIn this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

Nonstandard use of anti-windup loop for systems with input backlash

Control systems with backlash at the input are considered in this paper. The goal of this work is to characterize the attractor of such nonlinear dynamical systems, and to design anti-windup inspired loops such that the system is globally asymptotically stable with respect to this attractor. The anti-windup loops affect the dynamics of the controllers, and allow to increase the performance of the closed-loop systems. Different performance issues are considered throughout the paper such as robustness with respect to uncertainty in the backlash, and L2 gain when external disturbances affect the dynamics. Numerically tractable algorithms with feasibility guarantee are provided, as soon as the linear closed-loop system, obtained by neglecting the backlash effect, is asymptotically stable. The results are illustrated on an academic example and an open-loop unstable aircraft system.

Observer based fault reconstruction schemes using terminal sliding modes

ABSTRACT Two state/fault estimation methods using terminal sliding mode (TSM) concepts are presented in this paper. In contrast with conventional sliding modes, which guarantee asymptotic convergence of non-output estimation errors and faults, TSMs enable finite time convergence of estimation errors for faults and all the states. The minimum-phase condition, as a common condition required for fault estimation, is released in the proposed methods. Method I implements fractional power of the so-called switching term to make it robust against matched faults and disturbances. Compared with previous terminal scheme, this method covers a wider class of systems. In Method II, fractional power sliding variable is considered to achieve finite time convergence of estimation errors and their derivatives. In contrast with Method I, this approach is also robust against unmatched faults. Finally, the methods are applied to an unstable aircraft model.

On Hybrid Creativity

This article reviews the development of the author’s computational art practice, where the computer is used both as a device that provides the medium for generation of art (‘computer as art’) as well as acting actively as an assistant in the process of creating art (‘computer as artist’s assistant’), helping explore the space of possibilities afforded by generative systems. Drawing analogies with Kasparov’s Advanced Chess and the deliberate development of unstable aircraft using fly-by-wire technology, the article argues for a collaborative relationship with the computer that can free the artist to more fearlessly engage with the challenges of working with emergent systems that exhibit complex unpredictable behavior. The article also describes ‘Species Explorer’, the system the author has created in response to these challenges to assist exploration of the possibilities afforded by parametrically driven generative systems. This system provides a framework to allow the user to use a number of different techniques to explore new parameter combinations, including genetic algorithms, and machine learning methods. As the system learns the artist’s preferences the relationship with the computer can be considered to change from one of assistance to collaboration.

Fault Estimation Filter Design With Guaranteed Stability Using Markov Parameters

For additive actuator and sensor faults, we propose a systematic method to design a state-space fault estimation filter directly from Markov parameters identified from fault-free data. We address this problem by parameterizing a system-inversion-based fault estimation filter with the identified Markov parameters. Even without building an explicit state-space plant model, our novel approach still allows the filter gain design for stabilization and suboptimal $\mathcal{H}_2$ performance. This design freedom cannot be achieved by other existing data-driven fault estimation filter designs so far. Another benefit of our proposed design is the convenience of determining the state order: a higher state order of the filter leads to better estimation performance, at the cost of heavier computational burden. In contrast, order determination is cumbersome when using an identified state-space plant model for the filter design, because of the complicated propagation of the model mismatch into the fault estimation errors. Simulations using an unstable aircraft system illustrate the effectiveness of the proposed new method.

Accelerated Flight Control LAW Clearance Using Stores Grouping Concept

Current fighter aircraft are demanded to feature multirole capability with large number of different external stores to be integrated on the aircraft. These stores represent air to air, air to surface weapons etc., which have wide range of aerodynamic characteristics as well as different mass properties (CG, mass, inertia). This conflicts with the requirement of adequate stability, optimum handling qualities, superior agility and robustness characteristics over the entire flight envelope. Especially with an unstable aircraft it is impossible to fulfill these requirements for all stores with just one set of control laws. Therefore, automatic reconfiguration in control laws based on similar flight dynamic characteristics is carried out. Stores grouping concept is often used to reconfigure the flight control laws with store information in order to normalize/optimize the flight dynamics characteristics and corresponding support towards flight clearance. The flight clearance is a tedious procedure as a host of criteria need to be evaluated at a large number of nominal flight conditions for different airframe configurations, multiples thereof with tolerances and failures. This paper describes how various stores configurations can be grouped together in order to accelerate the clearance process.

Parameter Estimation of Unstable Aircraft using Extreme Learning Machine

<p class="p1">The parameter estimation of unstable aircraft using extreme learning machine method is presented. In the past, conventional methods such as output error method, filter error method, equation error method and non-conventional method such as artificial neural-network based methods have been used for aircraft’s aerodynamic parameter estimation. Nowadays, a trend of finding an accurate nonlinear function approximation is required to represent the aircraft’s equations-of-motion. Such type of nonlinear function approximation is usually achieved using artificial neural-network which is trained with the aircraft input-output flight data using a training algorithm. The accuracy of estimated parameters, which is achieved using the trained network, is highly dependent on the generalisation capability of the network which can be improved using extreme learning machine based network in contrast to artificial neural-network. To estimate the unstable aircraft parameters from the simulated flight data, Gauss-Newton based optimisation method has been used with a predefined aerodynamic model using the trained network. Further, the confidence of the estimated parameters has been shown in comparison to that of the standard parameter estimation methods in terms of the Cramer-Rao bounds.</p>

Robust observer-based control of nonlinear dynamical systems with state constraints

In this paper, we design a variable structure observer-based control system that guarantees asymptotic convergence of the plant’s trajectory to the equilibrium point despite matched and unmatched uncertainties in the plant dynamics. Our control laws are functions of the estimated plant state and the proposed framework allows employing any estimator or observer, such as the Walcott and Żak observer, as long as the estimated state converges asymptotically to the plant state. Barrier Lyapunov functions guarantee that the closed-loop system’s trajectory verifies the state constraints. This study is the first of its kind, since recently variable structure control architectures have been adapted to account for constraints on the state space or allow output-feedback, but observer-based variable structure control in the presence of state constraints has not been attempted before. A numerical simulation involving the roll dynamics of an unstable aircraft, whose aerodynamic coefficients are unknown, illustrates our theoretical framework.

Robust kernel-based model reference adaptive control for unstable aircraft

In this article, a robust kernel-based model reference adaptive control is proposed for an unstable nonlinear aircraft. The heart of the proposed kernel-based model reference adaptive control scheme comprises an offline neural identifier and an online neural controller. In the offline neural identifier, the kernel-based unified extreme learning machine algorithm is used to identify the aircraft model with the available input–output data in a finite time interval. The finite time interval is selected to avoid the response of the unstable aircraft growing unbounded. In the kernel-based unified extreme learning machine, the hidden layer feature mapping is determined by the kernel matrix. However, the unified extreme learning machine is a batch learning algorithm and is not suitable for the online control learning. To solve the problem, a recursive version of the unified extreme learning machine is developed in this study. Based on a given reference model and the identified model, the recursive version of the unified extreme learning machine algorithm is applied to construct the online control law to compensate for the changes in the aircraft dynamics or characteristics. The performance of the proposed kernel-based model reference adaptive control scheme is validated through the simulation studies of a locally nonlinear longitudinal high-performance aircraft. Simulation studies are also compared with a model reference adaptive control based on the back-propagation algorithm and a model reference adaptive control based on the basic extreme learning machine algorithm in terms of the identification and tracking abilities. The results show that the proposed kernel-based model reference adaptive control can achieve better identification and tracking performance.

Data-driven sensor fault estimation filter design with guaranteed stability

We propose a systematic method to directly identify a sensor fault estimation filter from plant input/output data collected under fault-free condition. This problem is challenging, especially when skipping the step of building an explicit state-space plant model in data-driven design, because the inverse of the underlying plant dynamics is required and needs to be stable. We show that it is possible to address this problem by relying on a system-inversion-based fault estimation filter that is parameterized using identified Markov parameters. Our novel data-driven approach improves estimation performance by avoiding the propagation of model reduction errors originating from identification of the state-space plant model into the designed filter. Furthermore, it allows additional design freedom to stabilize the obtained filter under the same stabilizability condition as the existing model-based system inversion. This crucial property enables its application to sensor faults in unstable plants, where existing data-driven filter designs could not be applied so far due to the lack of such stability guarantees (even after stabilizing the closed-loop system). A numerical simulation example of sensor faults in an unstable aircraft system illustrates the effectiveness of the proposed new method.

Nonlinear and Linear Unstable Aircraft Parameter Estimations Using Neural Partial Differentiation

To overcome the numerical problems faced by conventional methods for the parameter estimation of linear and nonlinear unstable aircraft dynamics, an approach based on neural partial differentiation is proposed. An equation for the relative standard deviation, which is equivalent to the Cramer–Rao bound in methods such as output error approach, is derived for the statistical analysis of the estimates obtained using the neural partial differentiation approach and verified through numerical simulation. The neural architecture to be used for the parameter estimation is also discussed. It is shown that the neural partial differentiation approach is better than the least-squares approach for noisy data. The longitudinal axis stability and control derivatives are obtained for simulated data, with and without noise for an unstable aircraft, using the neural partial differentiation approach and compared with the least-squares and stabilized output error methods. Moreover, the longitudinal axis stability and control derivatives estimated from actual flight data of the unstable X-31A aircraft are compared with the least-squares and stabilized output error approaches, proving the efficacy and superiority of the proposed neural approach.

Robust trajectory tracking of unstable aircraft with measurement delay

Trajectory tracking control is investigated for an open-loop unstable aircraft with delayed external measurement in this paper. The bounded measurement delay is unknown and variant. The trajectory signal is generated by a reference system. By using a Lyapunov–Krasovskii functional and linear matrix inequality technique, a delay-dependent criterion and the corresponding robust trajectory tracking control scheme are presented. The controller can reduce the undesirable delay effects and guarantee the boundedness of tracking error. The results with application to HiMAT (highly maneuverable technology) aircraft have demonstrated the effectiveness of the proposed approach.

In-flight Estimation of the Aerodynamic Roll Damping and Trim Angle for a Tethered Aircraft based on Multiple-shooting

The Airborne Wind Energy paradigm proposes to generate energy by flying a tethered aircraft across the wind flow. Accurate models for tethered flight are essential for the control and optimization of airborne wind energy systems. This paper proposes an estimation of the aerodynamic roll damping of a tethered aircraft based on Inertial Measurement Unit data only, gathered at a high aircraft angular velocity. The resulting system dynamics are nonlinear and the estimation problem is non-convex. Because the aircraft is subject to disturbances, the perturbations are estimated alongside the uncertain parameters. In order to handle the unstable aircraft dynamics, a discretization of the model equations based on multiple-shooting is proposed.

Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method

AbstractA new parameter estimation method based upon neural network is proposed. The method proposed here uses feed forward neural networks to establish a neural model that could be used to predict subsequent time histories given the suitable measured initial conditions. The proposed neural model would not represent a generic flight dynamic model. The neural model in this case develops point to point fitting of the input and the output data. Thus, it could at best be referred to as flight dynamic model in restricted sense. Gauss-Newton method is then used to obtain optimal values of the aerodynamic parameters by minimising a suitable defined error cost function. The method has been validated using longitudinal and lateral-directional flight data of various test aircraft. The results thus obtained were compared with those obtained through wind tunnel test, or those obtained using Maximum likelihood and/or Filter error methods. Unlike, most of the parameter estimation methods, the proposed method does not require a prior description of the model. It also bypasses the requirement of solving equations of motion. This feature of the proposed method may have special significance in handling flight data of an unstable aircraft.

ℒ 1 adaptive controller for tailless unstable aircraft in the presence of unknown actuator failures

This article considers application of ℒ1 adaptive controller to multi-input multi-output open loop unstable unmanned military aircraft. The control is designed to accommodate and to be robust to actuator failures as well as to pitch break uncertainty, which is used to model uncertain aerodynamics. Results of using the ℒ1 adaptive controller are compared with the conventional model reference adaptive control scheme to show improved transient command tracking as well as guaranteed time-delay margin.