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Mass Estimation for an Adaptive Trajectory Predictor using Optimal Control

Santi Vilardaga, Xavier Prats
2015 Proceedings of the 5th International Conference on Application and Theory of Automation in Command and Control Systems - ATACCS '15  
Then, an optimisation problem is formulated, using optimal control theory, that minimises the error with the known states, having the parameters of study (i.e. mass) as decision variables.  ...  A scenario with two departing trajectories is used to demonstrate the effectiveness of this parameter estimation method.  ...  Acknowledgments The authors would like to thank Airbus Industrie for the use of PEP (Performance Engineers Program) suite, which allowed us to undertake realistic aircraft performance simulations.  ... 
doi:10.1145/2899361.2899369 dblp:conf/atacss/VilardagaP15 fatcat:vdbgl7jnznffjnca2t776osfwa

Differential Drag-Based Reference Trajectories for Spacecraft Relative Maneuvering Using Density Forecast

David Pérez, Riccardo Bevilacqua
2016 Journal of Spacecraft and Rockets  
the optimized adaptation μ = Earth's gravitational parameter ρ = atmospheric density ω = magnitude of the orbital angular velocity of the target  ...  the optimization m C , m T = chaser and target spacecraft's mass R e = Earth mean radius R t = position vector of the target in relation to the Earth S C , S T = chaser and target spacecraft's crosswind  ...  Acknowledgments The authors wish to acknowledge the Office of Naval Research, Young Investigator Program, for sponsoring this investigation (award no. N00014-15-1-2087).  ... 
doi:10.2514/1.a33332 fatcat:3qxzdiya2jflrdzt4mzxqwdat4

Improvement to the Analytical Predictor-Corrector Guidance Algorithm Applied to Mars Aerocapture

Jean-Francois Hamel, Jean De Lafontaine
2006 Journal of Guidance Control and Dynamics  
Finally, the terminal point controller, part of the third category, uses a prede- fined optimal trajectory. In this case, the vehicle tries to remain on the optimal trajectory at any moment in time.  ...  The second modification pro- poses a way to compute an equivalent density scale height adapted to APC using density estimation during the vehicle capture phase.  ... 
doi:10.2514/1.20126 fatcat:fi5twgczj5aa7ekby4stx3infu

High-Precision Trajectory Tracking in Changing Environments Through L_1 Adaptive Feedback and Iterative Learning [article]

Karime Pereida, Rikky R. P. R. Duivenvoorden, Angela P. Schoellig
2017 arXiv   pre-print
In particular, we are able to generalize learned trajectories across different system configurations because the L_1 adaptive controller handles the underlying changes in the system.  ...  In this paper, we propose and provide theoretical proofs of a combined L_1 adaptive feedback and iterative learning control (ILC) framework to improve trajectory tracking of a system subject to unknown  ...  Output Predictor: The following output predictor is used within the L 1 adaptive output feedback architecture: y 1 (t) = −mŷ 1 (t) + m(u(t) +σ(t)) ,ŷ 1 (0) = 0 , whereσ(t) is the adaptive estimate of σ  ... 
arXiv:1705.04763v1 fatcat:z32n7aiqord5bcs5icxag25qtm

Robustifying Reinforcement Learning Policies with ℒ_1 Adaptive Control [article]

Yikun Cheng, Pan Zhao, Manan Gandhi, Bo Li, Evangelos Theodorou, Naira Hovakimyan
2022 arXiv   pre-print
Leveraging the capability of an ℒ_1 control law in the fast estimation of and active compensation for dynamic variations, our approach can significantly improve the robustness of an RL policy trained in  ...  We propose an approach to robustifying a pre-trained non-robust RL policy with ℒ_1 adaptive control.  ...  An L 1 controller mainly consists of three components: a state predictor, an adaptive law, and a control law.  ... 
arXiv:2106.02249v5 fatcat:v5t7eoyuivbhzmtonskroio2vm

Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External Disturbances

Cheongwoong Kang, Bumjin Park, Jaesik Choi
2021 Sensors  
Although PID control is widely used for quadrotor control, it is not adaptable to environmental changes, such as various trajectories and dynamic external disturbances.  ...  In this work, we discover that adjusting PID control frequencies is necessary for adapting to environmental changes by showing that the optimal control frequencies can be different for different environments  ...  We discover that the optimal control frequencies vary for different trajectories.  ... 
doi:10.3390/s22010150 pmid:35009692 pmcid:PMC8749744 fatcat:o7zu46ybkfgdrbkkua4zdwna7e

L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors [article]

Jintasit Pravitra, Kasey A. Ackerman, Chengyu Cao, Naira Hovakimyan, Evangelos A. Theodorou
2020 arXiv   pre-print
This paper presents a multirotor control architecture, where Model Predictive Path Integral Control (MPPI) and L1 adaptive control are combined to achieve both fast model predictive trajectory planning  ...  and robust trajectory tracking.  ...  The authors would also like to thank Aditya Gahlawat from UIUC for the fruitful discussion. This work is funded by NASA LaRC.  ... 
arXiv:2004.00152v1 fatcat:f3evn6h7drdxzcifpmhd7aoxki

Data-Driven Trajectory Uncertainty Quantification For Climbing Aircraft To Improve Ground-Based Trajectory Prediction

Mevlüt Uzun, Emre Koyuncu
2017 Anadolu University Journal of Science and Technology. A : Applied Sciences and Engineering  
In addition to controller actions, uncertainties in climbing flights are major components of prediction errors in a flight trajectory.  ...  We have used model-driven data statistical approaches through aircraft flight record data sets (i.e. QAR).  ...  It is an expected result as the estimated aircraft mass converges to the actual value, which is the aim of the adaptive algorithm.  ... 
doi:10.18038/aubtda.270074 fatcat:gh5nbkijunezlgnpcpqhasvjeq

A computational scheme for internal models not requiring precise system parameters

Dongwon Kim, Jian Jing
2019 PLoS ONE  
It is possible to estimate the dynamics of the system by using a motor command and its resulting output, instead of constructing a model of the dynamics with precise parameters.  ...  In this study, a computational model is proposed that uses a motor command and its corresponding output to estimate the dynamics of the system and it is examined whether the proposed model is capable of  ...  Computational models for comparison For a comparative study with a model-based control system, an optimal control model is chosen which supports the internal-model hypothesis [44] [45] [46] [47] .  ... 
doi:10.1371/journal.pone.0210616 pmid:30811420 pmcid:PMC6392307 fatcat:t4dwui53encpjjzv5pxieqhmby

Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions [article]

Karime Pereida, Angela Schoellig
2018 arXiv   pre-print
In this paper, we propose a novel adaptive model predictive controller that combines model predictive control (MPC) with an underlying L_1 adaptive controller to improve trajectory tracking of a system  ...  A higher-level model predictive controller then uses this reference model to calculate the optimal reference input based on a cost function, while taking into account input and state constraints.  ...  It is an optimal control scheme; however, its performance depends on the accuracy of the model used in the optimization.  ... 
arXiv:1807.05290v2 fatcat:rnxlnyaukzabhkjd5rf5u6l6pi

The eMOSAIC model for humanoid robot control

Norikazu Sugimoto, Jun Morimoto, Sang-Ho Hyon, Mitsuo Kawato
2012 Neural Networks  
The modular architecture of the MOSAIC model can be useful for solving nonlinear and non-stationary control problems.  ...  MOSAIC was originally proposed by neuroscientists to understand the human ability of adaptive control.  ...  Nakano for assistance with the experimental setup.  ... 
doi:10.1016/j.neunet.2012.01.002 pmid:22366503 fatcat:qpdnjbsif5dfrdqjk42y4fyvue

eMOSAIC Model for Humanoid Robot Control [chapter]

Norikazu Sugimoto, Jun Morimoto, Sang-Ho Hyon, Mitsuo Kawato
2010 Lecture Notes in Computer Science  
The modular architecture of the MOSAIC model can be useful for solving nonlinear and non-stationary control problems.  ...  MOSAIC was originally proposed by neuroscientists to understand the human ability of adaptive control.  ...  Nakano for assistance with the experimental setup.  ... 
doi:10.1007/978-3-642-15193-4_42 fatcat:iepseoa7ynhcjku5clmaz67pmi

Free Final-Time Fuel-Optimal Powered Landing Guidance Algorithm Combing Lossless Convex Optimization with Deep Neural Network Predictor

Wenbo Li, Shengping Gong
2022 Applied Sciences  
Firstly, the DNN predictor is built to map the optimal final time. Then, the LCvx algorithm is used to solve the problem of fuel-optimal powered landing with the given final time.  ...  Given the lack of real-time performance of the convex optimization algorithm with free final time, a lossless convex optimization (LCvx) algorithm based on the deep neural network (DNN) predictor is proposed  ...  The obtained data is then transmitted to the DNN optimal predictor, which estimates the optimal landing time.  ... 
doi:10.3390/app12073383 fatcat:ne2y77vlsnhm7eh4h6xpepnlem

Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction

Richard Alligier, David Gianazza, Nicolas Durand
2015 IEEE transactions on intelligent transportation systems (Print)  
Machine Learning method to the baseline BADA predictor and to the two other methods using estimated masses (adaptive, or least squares), on 9 different aircraft types and with various altitudes for the  ...  Approximation of the example trajectories when using the estimated massm 11,f uture For verification purposes, let us assess the accuracy of the trajectory computed using the estimated mass on our set  ...  For the E145 type, we see that neither the GBM method nor the mass estimation methods improve the results.  ... 
doi:10.1109/tits.2015.2437452 fatcat:n45n22oayrey3fbse7rei2hgam

Online Parameter Estimation for Safety-Critical Systems with Gaussian Processes [article]

Mouhyemen Khan, Abhijit Chatterjee
2020 arXiv   pre-print
Parameter estimation is crucial for modeling, tracking, and control of complex dynamical systems.  ...  By reconfiguring the controller with new optimized parameters iteratively, we drastically improve trajectory tracking of the system versus the nominal case and other solvers.  ...  For instance, estimating an unexpected change in mass allows not only controller modification but also necessary changes in path planning if desired, versus simply optimizing the controller gain while  ... 
arXiv:2002.07870v1 fatcat:7znnupu5xzdrrj7saeaiyf2mne
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