Andrea Iannelli

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Iannelli

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Andrea

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0000-0002-1865-6978

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Publications1 - 10 of 57

Distributed Dual Quaternion Extended Kalman Filtering for Spacecraft Pose Control
Hudoba de Badyn, Mathias; Binz, Jonas; Iannelli, Andrea; et al. (2023)
In this paper, we analyze a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft. Our proposed algorithm uses both absolute and relative pose measurements between neighbouring satellites in a network, allowing each individual satellite to estimate its own pose and that of its neighbours. By utilizing the distributed Kalman consensus filter, we propose a novel sensor and state-estimate fusion procedure that allows each satellite to improve its own state estimate by sharing data with its neighbours over a communication link. We also examine a leader-follower approach, whereby only a subset of the satellites have access to an absolute pose measurement. In this case, followers rely solely on the information provided by their neighbours, as well as relative pose measurements to those neighbours. The algorithm is tested extensively via numerical simulations, and it is shown that the approach provides a substantial improvement in performance over the scenario in which the satellites do not cooperate. A case study of satellites swarming an asteroid is presented, and the performance in the leader-follower scenario is also analyzed.
Nonlinear robust approaches to study stability and post-critical behaviour of an aeroelastic plant
Iannelli, Andrea; Marcos, Andrés; Lowenberg, Mark (2019)
IEEE Transactions on Control Systems Technology
Two approaches to tackle the nonlinear robust stability problem of an aerospace system are compared. The first employs a combination of the describing function method and μ analysis, while the second makes use of integral quadratic constraints (IQCs). The model analyzed consists of an open-loop wing's airfoil subject to free play and linear time-invariant parametric uncertainties. The key steps entailed by the application of the two methodologies and their main features are critically discussed. Emphasis is put on the available insight on the nonlinear postcritical behavior known as limit cycle oscillation. It is proposed a strategy to apply IQCs, typically used to find absolute stability certificates, in this scenario, based on a restricted sector bound condition for the nonlinearity. Another contribution of this paper is to understand how the conservatism usually associated with the IQCs multipliers selection can be overcome by using information coming from the first approach. Nonlinear time domain simulations showcase the prowess of these approaches in estimating qualitative trends and quantitative response's features.
Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization
Yin, Mingzhou; Iannelli, Andrea; Khosravi, Mohammad; et al. (2020)
IFAC-PapersOnLine ~ 21st IFAC World Congress
This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the structural constraints are interpreted as the requirement to place the poles of the non-truncated models at the same locations for all sub-models. This constraint is imposed by combining the atomic norm regularization framework for LTI systems with the group lasso technique in regression. As a result, the estimated system is both uniform and low-order, which is hard to achieve with other existing estimators. Monte Carlo simulation shows that the grouped atomic norm method does not only show better results compared to other regularized methods, but also outperforms the subspace identification method under high noise levels in terms of model fitting.
Study of Flexible Aircraft Body Freedom Flutter with Robustness Tools
Iannelli, Andrea; Marcos, Andrés; Lowenberg, Mark (2018)
Journal of Guidance, Control, and Dynamics
Body freedom flutter is a dynamic instability featuring strong coupling between rigid-body and elastic modes of the aircraft. Flexible configurations with adverse structural and geometric properties have been found susceptible to this phenomenon. Features that complicate its study are the presence of multiple modal instabilities and the different influence that system parameters have on each of them. The robust analysis framework based on linear fractional transformation modeling and structured singular value μ analysis is used in this work to study the body freedom flutter problem in a systematic way. The analyses performed showcase the potential of these methods, not only in supplying a characterization of the system in terms of its robustness but also in gaining further understanding of the body freedom flutter problem and reconciling the results with physical features. It is also shown that the robust modeling analysis framework complements the conventional, state-of-practice methods while allowing the study of highly coupled systems (of which the flexible aircraft is an example) to be addressed in an incremental and methodological manner. For this study, a simplified wing model is augmented including the short-period approximation aircraft model and the rigid–elastic coupling terms. The proposed model captures properties and trends of both restrained wing flutter and body freedom flutter instabilities.
A Multiobjective LQR Synthesis Approach to Dual Control for Uncertain Plants
Iannelli, Andrea; Smith, Roy (2020)
IEEE Control Systems Letters
The paper proposes a dual control finite horizon LQR synthesis procedure for unknown systems characterized by mean and covariance estimates. The optimized policy comprises time-varying state-feedback and dithering components, and the control problem is framed as a multiobjective synthesis which seeks a balance between exploitation and exploration costs. It is shown that classic experiment design problems can be recast in this framework by replacing the exploitation cost with an information reward. Numerical examples demonstrate the different dual control trade-offs on plants with different properties.
On the Regret of Recursive Methods for Discrete-Time Adaptive Control with Matched Uncertainty
Karapetyan, Aren; Balta, Efe C.; Tsiamis, Anastasios; et al. (2024)
2024 IEEE 63rd Conference on Decision and Control (CDC)
Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty. However, such methods are often impossible to implement directly, as they depend on an unobserved estimation error. We consider the equivalent discrete-time setting with a causal information structure, and propose a novel, online proximal point method-based adaptive controller, that under a sufficient excitation (SE) condition is asymptotically stable and achieves finite regret, scaling only with the time required to fulfill the SE. We show the same also for the widely-used recursive least squares with exponential forgetting controller under a stronger persistence of excitation condition.
Distributed Dual-Quaternion Extended Kalman Filtering for Spacecraft Pose Estimation
Hudoba de Badyn, Mathias; Binz, Jonas; Iannelli, Andrea; et al. (2025)
Journal of Guidance, Control, and Dynamics
In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is analyzed. The proposed algorithm uses both absolute and relative pose measurements between neighboring satellites in a network, allowing each individual satellite to estimate its own pose and that of its neighbors. By utilizing the distributed Kalman consensus filter, a novel sensor and state-estimate fusion procedure is proposed that allows each satellite to improve its own state estimate by sharing data with its neighbors over a communication link. A leader-follower approach, whereby only a subset of the satellites has access to an absolute pose measurement, is also examined. In this case, followers rely solely on the information provided by their neighbors, as well as relative pose measurements to those neighbors. The algorithm is tested extensively via numerical simulations, and it is shown that the approach provides a substantial improvement in performance over the scenario in which the satellites do not cooperate. A case study of satellites swarming an asteroid is presented, and the performance in the leader-follower scenario is also analyzed.
Subspace Identification of Linear Time-Periodic Systems with Periodic Inputs
Yin, Mingzhou; Iannelli, Andrea; Smith, Roy (2021)
IEEE Control Systems Letters
This letter proposes a new methodology for subspace identification of linear time-periodic (LTP) systems with periodic inputs. This method overcomes the issues related to the computation of frequency response of LTP systems by utilizing the frequency response of the time-lifted system with linear time-invariant structure instead. The response is estimated with an ensemble of input-output data with periodic inputs. This allows the frequency-domain subspace identification technique to be extended to LTP systems. The time-aliased periodic impulse response can then be estimated and the order-revealing decomposition of the block-Hankel matrix is formulated. The consistency of the proposed method is proved under mild noise assumptions. Numerical simulation shows that the proposed method performs better than multiple widely-used time-domain subspace identification methods when an ensemble of periodic data is available.
A symbolic LFT approach for robust flutter analysis of high-order models
Iannelli, Andrea; Marcos, Andrés; Bombardieri, Rocco; et al. (2019)
Proceedings of the 18th European Control Conference (ECC 2019)
The paper proposes an alternative methodology to build Linear Fractional Transformation (LFT) models of uncertain aeroelastic systems described by Fluid-Structure Interaction (FSI) solvers with the aim of studying flutter with the μ analysis technique from robust control. Two main issues can be identified for the fulfillment of this task. On the one hand, there is the difficult reconciliation between sources of physical uncertainty (well distinguishable in the original high-order system) and the abstracted uncertainties (defined in the reduced-order size representation used for the robust analyses). On the other hand, the large size of the resulting LFT model can prevent the application of robust analysis techniques. The solution proposed here consists of a symbolic LFT algorithm applied at FSI solver level, which guarantees the connection between the physical uncertainties and the parameters captured by the LFT. It also alleviates the final size of the LFT by exploiting the modal-oriented approach taken in introducing the uncertainties. Application of the framework using an unconventional aircraft layout as case study is finally discussed.
On the Regret of $\mathcal{H}_{\infty}$ Control
Karapetyan, Aren; Iannelli, Andrea; Lygeros, John (2022)
2022 IEEE 61st Conference on Decision and Control (CDC)
The $\mathcal{H}_{\infty}$ synthesis approach is a cornerstone robust control design technique, but is known to be conservative in some cases. The objective of this paper is to quantify the additional cost the controller incurs planning for the worst-case scenario, by adopting an approach inspired by regret from online learning. We define the disturbance-reality gap as the difference between the predicted worst-case disturbance signal and the actual realization. The regret is shown to scale with the norm of this gap, which turns out to have a similar structure to that of the certainty equivalent controller with inaccurate predictions, obtained here in terms of the prediction error norm.

Publications1 - 10 of 57

Andrea Iannelli

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