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departmentresearch group programmeprojectcoordinates)uuid:fa68d1b6623649b8819fc76452ddab3bDhttp://resolver.tudelft.nl/uuid:fa68d1b6623649b8819fc76452ddab3bdOnline Aerodynamic Model Identification using a Recursive Sequential Method for Multivariate Splines3Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.6Avoiding high computational loads is essential to online aerodynamic model identi fication algorithms, which are at the heart of any modelbased adaptive flight control system. Multivariate simplex Bspline (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 realtime 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 Bcoefficients for each module are estimated using a least squares estimator. In the second step the local Bcoefficients for each module are then smoothened into a single global Bcoefficient 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 F16 windtunnel model.Precursive identification; aerodynamic model identification; multivariate splinesenjournal articleAIAAAerospace EngineeringControl & Operations)uuid:e070de9de8054aa59bcc7f8719bb56e1Dhttp://resolver.tudelft.nl/uuid:e070de9de8054aa59bcc7f8719bb56e1eA novel adaptive kernel method with kernel centers determined by a support vector regression approach]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 (IRRLSSVR), 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 WVLSSVR is proposed in this paper using the same type of kernels and the same centers as those used in the IRRLSSVR. 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 GPKLSSVR 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 WVLSSVR algorithm shows higher approximation accuracy than the recursive parametric kernel method using the centers calculated by the kmeans clustering approach. The extended recursive kernel method (i.e. GPKLSSVR) has not shown advantage in terms of global approximation accuracy when validating the test data set without realtime updation, but it can increase modeling accuracy if the realtime i< dentification is involved.Wsupport vector machine; recursive identification; adaptive model; kernel basis functionElsevier)uuid:730597cdcf1a493f97b1fa903d4e4c0eDhttp://resolver.tudelft.nl/uuid:730597cdcf1a493f97b1fa903d4e4c0ePAdaptive Optimizing Nonlinear Control Design for an Overactuated Aircraft Model7Van Oort, E.R.; Sonneveldt, L.; Chu, Q.P.; Mulder, J.A.In this paper nonlinear adaptive flight control laws based on the backstepping approach are proposed which are applicable to overactuated 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 closedloop 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. Secondorder 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.conference paper9American Institute of Aeronautics and Astronautics (AIAA))uuid:c60c91583a234381aa6decb04c8d071dDhttp://resolver.tudelft.nl/uuid:c60c91583a234381aa6decb04c8d071d2Trajectory Optimization Based on Interval Analysis6De Weerdt, E.; Van Kampen, E.; Chu, Q.P.; Mulder, J.A.Trajectory optimization has been a large field of research for many years. The drawback is that for nonconvex, 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.)uuid:926233cbd99941f586a64ce19965dce5Dhttp://resolver.tudelft.nl/uuid:926233cbd99941f586a64ce19965dce5WDifferential constraints for bounded recursive identification with multivariate splines(De Visser, C.C.; Chu, Q.P.; Mulder, J.A.The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive modelbased 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 modelbased 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, onestep 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.Rmultivariate splines; parameter estimation; scattered data; function approximators
20140501)uuid:cf9119d38bbf489bb734f25c4cd1402aDhttp://resolver.tudelft.nl/uuid:cf9119d38bbf489bb734f25c4cd1402agRobust flight control using incremental nonlinear dynamic inversion and angular acceleration prediction'Sieberling, S.; Chu, Q.P.; Mulder, J.A.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.dynamic inversion; nonlinear dynamic inversion; incremental nonlinear dynamic inversion; NDI; INDI; feedback linearization; robust flight control; angular acceleration predictionControl and Simulation Division)uuid:1e456421197e4fef8528bed022ea4a41Dhttp://resolver.tudelft.nl/uuid:1e456421197e4fef8528bed022ea4a41SPiloted Simulator Evaluation Results of New FaultTolerant Flight Control AlgorithmULombaerts, T.J.J.; Smaili, M.H.; Stroosma, O.; Chu, Q.P.; Mulder, J.A.; Joosten, D.A.SA 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 FaultTolerant 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 realtime 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.)uuid:fcf4d3b29f3c4fd18ec71c44d90a33e2Dhttp://resolver.tudelft.nl/uuid:fcf4d3b29f3c4fd18ec71c44d90a33e2IOptimization of Spacecraft Rendezvous and Docking using Interval Analysis'Van Kampen, E.; Chu, Q.P.; Mulder, J.A.>This paper applies interval optimization to the fixedtime 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 CWequations 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.)uuid:2463ebfa0fc94eb4aaf78d9368535c83Dhttp://resolver.tudelft.nl/uuid:2463ebfa0fc94eb4aaf78d9368535c83AOptic Flow Based State Estimation for an Indoor Micro Air Vehicle5Verveld, M.J.; Chu, Q.P.; De Wagter, C.; Mulder, J.A.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 threeaxis 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.)uuid:3846d2ceaecc4c7db2fc892177301c23Dhttp://resolver.tudelft.nl/uuid:3846d2ceaecc4c7db2fc892177301c23NPseudo Control Hedging and its Application for Safe Flight Envelope Protection9Lombaerts, T.J.J.; Looye, G.H.N.; Chu, Q.P.; Mulder, J.A.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.)uuid:ef521625a56249e99bde44538ce159feDhttp://resolver.tudelft.nl/uuid:ef521625a56249e99bde44538ce159fe[Full Envelope Modular Adaptive Control of a Fighter Aircraft using Orthogonal Least SquaresA new adaptive nonlinear flight controller is designed for a high fidelity, six degrees of freedom F16 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 continuoustime formulation of orthogonal least squares is used for identification of these locally valid models. The obtained local models are interpolated by means of Bsplines to obtain a smooth model valid for the complete flight envelope. The performance of the resulting nonlinear adaptive control design is evaluated on the F16 aircraft model for representative flight conditions, maneuvers, and failure cases.)uuid:ba549e3d6b064b52b37c5d3259ae5d70Dhttp://resolver.tudelft.nl/uuid:ba549e3d6b064b52b37c5d3259ae5d70@Immersion and Invariance Based Nonlinear Adaptive Flight Control7Sonneveldt, L.; Van Oort, E.R.; Chu, Q.P.; Mulder, J.A.In this paper a theoretical framework for nonlinear adaptive flight control is developed and applied to a simplified, overactuated nonlinear fighter aircraft model. The framework is based on a modular adaptive backstepping scheme with a new type of no< nlinear 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 closedloop system inputtostate 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.)uuid:f2b6bdbd5d114ec5ac7ed2c2ce21144cDhttp://resolver.tudelft.nl/uuid:f2b6bdbd5d114ec5ac7ed2c2ce21144c=A new approach to linear regression with multivariate splinesA new methodology for creating highly accurate, static nonlinear maps from scattered, multivariate data is presented. This new methodology uses the Bform polynomials of multivariate simplex splines in a new linear regression scheme. This allows the use of standard parameter estimation techniques for estimating the Bcoefficients of the multivariate simplex splines. We present a generalized least squares estimator for the Bcoefficients, and show how the estimated Bcoefficient variances lead to a new model quality assessment measure in the form of the Bcoefficient variance surface. The new modeling methodology is demonstrated on a nonlinear scattered bivariate dataset.Csplines; parameter estimation; scattered data; multivariate splines)uuid:8f82534208604316a3d166b375cce610Dhttp://resolver.tudelft.nl/uuid:8f82534208604316a3d166b375cce610\Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood EstimationPZaal, P.M.T.; Pool, D.M.; Chu, Q.P.; Van Paassen, M.M.; Mulder, M.; Mulder, J.A.VThis paper presents a new method for estimating the parameters of multichannel pilot models that is based on maximum likelihood estimation. To cope with the inherent nonlinearity of this optimization problem, the gradientbased GaussNewton 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 humanintheloop 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.;System Identification; Pilot Modeling; Parameter Estimation)uuid:b0d2cc9fc6b949b091e5d5ef37e32ecbDhttp://resolver.tudelft.nl/uuid:b0d2cc9fc6b949b091e5d5ef37e32ecb:Neural Network Output Optimization Using Interval Analysis&De Weerdt, E.; Chu, Q.P.; Mulder, J.A.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 highcomputational load, are both present for the problem o<f 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.feedforward neural networks (FFNNs); global optimization; inclusion function; interval analysis; optimization methods; polynomial set; radial basis function neural networks (RBFNNs); taylor expansion; taylor model (TM)IEEE
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