Online Adaptive Parameter Estimation for Quadrotors

Zhao, Jun; Wang, Xian; Gao, Guanbin; Na, Jing; Liu, Hongping; Luan, Fujin

doi:10.3390/a11110167

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…are defined in (13). Since a T i (x) ∈ R 1×2 , from rank-nullity theorem it follows that a T i (x) has a non-trivial null space of dimension one.…”

Section: Exactly One Active Constraint Ie K =mentioning

confidence: 99%

“…where a T ij (x) and b ij (x) are defined using (13). Since rank(A ac (x)) = 1, WLOG we have a T ij (x) = λ j a T i1 (x) where λ j ∈ R ∀j ∈ {2, 3, • • • , K}.…”

Section: ≤ K ≤ M and Rank(a Ac (X)) =mentioning

confidence: 99%

“…The development of provably convergent parameter estimation algorithms for nonlinear systems has been studied extensively [9], [10], [11]. These algorithms have been used for identifying parameters of manipulators [12] and quadrotors [13]. We consider these robots "monolithic" since they do not interact with other agents during the parameter identification phase.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Identification for Multirobot Systems Using Optimization Based Controllers (Extended Version)

Grover,

Liu,

Sycara

2020

Preprint

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This paper considers the problem of parameter identification for a multirobot system. We wish to understand when is it feasible for an adversarial observer to reverse-engineer the parameters of tasks being performed by a team of robots by simply observing their positions. We address this question by using the concept of persistency of excitation from system identification. Each robot in the team uses optimization-based controllers for mediating between task satisfaction and collision avoidance. These controllers exhibit an implicit dependence on the task's parameters which poses a hurdle for deriving necessary conditions for parameter identification, since such conditions usually require an explicit relation. We address this bottleneck by using duality theory and SVD of active collision avoidance constraints and derive an explicit relation between each robot's task parameters and its control inputs. This allows us to derive the main necessary conditions for successful identification which agree with our intuition. We demonstrate the importance of these conditions through numerical simulations by using (a) an adaptive observer and (b) an unscented Kalman filter for goal estimation in various geometric settings. These simulations show that under circumstances where parameter inference is supposed to be infeasible per our conditions, both these estimators fail and likewise when it is feasible, both converge to the true parameters. Videos of these results are available at https://bit.ly/3kQYj5J

show abstract

“…are defined in (13). Since a T i (x) ∈ R 1×2 , from rank-nullity theorem it follows that a T i (x) has a non-trivial null space of dimension one.…”

Section: Exactly One Active Constraint Ie K =mentioning

confidence: 99%

“…where a T ij (x) and b ij (x) are defined using (13). Since rank(A ac (x)) = 1, WLOG we have a T ij (x) = λ j a T i1 (x) where λ j ∈ R ∀j ∈ {2, 3, • • • , K}.…”

Section: ≤ K ≤ M and Rank(a Ac (X)) =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter Identification for Multirobot Systems Using Optimization Based Controllers (Extended Version)

Grover,

Liu,

Sycara

2020

Preprint

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show abstract

“…When an observer has the freedom to command inputs to the system, he can design sufficiently rich inputs that will ensure convergence of these estimators to the true parameters of the system. This open-loop identification has been used for identifying physical parameters of manipulators [12] and quadrotors [13].…”

Section: Introductionmentioning

confidence: 99%

Feasible Region-based Identification Using Duality (Extended Version)

Grover¹,

Liu²,

Sycara³

2020

Preprint

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We consider the problem of estimating bounds on parameters representing tasks being performed by individual robots in a multirobot system. In our previous work, we derived necessary conditions based on persistency of excitation analysis for exact identification of these parameters. We concluded that depending on the robot's task, the dynamics of individual robots may fail to satisfy these conditions, thereby preventing exact inference. As an extension to that work, this paper focuses on estimating bounds on task parameters when such conditions are not satisfied. Each robot in the team uses optimization-based controllers for mediating between task satisfaction and collision avoidance. We use KKT conditions of this optimization and SVD of active collision avoidance constraints to derive explicit relations between Lagrange multipliers, robot dynamics and task parameters. Using these relations, we are able to derive bounds on each robot's task parameters. Through numerical simulations we show how our proposed region based identification approach generates feasible regions for parameters when a conventional estimator such as a UKF fails. Additionally, empirical evidence shows that this approach generates contracting sets which converge to the true parameters much faster than the rate at which a UKF based estimate converges. Videos of these results are available at https://bit.ly/2JDMgeJ

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Approximate Optimal Stabilization Control of Servo Mechanisms based on Reinforcement Learning Scheme

Ren

et al. 2019

Int. J. Control Autom. Syst.

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Online Adaptive Parameter Estimation for Quadrotors

Cited by 9 publications

References 22 publications

Parameter Identification for Multirobot Systems Using Optimization Based Controllers (Extended Version)

Parameter Identification for Multirobot Systems Using Optimization Based Controllers (Extended Version)

Feasible Region-based Identification Using Duality (Extended Version)

Approximate Optimal Stabilization Control of Servo Mechanisms based on Reinforcement Learning Scheme

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