Search Results for author: Aditya Gahlawat

Found 11 papers, 1 papers with code

Guaranteed Nonlinear Tracking in the Presence of DNN-Learned Dynamics With Contraction Metrics and Disturbance Estimation

no code implementations15 Dec 2021 Pan Zhao, Ziyao Guo, Yikun Cheng, Aditya Gahlawat, Hyungsoo Kang, Naira Hovakimyan

The learned dynamics, the estimated disturbances, and the EEBs are then incorporated in a robust Riemannian energy condition to compute the control law that guarantees exponential convergence of actual trajectories to desired ones throughout the learning phase, even when the learned model is poor.

Generalization of Safe Optimal Control Actions on Networked Multi-Agent Systems

no code implementations21 Sep 2021 Lin Song, Neng Wan, Aditya Gahlawat, Chuyuan Tao, Naira Hovakimyan, Evangelos A. Theodorou

The control action composition is achieved by taking a weighted mixture of the existing controllers according to the contribution of each component task.

$\mathcal{L}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors

no code implementations14 Sep 2021 Zhuohuan Wu, Sheng Cheng, Kasey A. Ackerman, Aditya Gahlawat, Arun Lakshmanan, Pan Zhao, Naira Hovakimyan

This paper introduces an $\mathcal{L}_1$ adaptive control augmentation for geometric tracking control of quadrotors.

Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics

1 code implementation9 Sep 2021 Pan Zhao, Arun Lakshmanan, Kasey Ackerman, Aditya Gahlawat, Marco Pavone, Naira Hovakimyan

This paper presents an approach towards guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction metrics (CCM) that aims to minimize the $\mathcal L_\infty$ gain from the disturbances to nominal-actual trajectory deviations.

Motion Planning

Distributed Algorithms for Linearly-Solvable Optimal Control in Networked Multi-Agent Systems

no code implementations18 Feb 2021 Neng Wan, Aditya Gahlawat, Naira Hovakimyan, Evangelos A. Theodorou, Petros G. Voulgaris

Distributed algorithms for both discrete-time and continuous-time linearly solvable optimal control (LSOC) problems of networked multi-agent systems (MASs) are investigated in this paper.

Cooperative Path Integral Control for Stochastic Multi-Agent Systems

no code implementations30 Sep 2020 Neng Wan, Aditya Gahlawat, Naira Hovakimyan, Evangelos A. Theodorou, Petros G. Voulgaris

Local control actions that rely only on agents' local observations are designed to optimize the joint cost functions of subsystems.

Compositionality of Linearly Solvable Optimal Control in Networked Multi-Agent Systems

no code implementations28 Sep 2020 Lin Song, Neng Wan, Aditya Gahlawat, Naira Hovakimyan, Evangelos A. Theodorou

The proposed approach achieves both the compositionality and optimality of control actions simultaneously within the cooperative MAS framework in both discrete- and continuous-time in a sample-efficient manner, which reduces the burden of re-computation of the optimal control solutions for the new task on the MASs.

Contraction $\mathcal{L}_1$-Adaptive Control using Gaussian Processes

no code implementations8 Sep 2020 Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos Theodorou

We present $\mathcal{CL}_1$-$\mathcal{GP}$, a control framework that enables safe simultaneous learning and control for systems subject to uncertainties.

Gaussian Processes

L1-GP: L1 Adaptive Control with Bayesian Learning

no code implementations L4DC 2020 Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos Theodorou

We present L1-GP, an architecture based on L1 adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learning.


Learning Probabilistic Intersection Traffic Models for Trajectory Prediction

no code implementations5 Feb 2020 Andrew Patterson, Aditya Gahlawat, Naira Hovakimyan

The safety of these agents is dependent on their ability to predict collisions with other vehicles' future trajectories for replanning and collision avoidance.

Object Recognition Time Series +1

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