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Vmcg, or ground minimum control speed, is established by aircraft manufacturers during the aircraft certification process. Vmcg is used as a limiting speed for V1 (decision speed) when performing takeoff performance calculations. Performance calculations on contaminated and slippery runways will result in a V1 speed equal to Vmcg-limited V1 for a wide range of takeoff weights when using aircraft manufacturer procedures in a flight crew operationsmanual or computer calculations based on theV1−min policy. In this paper, itwill be shown thatVmcg will not be a safe speed to continue a takeoff after an engine failure in strong crosswind or reduced runway surface friction conditions. A model is used to determine the effect of these environmental conditions on lateral deviation. Apart from the continued takeoff, the lateral deviation in the rejected takeoff after an engine failure was also calculated under different environmental conditions. This resulted in advice for the use of a differential braking technique to prevent a runway excursion if a runway is not dry. A method to mitigate the risk of runway excursion on contaminated and slippery runways is presented. An evaluation, conclusions, and subjects for further research are also presented., Control & Simulation

When an aircraft experiences an engine failure during takeoff, it must be able to either reject or continue the takeoff without exceeding the longitudinal or lateral dimensions of the usable runway. This paper focuses on the lateral deviation. During certification, the minimum control speed ground is determined in certification tests; at this speed, the allowable maximum lateral deviation is 30 ft. These tests are done with a free castering nose wheel and other requirements such as not using ailerons. These are all V mcg increasing factors. On the other hand, the aircraft manufacturer chooses the most favorable conditions, new tires, a dry runway, and no crosswind. It is unclear if the free castering nose wheel is a stringent-enough requirement to compensate for reduced runway friction, and the effect of crosswind is not considered in the certification tests. Furthermore, the rejected takeoff condition is not certified against a lateral excursion limit. Therefore, in this paper, a model is developed to determine the lateral deviation in a continued as well as a rejected takeoff, including the effect of pilot reaction time, runway surface condition, and crosswind. For the present evaluation, a Boeing 737-300 model was used., Control & Simulation

When an aircraft experiences an engine failure during takeoff, it must be able to either reject or continue the takeoff without exceeding the longitudinal or lateral dimensions of the usable runway. This paper focuses on the lateral deviation. During certification, the minimum control speed ground is determined in certification tests; at this speed, the allowable maximum lateral deviation is 30 ft. These tests are done with a free castering nose wheel and other requirements such as not using ailerons. These are all V mcg increasing factors. On the other hand, the aircraft manufacturer chooses the most favorable conditions, new tires, a dry runway, and no crosswind. It is unclear if the free castering nose wheel is a stringent-enough requirement to compensate for reduced runway friction, and the effect of crosswind is not considered in the certification tests. Furthermore, the rejected takeoff condition is not certified against a lateral excursion limit. Therefore, in this paper, a model is developed to determine the lateral deviation in a continued as well as a rejected takeoff, including the effect of pilot reaction time, runway surface condition, and crosswind. For the present evaluation, a Boeing 737-300 model was used., Control & Simulation

Vmcg, or ground minimum control speed, is established by aircraft manufacturers during the aircraft certification process. Vmcg is used as a limiting speed for V1 (decision speed) when performing takeoff performance calculations. Performance calculations on contaminated and slippery runways will result in a V1 speed equal to Vmcg-limited V1 for a wide range of takeoff weights when using aircraft manufacturer procedures in a flight crew operationsmanual or computer calculations based on theV1−min policy. In this paper, itwill be shown thatVmcg will not be a safe speed to continue a takeoff after an engine failure in strong crosswind or reduced runway surface friction conditions. A model is used to determine the effect of these environmental conditions on lateral deviation. Apart from the continued takeoff, the lateral deviation in the rejected takeoff after an engine failure was also calculated under different environmental conditions. This resulted in advice for the use of a differential braking technique to prevent a runway excursion if a runway is not dry. A method to mitigate the risk of runway excursion on contaminated and slippery runways is presented. An evaluation, conclusions, and subjects for further research are also presented., Control & Simulation

Avoiding high computational loads is essential to online aerodynamic model identi- fication algorithms, which are at the heart of any model-based adaptive flight control system. Multivariate simplex B-spline (MVSB) methods are excellent function approximation tools for modeling the nonlinear aerodynamics of high performance aircraft. However, the computational efficiency of the MVSB method must be improved in order to enable real-time onboard applications, for example in adaptive nonlinear flight control systems. In this paper, a new recursive sequential identification strategy is proposed for the MVSB method aimed at increasing its computational efficiency, thereby allowing its use in onboard system identification applications. The main contribution of this new method is a significant reduction of computational load for large scale online identification problems as compared to the existing MVSB methods. The proposed method consists of two sequential steps for each time interval, and makes use of a decomposition of the global problem domain into a number of subdomains, called modules. In the first step the B-coefficients for each module are estimated using a least squares estimator. In the second step the local B-coefficients for each module are then smoothened into a single global B-coefficient vector using a linear minimum mean square errors (LMMSE) estimation. The new method is compared to existing batch and recursive MVSB methods in a numerical experiment in which an aerodynamic model is recursively identified based on data from an NASA F-16 wind-tunnel model., Control & Operations, Aerospace Engineering

Avoiding high computational loads is essential to online aerodynamic model identi- fication algorithms, which are at the heart of any model-based adaptive flight control system. Multivariate simplex B-spline (MVSB) methods are excellent function approximation tools for modeling the nonlinear aerodynamics of high performance aircraft. However, the computational efficiency of the MVSB method must be improved in order to enable real-time onboard applications, for example in adaptive nonlinear flight control systems. In this paper, a new recursive sequential identification strategy is proposed for the MVSB method aimed at increasing its computational efficiency, thereby allowing its use in onboard system identification applications. The main contribution of this new method is a significant reduction of computational load for large scale online identification problems as compared to the existing MVSB methods. The proposed method consists of two sequential steps for each time interval, and makes use of a decomposition of the global problem domain into a number of subdomains, called modules. In the first step the B-coefficients for each module are estimated using a least squares estimator. In the second step the local B-coefficients for each module are then smoothened into a single global B-coefficient vector using a linear minimum mean square errors (LMMSE) estimation. The new method is compared to existing batch and recursive MVSB methods in a numerical experiment in which an aerodynamic model is recursively identified based on data from an NASA F-16 wind-tunnel model., Control & Operations, Aerospace Engineering

The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an algorithm, namely improved recursive reduced least squares support vector regression (IRR-LSSVR), was proposed for establishing a global nonparametric offline model, which demonstrates significant advantage in choosing representing and fewer support vectors compared with others. Inspired by the IRR- LSSVR, a new adaptive parametric kernel method called WV-LSSVR is proposed in this paper using the same type of kernels and the same centers as those used in the IRR-LSSVR. Furthermore, inspired by the multikernel semiparametric support vector regression, the effect of the kernel extension is investigated in a recursive regression framework, and a recursive kernel method called GPK-LSSVR is proposed using a compound type of kernels which are recommended for Gaussian process regression. Numerical experiments on benchmark data sets confirm the validity and effectiveness of the presented algorithms. The WV-LSSVR algorithm shows higher approximation accuracy than the recursive parametric kernel method using the centers calculated by the k-means clustering approach. The extended recursive kernel method (i.e. GPK-LSSVR) has not shown advantage in terms of global approximation accuracy when validating the test data set without real-time updation, but it can increase modeling accuracy if the real-time identification is involved., Control & Operations, Aerospace Engineering

This work concentrates on the development of an optimization technique which is capable of minimizing the noise impact of an arriving aircraft by optimizing its flight trajectory. Actions needed from pilots to gradually establish the landing configuration are considered because it is expected that the pilot workload throughout this phase should be remained or even reduced compared with the current standard arrival procedures. Therefore, the conventional point-mass equations of motion are reformulated in such a way that the variations of the aerodynamic performance of the aircraft of different configurations can be easily taken into account. A set of independent state variables are chosen to be parameterized with Bernstein polynomials in order to convert the infinite-dimensional optimal control problem into a finite-dimensional parametric optimization problem. The number of awakenings is selected as the performance index and finally written into a function of the parameters introduced by the parameterization process. Genetic algorithms are employed to optimize these parameters within a search domain in order to minimize the number of awakenings while satisfying all constraints on both state and control variables. A number of numerical examples, for a Boeing 747-400 aircraft arriving at an airport with different population distribution situations, are provided to demonstrate the feasibility and effectiveness of the proposed optimization technique. Without loss of generality, this particular technique is also able to deal with a departing aircraft since most of the models are built into replaceable modules., Control & Operations, Aerospace Engineering

The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an algorithm, namely improved recursive reduced least squares support vector regression (IRR-LSSVR), was proposed for establishing a global nonparametric offline model, which demonstrates significant advantage in choosing representing and fewer support vectors compared with others. Inspired by the IRR- LSSVR, a new adaptive parametric kernel method called WV-LSSVR is proposed in this paper using the same type of kernels and the same centers as those used in the IRR-LSSVR. Furthermore, inspired by the multikernel semiparametric support vector regression, the effect of the kernel extension is investigated in a recursive regression framework, and a recursive kernel method called GPK-LSSVR is proposed using a compound type of kernels which are recommended for Gaussian process regression. Numerical experiments on benchmark data sets confirm the validity and effectiveness of the presented algorithms. The WV-LSSVR algorithm shows higher approximation accuracy than the recursive parametric kernel method using the centers calculated by the k-means clustering approach. The extended recursive kernel method (i.e. GPK-LSSVR) has not shown advantage in terms of global approximation accuracy when validating the test data set without real-time updation, but it can increase modeling accuracy if the real-time identification is involved., Control & Operations, Aerospace Engineering

This work concentrates on the development of an optimization technique which is capable of minimizing the noise impact of an arriving aircraft by optimizing its flight trajectory. Actions needed from pilots to gradually establish the landing configuration are considered because it is expected that the pilot workload throughout this phase should be remained or even reduced compared with the current standard arrival procedures. Therefore, the conventional point-mass equations of motion are reformulated in such a way that the variations of the aerodynamic performance of the aircraft of different configurations can be easily taken into account. A set of independent state variables are chosen to be parameterized with Bernstein polynomials in order to convert the infinite-dimensional optimal control problem into a finite-dimensional parametric optimization problem. The number of awakenings is selected as the performance index and finally written into a function of the parameters introduced by the parameterization process. Genetic algorithms are employed to optimize these parameters within a search domain in order to minimize the number of awakenings while satisfying all constraints on both state and control variables. A number of numerical examples, for a Boeing 747-400 aircraft arriving at an airport with different population distribution situations, are provided to demonstrate the feasibility and effectiveness of the proposed optimization technique. Without loss of generality, this particular technique is also able to deal with a departing aircraft since most of the models are built into replaceable modules., Control & Operations, Aerospace Engineering

The validation of aerodynamic models created using flight test data is a time consuming and often costly process. In this paper a new method for the validation of global nonlinear aerodynamic models based on multivariate simplex splines is presented. This new method uses the unique properties of the multivariate simplex splines, a recent type of of multivariate spline, to speedup the process of aerodynamic model validation. Multivariate simplex splines are defined on non-rectangular domains and can be used to accurately fit scattered nonlinear datasets in any number of dimensions. The simplex splines consist of piecewise defined, ordinary multivariate polynomials with a predefined continuity between neighboring polynomial pieces. A recent method for nonlinear system identification based on multivariate simplex splines was used to create a global nonlinear aerodynamic model of the Cessna Citation II laboratory aircraft operated by the Delft University of Technology. In this paper, the multivariate spline based aerodynamic model for the pitching moment coefficient will be validated using both a model residual analysis as well as a statistical model quality analysis. It will be demonstrated that these new analysis methods, which are both unique to the multivariate simplex splines, provide a highly efficient and powerful new method for aerodynamic model validation., Control & Operations, Aerospace Engineering

Trajectory optimization has been a large field of research for many years. The drawback is that for non-convex, constrained problems practically all available solvers cannot guarantee that the globally optimal trajectory is found. Interval analysis based solvers however can provide this guarantee. Interval analysis has been applied to trajectory optimization before, but the previously presented methods suffered from major drawbacks which limited their application to small scale problems. In this paper a new interval based method is introduced which incorporates state parameterization to prevent explicit integration. The performance of the proposed method is demonstrated by applying it to a spacecraft formation flying optimization problem. The results are compared with a gradient based solver and it is shown that the interval method is guaranteed to find the global optimal solution. Finally the first steps for another new trajectory optimization method based on interval analysis and direct collocation are presented., Control & Operations, Aerospace Engineering

The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able to perform online model identification and bounded model extrapolation in the framework of a model-based control system. A new type of linear constraints, the differential constraints, are used as differential boundary conditions within the recursive estimator which limit polynomial divergence when extrapolating data. The differential constraints are derived with a new, one-step matrix form of the de Casteljau algorithm, which reduces their formulation into a single matrix multiplication. The recursive estimator is demonstrated on a bivariate dataset, where it is shown to provide a speedup of two orders of magnitude over an ordinary least squares batch method. Additionally, it is demonstrated that inclusion of differential constraints in the least squares optimization scheme can prevent polynomial divergence close to edges of the model domain where local data coverage may be insufficient, a situation often encountered with global recursive data approximation., Control & Operations, Aerospace Engineering

In this paper nonlinear adaptive flight control laws based on the backstepping approach are proposed which are applicable to over-actuated nonlinear systems. Instead of solving the control allocation exactly, update laws for the desired control effector signals are defined such that they converge to the optimal solution. Stability and boundedness of the resulting closed-loop system can be shown by means of Lyapunov analysis. Three different update laws are defined, the integrated, modular and composite adaptive designs. The last can be seen as a combination between the first two and has the best convergence and numerical properties. Second-order actuator dynamics are taken into account in the control designs. The control design is evaluated using numerical simulations where several cases of locked control surface failures are considered during two different maneuvers. No sensor information about these failures is fed back to the control system. The tracking performance of the adaptive control design is excellent for the nominal case and all considered failure cases. The failures are recognized shortly after they are introduced into the system, and the new dynamics are rapidly identified., Control & Operations, Aerospace Engineering

The validation of aerodynamic models created using flight test data is a time consuming and often costly process. In this paper a new method for the validation of global nonlinear aerodynamic models based on multivariate simplex splines is presented. This new method uses the unique properties of the multivariate simplex splines, a recent type of of multivariate spline, to speedup the process of aerodynamic model validation. Multivariate simplex splines are defined on non-rectangular domains and can be used to accurately fit scattered nonlinear datasets in any number of dimensions. The simplex splines consist of piecewise defined, ordinary multivariate polynomials with a predefined continuity between neighboring polynomial pieces. A recent method for nonlinear system identification based on multivariate simplex splines was used to create a global nonlinear aerodynamic model of the Cessna Citation II laboratory aircraft operated by the Delft University of Technology. In this paper, the multivariate spline based aerodynamic model for the pitching moment coefficient will be validated using both a model residual analysis as well as a statistical model quality analysis. It will be demonstrated that these new analysis methods, which are both unique to the multivariate simplex splines, provide a highly efficient and powerful new method for aerodynamic model validation., Control & Operations, Aerospace Engineering

Trajectory optimization has been a large field of research for many years. The drawback is that for non-convex, constrained problems practically all available solvers cannot guarantee that the globally optimal trajectory is found. Interval analysis based solvers however can provide this guarantee. Interval analysis has been applied to trajectory optimization before, but the previously presented methods suffered from major drawbacks which limited their application to small scale problems. In this paper a new interval based method is introduced which incorporates state parameterization to prevent explicit integration. The performance of the proposed method is demonstrated by applying it to a spacecraft formation flying optimization problem. The results are compared with a gradient based solver and it is shown that the interval method is guaranteed to find the global optimal solution. Finally the first steps for another new trajectory optimization method based on interval analysis and direct collocation are presented., Control & Operations, Aerospace Engineering

The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able to perform online model identification and bounded model extrapolation in the framework of a model-based control system. A new type of linear constraints, the differential constraints, are used as differential boundary conditions within the recursive estimator which limit polynomial divergence when extrapolating data. The differential constraints are derived with a new, one-step matrix form of the de Casteljau algorithm, which reduces their formulation into a single matrix multiplication. The recursive estimator is demonstrated on a bivariate dataset, where it is shown to provide a speedup of two orders of magnitude over an ordinary least squares batch method. Additionally, it is demonstrated that inclusion of differential constraints in the least squares optimization scheme can prevent polynomial divergence close to edges of the model domain where local data coverage may be insufficient, a situation often encountered with global recursive data approximation., Control & Operations, Aerospace Engineering

In this paper nonlinear adaptive flight control laws based on the backstepping approach are proposed which are applicable to over-actuated nonlinear systems. Instead of solving the control allocation exactly, update laws for the desired control effector signals are defined such that they converge to the optimal solution. Stability and boundedness of the resulting closed-loop system can be shown by means of Lyapunov analysis. Three different update laws are defined, the integrated, modular and composite adaptive designs. The last can be seen as a combination between the first two and has the best convergence and numerical properties. Second-order actuator dynamics are taken into account in the control designs. The control design is evaluated using numerical simulations where several cases of locked control surface failures are considered during two different maneuvers. No sensor information about these failures is fed back to the control system. The tracking performance of the adaptive control design is excellent for the nominal case and all considered failure cases. The failures are recognized shortly after they are introduced into the system, and the new dynamics are rapidly identified., Control & Operations, Aerospace Engineering

A new adaptive nonlinear flight controller is designed for a high fidelity, six degrees of freedom F-16 model for the entire flight envelope. The design is based on a modular approach which separates the design of the control law and the online identifier. The control law design is based on backstepping with nonlinear damping terms to robustify the design against parameter estimation errors and unknown bounded disturbances. The flight envelope is partitioned into hyperboxes, for each hyperbox a locally valid incremental model is estimated based on the linearized equations of motion. A continuous-time formulation of orthogonal least squares is used for identification of these locally valid models. The obtained local models are interpolated by means of B-splines to obtain a smooth model valid for the complete flight envelope. The performance of the resulting nonlinear adaptive control design is evaluated on the F-16 aircraft model for representative flight conditions, maneuvers, and failure cases., Control & Operations, Aerospace Engineering

Personal air transportation utilizing small aircraft is a market that is expected to grow significantly in the future. For this segment, “stick and rudder” related accidents should be mitigated to guide this process in a safe manner. Instead of downscaling advanced and expensive fly-by-wire platforms that incorporate flight envelope protection found in commercial aircraft, a low cost solution should be considered. This paper focuses on a flight envelope protection system for small aircraft, to allow carefree maneuvering for the less experienced pilot. Preliminary results are obtained from an empirical comparison study in the time domain, between a PID based control limiting approach, a command limiting approach and a constrained Flight Control Law (FCL) approach using Model-based Predictive Control (MPC), with and without parametric model uncertainties. Investigation of the results reveals that, for this study, command limiting and MPC should be preferred over control limiting and that the practicality of command limiting outweighs the small performance increase of MPC., Control & Operations, Aerospace Engineering

This work addresses the problem of indoor state estimation for autonomous flying vehicles with an optic flow approach. The paper discusses a sensor configuration using six optic flow sensors of the computer mouse type augmented by a three-axis accelerometer to estimate velocity, rotation, attitude and viewing distances. It is shown that the problem is locally observable for a moving vehicle. A Kalman filter is used to extract these states from the sensor data. The resulting approach is tested in a simulation environment evaluating the performance of three Kalman filter algorithms under various noise conditions. Finally, a prototype of the sensor hardware has been built and tested in a laboratory setup., Control & Operations, Aerospace Engineering

This paper applies interval optimization to the fixed-time multiple impulse rendezvous and docking problem. Current methods for solving this type of optimization problem include for example genetic algorithms and gradient based optimization. Unlike these methods, interval methods can guarantee that the globally best solution is found for a given parameterization of the input. The state transition matrix approach for the linearized CW-equations is used to avoid interval integration. Thruster pulse amplitudes are optimized by an interval branch and bound algorithm, which systematically eliminates parts of the control input space that do not satisfy the final time state constraints. Interval analysis is shown to be a useful tool in both sensitivity analysis and nonlinear optimization of the rendezvous and docking problem., Control & Operations, Aerospace Engineering

This paper presents a flight control strategy based on nonlinear dynamic inversion. The approach presented, called incremental nonlinear dynamic inversion, uses properties of general mechanical systems and nonlinear dynamic inversion by feeding back angular accelerations. Theoretically, feedback of angular accelerations eliminates sensitivity to model mismatch, greatly increasing the robust performance of the system compared with conventional nonlinear dynamic inversion. However, angular accelerations are not readily available. Furthermore, it is shown that angular acceleration feedback is sensitive to sensor measurement time delays. Therefore, a linear predictive filter is proposed that predicts the angular accelerations, solving the time delay and angular acceleration availability problem. The predictive filter uses only references and measurements of angular rates. Hence, the proposed control method makes incremental nonlinear dynamic inversion practically available using conventional inertial measurement units., Control and Simulation Division, Aerospace Engineering

In this paper a theoretical framework for nonlinear adaptive flight control is developed and applied to a simplified, over-actuated nonlinear fighter aircraft model. The framework is based on a modular adaptive backstepping scheme with a new type of nonlinear estimator. The nonlinear estimator is constructed using an invariant manifold based approach which allows for prescribed dynamics to be assigned to the estimation error. Attractivity of the manifold is ensured with the addition of dynamic scaling factors and output filters to the design procedure. The properties of the estimator can be exploited by designing a command filtered backstepping control law that renders the closed-loop system input-to-state stable with respect to the parameter estimation error. It is demonstrated that the resulting modular adaptive controller is easier to tune compared to controllers obtained using the classical adaptive backstepping approaches. Furthermore, the performance of the adaptive controller does not suffer from unpredictable dynamical behavior of the parameter update laws. This is illustrated in numerical simulations where several types of realistic failures are introduced in the aircraft model., Control & Operations, Aerospace Engineering

This paper describes how the previously developed concept of Pseudo Control Hedging (PCH) can be integrated in a Fault Tolerant Flight Controller (FTFC) as a safe flight envelope protection system of the first degree. This PCH algorithm adapts the reference model for the system output in case of unachievable commands due to control input saturation. As an example, this algorithm has been applied in the pitch rate and velocity control loops of a high fidelity Boeing 747 simulation model where its beneficial influence has been illustrated. The nonlinear adaptive control law used for this example is a triple layered nonlinear dynamic inversion algorithm, based upon the concept of time scale separation., Control & Operations, Aerospace Engineering

A high fidelity aircraft simulation model, reconstructed using the Digital Flight Data Recorder (DFDR) of the 1992 Amsterdam Bijlmermeer aircraft accident (Flight 1862), has been used to evaluate a new Fault-Tolerant Flight Control Algorithm in an online piloted evaluation. This paper focuses on the piloted simulator evaluation results. Reconfiguring control is implemented by making use of Adaptive Nonlinear Dynamic Inversion (ANDI) for manual fly by wire control. After discussing the modular adaptive controller setup, the experiment is described for a piloted simulator evaluation of this innovative recon- figurable control algorithm applied to a damaged civil transport aircraft. The evaluation scenario, measurements and experimental design, as well as the real-time implementation are described. Finally, reconfiguration test results are shown for damaged aircraft models including component as well as structural failures. The evaluation shows that the FTFC algorithm is able to restore conventional control strategies after the aircraft configuration has changed dramatically due to these severe failures. The algorithm supports the pilot after a failure by lowering workload and allowing a safe return to the airport. For most failures, the handling qualities are shown to degrade less with a failure than the baseline classical control system does., Control & Operations, Aerospace Engineering

A new method is proposed for the identification of global nonlinear models of aircraft non-dimensional force and moment coefficients. The method is based on a recent type of multivariate spline, the multivariate simplex spline, which can accurately approximate very large, scattered nonlinear datasets in any number of dimensions. The new identification method is used to identify a global nonlinear aerodynamic model of high dimensionality for the Cessna Citation II laboratory aircraft operated by the Delft University of Technology and the Netherlands National Aerospace Laboratory. The data used in the identification process consisted of millions of measurements and was accumulated during more than 250 flight test maneuvers with the laboratory aircraft. The resulting models for the aerodynamic force and moment coefficients are continuous analytical functions as they consist of sets of piecewise defined, multivariate polynomials. The identified models were validated using a subset of the flight data, with validation results showing a very close match between model and reality., Control & Operations, Aerospace Engineering

This paper applies interval optimization to the fixed-time multiple impulse rendezvous and docking problem. Current methods for solving this type of optimization problem include for example genetic algorithms and gradient based optimization. Unlike these methods, interval methods can guarantee that the globally best solution is found for a given parameterization of the input. The state transition matrix approach for the linearized CW-equations is used to avoid interval integration. Thruster pulse amplitudes are optimized by an interval branch and bound algorithm, which systematically eliminates parts of the control input space that do not satisfy the final time state constraints. Interval analysis is shown to be a useful tool in both sensitivity analysis and nonlinear optimization of the rendezvous and docking problem., Control & Operations, Aerospace Engineering

A new adaptive nonlinear flight controller is designed for a high fidelity, six degrees of freedom F-16 model for the entire flight envelope. The design is based on a modular approach which separates the design of the control law and the online identifier. The control law design is based on backstepping with nonlinear damping terms to robustify the design against parameter estimation errors and unknown bounded disturbances. The flight envelope is partitioned into hyperboxes, for each hyperbox a locally valid incremental model is estimated based on the linearized equations of motion. A continuous-time formulation of orthogonal least squares is used for identification of these locally valid models. The obtained local models are interpolated by means of B-splines to obtain a smooth model valid for the complete flight envelope. The performance of the resulting nonlinear adaptive control design is evaluated on the F-16 aircraft model for representative flight conditions, maneuvers, and failure cases., Control & Operations, Aerospace Engineering

This paper presents a flight control strategy based on nonlinear dynamic inversion. The approach presented, called incremental nonlinear dynamic inversion, uses properties of general mechanical systems and nonlinear dynamic inversion by feeding back angular accelerations. Theoretically, feedback of angular accelerations eliminates sensitivity to model mismatch, greatly increasing the robust performance of the system compared with conventional nonlinear dynamic inversion. However, angular accelerations are not readily available. Furthermore, it is shown that angular acceleration feedback is sensitive to sensor measurement time delays. Therefore, a linear predictive filter is proposed that predicts the angular accelerations, solving the time delay and angular acceleration availability problem. The predictive filter uses only references and measurements of angular rates. Hence, the proposed control method makes incremental nonlinear dynamic inversion practically available using conventional inertial measurement units., Control and Simulation Division, Aerospace Engineering

In this paper a theoretical framework for nonlinear adaptive flight control is developed and applied to a simplified, over-actuated nonlinear fighter aircraft model. The framework is based on a modular adaptive backstepping scheme with a new type of nonlinear estimator. The nonlinear estimator is constructed using an invariant manifold based approach which allows for prescribed dynamics to be assigned to the estimation error. Attractivity of the manifold is ensured with the addition of dynamic scaling factors and output filters to the design procedure. The properties of the estimator can be exploited by designing a command filtered backstepping control law that renders the closed-loop system input-to-state stable with respect to the parameter estimation error. It is demonstrated that the resulting modular adaptive controller is easier to tune compared to controllers obtained using the classical adaptive backstepping approaches. Furthermore, the performance of the adaptive controller does not suffer from unpredictable dynamical behavior of the parameter update laws. This is illustrated in numerical simulations where several types of realistic failures are introduced in the aircraft model., Control & Operations, Aerospace Engineering

Personal air transportation utilizing small aircraft is a market that is expected to grow significantly in the future. For this segment, “stick and rudder” related accidents should be mitigated to guide this process in a safe manner. Instead of downscaling advanced and expensive fly-by-wire platforms that incorporate flight envelope protection found in commercial aircraft, a low cost solution should be considered. This paper focuses on a flight envelope protection system for small aircraft, to allow carefree maneuvering for the less experienced pilot. Preliminary results are obtained from an empirical comparison study in the time domain, between a PID based control limiting approach, a command limiting approach and a constrained Flight Control Law (FCL) approach using Model-based Predictive Control (MPC), with and without parametric model uncertainties. Investigation of the results reveals that, for this study, command limiting and MPC should be preferred over control limiting and that the practicality of command limiting outweighs the small performance increase of MPC., Control & Operations, Aerospace Engineering

This paper describes how the previously developed concept of Pseudo Control Hedging (PCH) can be integrated in a Fault Tolerant Flight Controller (FTFC) as a safe flight envelope protection system of the first degree. This PCH algorithm adapts the reference model for the system output in case of unachievable commands due to control input saturation. As an example, this algorithm has been applied in the pitch rate and velocity control loops of a high fidelity Boeing 747 simulation model where its beneficial influence has been illustrated. The nonlinear adaptive control law used for this example is a triple layered nonlinear dynamic inversion algorithm, based upon the concept of time scale separation., Control & Operations, Aerospace Engineering

This work addresses the problem of indoor state estimation for autonomous flying vehicles with an optic flow approach. The paper discusses a sensor configuration using six optic flow sensors of the computer mouse type augmented by a three-axis accelerometer to estimate velocity, rotation, attitude and viewing distances. It is shown that the problem is locally observable for a moving vehicle. A Kalman filter is used to extract these states from the sensor data. The resulting approach is tested in a simulation environment evaluating the performance of three Kalman filter algorithms under various noise conditions. Finally, a prototype of the sensor hardware has been built and tested in a laboratory setup., Control & Operations, Aerospace Engineering

A high fidelity aircraft simulation model, reconstructed using the Digital Flight Data Recorder (DFDR) of the 1992 Amsterdam Bijlmermeer aircraft accident (Flight 1862), has been used to evaluate a new Fault-Tolerant Flight Control Algorithm in an online piloted evaluation. This paper focuses on the piloted simulator evaluation results. Reconfiguring control is implemented by making use of Adaptive Nonlinear Dynamic Inversion (ANDI) for manual fly by wire control. After discussing the modular adaptive controller setup, the experiment is described for a piloted simulator evaluation of this innovative recon- figurable control algorithm applied to a damaged civil transport aircraft. The evaluation scenario, measurements and experimental design, as well as the real-time implementation are described. Finally, reconfiguration test results are shown for damaged aircraft models including component as well as structural failures. The evaluation shows that the FTFC algorithm is able to restore conventional control strategies after the aircraft configuration has changed dramatically due to these severe failures. The algorithm supports the pilot after a failure by lowering workload and allowing a safe return to the airport. For most failures, the handling qualities are shown to degrade less with a failure than the baseline classical control system does., Control & Operations, Aerospace Engineering

A new method is proposed for the identification of global nonlinear models of aircraft non-dimensional force and moment coefficients. The method is based on a recent type of multivariate spline, the multivariate simplex spline, which can accurately approximate very large, scattered nonlinear datasets in any number of dimensions. The new identification method is used to identify a global nonlinear aerodynamic model of high dimensionality for the Cessna Citation II laboratory aircraft operated by the Delft University of Technology and the Netherlands National Aerospace Laboratory. The data used in the identification process consisted of millions of measurements and was accumulated during more than 250 flight test maneuvers with the laboratory aircraft. The resulting models for the aerodynamic force and moment coefficients are continuous analytical functions as they consist of sets of piecewise defined, multivariate polynomials. The identified models were validated using a subset of the flight data, with validation results showing a very close match between model and reality., Control & Operations, Aerospace Engineering

A new methodology for creating highly accurate, static nonlinear maps from scattered, multivariate data is presented. This new methodology uses the B-form polynomials of multivariate simplex splines in a new linear regression scheme. This allows the use of standard parameter estimation techniques for estimating the B-coefficients of the multivariate simplex splines. We present a generalized least squares estimator for the B-coefficients, and show how the estimated B-coefficient variances lead to a new model quality assessment measure in the form of the B-coefficient variance surface. The new modeling methodology is demonstrated on a nonlinear scattered bivariate dataset., Control & Operations, Aerospace Engineering

The problem of output optimization within a specified input space of neural networks (NNs) with fixed weights is discussed in this paper. The problem is (highly) nonlinear when nonlinear activation functions are used. This global optimization problem is encountered in the reinforcement learning (RL) community. Interval analysis is applied to guarantee that all solutions are found to any degree of accuracy with guaranteed bounds. The major drawbacks of interval analysis, i.e., dependency effect and high-computational load, are both present for the problem of NN output optimization. Taylor models (TMs) are introduced to reduce these drawbacks. They have excellent convergence properties for small intervals. However, the dependency effect still remains and is even made worse when evaluating large input domains. As an alternative to TMs, a different form of polynomial inclusion functions, called the polynomial set (PS) method, is introduced. This new method has the property that the bounds on the network output are tighter or at least equal to those obtained through standard interval arithmetic (IA). Experiments show that the PS method outperforms the other methods for the NN output optimization problem., Control & Operations, Aerospace Engineering

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., Control & Operations, Aerospace Engineering

A new methodology for creating highly accurate, static nonlinear maps from scattered, multivariate data is presented. This new methodology uses the B-form polynomials of multivariate simplex splines in a new linear regression scheme. This allows the use of standard parameter estimation techniques for estimating the B-coefficients of the multivariate simplex splines. We present a generalized least squares estimator for the B-coefficients, and show how the estimated B-coefficient variances lead to a new model quality assessment measure in the form of the B-coefficient variance surface. The new modeling methodology is demonstrated on a nonlinear scattered bivariate dataset., Control & Operations, Aerospace Engineering

The problem of output optimization within a specified input space of neural networks (NNs) with fixed weights is discussed in this paper. The problem is (highly) nonlinear when nonlinear activation functions are used. This global optimization problem is encountered in the reinforcement learning (RL) community. Interval analysis is applied to guarantee that all solutions are found to any degree of accuracy with guaranteed bounds. The major drawbacks of interval analysis, i.e., dependency effect and high-computational load, are both present for the problem of NN output optimization. Taylor models (TMs) are introduced to reduce these drawbacks. They have excellent convergence properties for small intervals. However, the dependency effect still remains and is even made worse when evaluating large input domains. As an alternative to TMs, a different form of polynomial inclusion functions, called the polynomial set (PS) method, is introduced. This new method has the property that the bounds on the network output are tighter or at least equal to those obtained through standard interval arithmetic (IA). Experiments show that the PS method outperforms the other methods for the NN output optimization problem., Control & Operations, Aerospace Engineering

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., Control & Operations, Aerospace Engineering

A motion simulator provided with a movable housing, preferably carried by a number of length-adjustable legs, in which housing projection means are arranged for visual information supply, while in the housing a control environment of a motion apparatus to be simulated is situated, the control environment being incorporated in a removable unit, which unit is exchangeable for another, comparable unit having a different control environment to be simulated, Aerospace Engineering

A motion simulator provided with a movable housing, preferably carried by a number of length-adjustable legs, in which housing projection means are arranged for visual information supply, while in the housing a control environment of a motion apparatus to be simulated is situated, the control environment being incorporated in a removable unit, which unit is exchangeable for another, comparable unit having a different control environment to be simulated, Aerospace Engineering

Aerospace Engineering

Aerospace Engineering

Aerospace Engineering

Aerospace Engineering