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2020 – today
- 2025
[j35]Jared Town, Zachary Morrison, Rushikesh Kamalapurkar:
Nonuniqueness and convergence to equivalent solutions in observer-based inverse reinforcement learning. Autom. 171: 111977 (2025)- [j34]Max L. Greene
, Masoud S. Sakha, Rushikesh Kamalapurkar, Warren E. Dixon:
Approximate Dynamic Programming for Trajectory Tracking of Switched Systems. IEEE Trans. Autom. Control. 70(2): 1024-1037 (2025) - [i46]Masoud S. Sakha, Rushikesh Kamalapurkar:
Switched Optimal Control with Dwell Time Constraints. CoRR abs/2501.05548 (2025) - [i45]Muzaffar Qureshi, Tochukwu Elijah Ogri, Humberto Ramos, Zachary I. Bell, Rushikesh Kamalapurkar:
Gaussian Process-Based Scalar Field Estimation in GPS-Denied Environments. CoRR abs/2502.17584 (2025) - [i44]Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar:
On Dynamic Mode Decomposition of Control-affine Systems. CoRR abs/2503.10891 (2025) - 2024
- [j33]Zachary Morrison, Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar:
Dynamic Mode Decomposition of Control-Affine Nonlinear Systems Using Discrete Control Liouville Operators. IEEE Control. Syst. Lett. 8: 79-84 (2024) - [j32]Joel A. Rosenfeld, Benjamin P. Russo, Rushikesh Kamalapurkar, Taylor T. Johnson:
The Occupation Kernel Method for Nonlinear System Identification. SIAM J. Control. Optim. 62(3): 1643-1668 (2024) - [j31]Joel A. Rosenfeld, Rushikesh Kamalapurkar:
Dynamic Mode Decomposition With Control Liouville Operators. IEEE Trans. Autom. Control. 69(12): 8571-8586 (2024) - [j30]Jared Town, Zachary Morrison, Rushikesh Kamalapurkar:
Pilot Performance Modeling via Observer-Based Inverse Reinforcement Learning. IEEE Trans. Control. Syst. Technol. 32(6): 2444-2451 (2024) - [c40]Tochukwu Elijah Ogri, Muzaffar Qureshi, Zachary I. Bell, Kristy Waters, Rushikesh Kamalapurkar:
An Adaptive Optimal Control Approach to Monocular Depth Observability Maximization. ACC 2024: 2356-2361 - [c39]Muzaffar Qureshi, Tochukwu Elijah Ogri, Zachary I. Bell, Rushikesh Kamalapurkar:
Scalar Field Mapping with Adaptive High-Intensity Region Avoidance. CCTA 2024: 388-393 - [i43]Tochukwu Elijah Ogri, Muzaffar Qureshi, Zachary I. Bell, Kristy Waters, Rushikesh Kamalapurkar:
An adaptive optimal control approach to monocular depth observability maximization. CoRR abs/2401.09658 (2024) - [i42]Tochukwu Elijah Ogri, Muzaffar Qureshi, Zachary I. Bell, Rushikesh Kamalapurkar:
State and Input Constrained Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems. CoRR abs/2406.18804 (2024) - [i41]Muzaffar Qureshi, Tochukwu Elijah Ogri, Zachary I. Bell, Rushikesh Kamalapurkar:
Scalar Field Mapping with Adaptive High-Intensity Region Avoidance. CoRR abs/2407.13543 (2024) - [i40]Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar:
On Convergent Dynamic Mode Decomposition and its Equivalence with Occupation Kernel Regression. CoRR abs/2409.12285 (2024) - 2023
- [j29]Zachary Morrison, Benjamin P. Russo, Ying Zhao Lian, Rushikesh Kamalapurkar:
Fault Detection via Occupation Kernel Principal Component Analysis. IEEE Control. Syst. Lett. 7: 2695-2700 (2023) - [j28]Joel A. Rosenfeld, Rushikesh Kamalapurkar:
Singular Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 22(3): 2357-2381 (2023) - [c38]Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar:
Carleman Lifting for Nonlinear System Identification with Guaranteed Error Bounds. ACC 2023: 929-934 - [c37]Efrain Gonzalez, Ladan Avazpour, Rushikesh Kamalapurkar, Joel A. Rosenfeld:
Modeling Partially Unknown Dynamics with Continuous Time DMD*. ACC 2023: 2913-2918 - [c36]Jared Town, Zachary Morrison, Rushikesh Kamalapurkar:
Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning. ACC 2023: 3989-3994 - [c35]Tochukwu Elijah Ogri, S. M. Nahid Mahmud, Zachary I. Bell, Rushikesh Kamalapurkar:
Output Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model. CCTA 2023: 645-650 - [c34]Tochukwu Elijah Ogri, Zachary I. Bell, Rushikesh Kamalapurkar:
State and Parameter Estimation for Affine Nonlinear Systems. CDC 2023: 1517-1522 - [c33]Joel A. Rosenfeld, Rushikesh Kamalapurkar:
Convergent Dynamic Mode Decomposition. CDC 2023: 4972-4977 - [i39]Zachary Morrison, Benjamin P. Russo, Yingzhao Lian, Rushikesh Kamalapurkar:
Fault Detection via Occupation Kernel Principal Component Analysis. CoRR abs/2303.11138 (2023) - [i38]Tochukwu Elijah Ogri, Zachary I. Bell, Rushikesh Kamalapurkar:
State and Parameter Estimation for Affine Nonlinear Systems. CoRR abs/2304.01526 (2023) - [i37]Jared Town, Zachary Morrison, Rushikesh Kamalapurkar:
Pilot Performance modeling via observer-based inverse reinforcement learning. CoRR abs/2307.13150 (2023) - 2022
- [j27]Ryan Self, Moad Abudia, S. M. Nahid Mahmud, Rushikesh Kamalapurkar:
Model-based inverse reinforcement learning for deterministic systems. Autom. 140: 110242 (2022) - [j26]Ashwin Dani, Zhen Kan, Rushikesh Kamalapurkar, Nicholas R. Gans:
Editorial: Safety in Collaborative Robotics and Autonomous Systems. Frontiers Robotics AI 9 (2022) - [j25]Joel A. Rosenfeld, Rushikesh Kamalapurkar, L. Forest Gruss, Taylor T. Johnson:
Dynamic Mode Decomposition for Continuous Time Systems with the Liouville Operator. J. Nonlinear Sci. 32(1): 5 (2022) - [i36]S. M. Nahid Mahmud, Moad Abudia, Scott A. Nivison, Zachary I. Bell, Rushikesh Kamalapurkar:
Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning. CoRR abs/2204.01409 (2022) - [i35]Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar:
Carleman Lifting for Nonlinear System Identification with Guaranteed Error Bounds. CoRR abs/2205.15009 (2022) - [i34]Tochukwu Elijah Ogri, S. M. Nahid Mahmud, Zachary I. Bell, Rushikesh Kamalapurkar:
Output Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model. CoRR abs/2210.06637 (2022) - [i33]Jared Town, Zachary Morrison, Rushikesh Kamalapurkar:
Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning. CoRR abs/2210.16299 (2022) - 2021
- [j24]Ryan Self, Kevin Coleman, He Bai, Rushikesh Kamalapurkar:
Online Observer-Based Inverse Reinforcement Learning. IEEE Control. Syst. Lett. 5(6): 1922-1927 (2021) - [j23]S. M. Nahid Mahmud, Scott A. Nivison, Zachary I. Bell, Rushikesh Kamalapurkar:
Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties. Frontiers Robotics AI 8: 733104 (2021) - [j22]Ghananeel Rotithor, Daniel Trombetta, Rushikesh Kamalapurkar, Ashwin P. Dani:
Full- and Reduced-Order Observers for Image-Based Depth Estimation Using Concurrent Learning. IEEE Trans. Control. Syst. Technol. 29(6): 2647-2653 (2021) - [c32]Ryan Self, Kevin Coleman, He Bai, Rushikesh Kamalapurkar:
Online Observer-Based Inverse Reinforcement Learning. ACC 2021: 1959-1964 - [c31]S. M. Nahid Mahmud, Katrine Hareland, Scott A. Nivison, Zachary I. Bell, Rushikesh Kamalapurkar:
A Safety Aware Model-Based Reinforcement Learning Framework for Systems with Uncertainties. ACC 2021: 1979-1984 - [c30]Joel A. Rosenfeld, Rushikesh Kamalapurkar, L. Forest Gruss, Taylor T. Johnson:
On Occupation Kernels, Liouville Operators, and Dynamic Mode Decomposition. ACC 2021: 3957-3962 - [i32]Moad Abudia, Tejasvi Channagiri, Joel A. Rosenfeld, Rushikesh Kamalapurkar:
Control Occupation Kernel Regression for Nonlinear Control-Affine Systems. CoRR abs/2106.00103 (2021) - [i31]Efrain Gonzalez, Moad Abudia, Michael Jury, Rushikesh Kamalapurkar, Joel A. Rosenfeld:
Anti-Koopmanism. CoRR abs/2106.00106 (2021) - [i30]Rushikesh Kamalapurkar, Joel A. Rosenfeld:
An occupation kernel approach to optimal control. CoRR abs/2106.00663 (2021) - [i29]Joel A. Rosenfeld, Rushikesh Kamalapurkar:
Singular Dynamic Mode Decompositions. CoRR abs/2106.02639 (2021) - [i28]S. M. Nahid Mahmud, Moad Abudia, Scott A. Nivison, Zachary I. Bell, Rushikesh Kamalapurkar:
Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems. CoRR abs/2110.00271 (2021) - 2020
- [c29]Ryan Self, Moad Abudia, Rushikesh Kamalapurkar:
Online inverse reinforcement learning for systems with disturbances. ACC 2020: 1118-1123 - [c28]Max L. Greene, Moad Abudia, Rushikesh Kamalapurkar, Warren E. Dixon:
Model-Based Reinforcement Learning for Optimal Feedback Control of Switched Systems. CDC 2020: 162-167 - [c27]Ryan Self, S. M. Nahid Mahmud, Katrine Hareland, Rushikesh Kamalapurkar:
Online inverse reinforcement learning with limited data. CDC 2020: 603-608 - [c26]Moad Abudia, Michael Harlan, Ryan Self, Rushikesh Kamalapurkar:
Switched Optimal Control and Dwell Time Constraints: A Preliminary Study. CDC 2020: 3261-3266 - [i27]Ryan Self, Moad Abudia, Rushikesh Kamalapurkar:
Online inverse reinforcement learning with unknown disturbances. CoRR abs/2003.03912 (2020) - [i26]S. M. Nahid Mahmud, Ryan Self, Katrine Hareland, Rushikesh Kamalapurkar:
A safety aware model based reinforcement learning framework for systems with uncertainties. CoRR abs/2007.12666 (2020) - [i25]Ghananeel Rotithor, Daniel Trombetta, Rushikesh Kamalapurkar, Ashwin Dani:
Extension of Full and Reduced Order Observers for Image-based Depth Estimation using Concurrent Learning. CoRR abs/2008.04809 (2020) - [i24]Ryan Self, S. M. Nahid Mahmud, Katrine Hareland, Rushikesh Kamalapurkar:
Online inverse reinforcement learning with limited data. CoRR abs/2008.08972 (2020) - [i23]Ryan Self, Kevin Coleman, He Bai, Rushikesh Kamalapurkar:
Online Observer-Based Inverse Reinforcement Learning. CoRR abs/2011.02057 (2020) - [i22]Moad Abudia, Michael Harlan, Ryan Self, Rushikesh Kamalapurkar:
Switched Optimal Control and Dwell Time Constraints: A Preliminary Study. CoRR abs/2011.02351 (2020)
2010 – 2019
- 2019
- [j21]Sandesh Thapa, Ryan V. Self, Rushikesh Kamalapurkar, He Bai:
Cooperative Manipulation of an Unknown Payload With Concurrent Mass and Drag Force Estimation. IEEE Control. Syst. Lett. 3(4): 907-912 (2019) - [j20]Rushikesh Kamalapurkar, Joel A. Rosenfeld, Anup Parikh, Andrew R. Teel, Warren E. Dixon:
Invariance-Like Results for Nonautonomous Switched Systems. IEEE Trans. Autom. Control. 64(2): 614-627 (2019) - [j19]Joel A. Rosenfeld, Rushikesh Kamalapurkar, Warren E. Dixon:
The State Following Approximation Method. IEEE Trans. Neural Networks Learn. Syst. 30(6): 1716-1730 (2019) - [c25]Ryan Self, Michael Harlan, Rushikesh Kamalapurkar:
Model-based reinforcement learning for output-feedback optimal control of a class of nonlinear systems. ACC 2019: 2378-2383 - [c24]Ghananeel Rotithor, Ryan Saltus, Rushikesh Kamalapurkar, Ashwin Dani:
Observer Design for Structure from Motion using Concurrent Learning. ACC 2019: 2384-2389 - [c23]Ryan Self, Michael Harlan, Rushikesh Kamalapurkar:
Online inverse reinforcement learning for nonlinear systems. CCTA 2019: 296-301 - [c22]Joel A. Rosenfeld, Rushikesh Kamalapurkar, Benjamin Russo, Taylor T. Johnson:
Occupation Kernels and Densely Defined Liouville Operators for System Identification. CDC 2019: 6455-6460 - [c21]Ghananeel Rotithor, Daniel Trombetta, Rushikesh Kamalapurkar, Ashwin P. Dani:
Reduced Order Observer for Structure from Motion using Concurrent Learning. CDC 2019: 6815-6820 - [i21]Rushikesh Kamalapurkar:
Output-feedback online optimal control for a class of nonlinear systems. CoRR abs/1903.02078 (2019) - 2018
- [j18]Rushikesh Kamalapurkar, Justin R. Klotz, Patrick Walters, Warren E. Dixon:
Model-Based Reinforcement Learning in Differential Graphical Games. IEEE Trans. Control. Netw. Syst. 5(1): 423-433 (2018) - [j17]Patryk Deptula, Joel A. Rosenfeld, Rushikesh Kamalapurkar, Warren E. Dixon:
Approximate Dynamic Programming: Combining Regional and Local State Following Approximations. IEEE Trans. Neural Networks Learn. Syst. 29(6): 2154-2166 (2018) - [j16]Patrick Walters, Rushikesh Kamalapurkar, Forrest Voight, Eric M. Schwartz, Warren E. Dixon:
Online Approximate Optimal Station Keeping of a Marine Craft in the Presence of an Irrotational Current. IEEE Trans. Robotics 34(2): 486-496 (2018) - [j15]Anup Parikh, Rushikesh Kamalapurkar, Warren E. Dixon:
Target Tracking in the Presence of Intermittent Measurements via Motion Model Learning. IEEE Trans. Robotics 34(3): 805-819 (2018) - [c20]Rushikesh Kamalapurkar:
Linear inverse reinforcement learning in continuous time and space. ACC 2018: 1683-1688 - [i20]Rushikesh Kamalapurkar:
Inverse reinforcement learning in continuous time and space. CoRR abs/1801.07663 (2018) - [i19]Ryan Self, Michael Harlan, Rushikesh Kamalapurkar:
Online inverse reinforcement learning for nonlinear systems. CoRR abs/1811.10471 (2018) - 2017
- [j14]Serhat Obuz, Justin R. Klotz, Rushikesh Kamalapurkar, Warren E. Dixon:
Unknown time-varying input delay compensation for uncertain nonlinear systems. Autom. 76: 222-229 (2017) - [j13]Rushikesh Kamalapurkar, Benjamin Reish, Girish Chowdhary, Warren E. Dixon:
Concurrent Learning for Parameter Estimation Using Dynamic State-Derivative Estimators. IEEE Trans. Autom. Control. 62(7): 3594-3601 (2017) - [j12]Rushikesh Kamalapurkar, Lindsey Andrews, Patrick Walters, Warren E. Dixon:
Model-Based Reinforcement Learning for Infinite-Horizon Approximate Optimal Tracking. IEEE Trans. Neural Networks Learn. Syst. 28(3): 753-758 (2017) - [c19]Rushikesh Kamalapurkar:
Online output-feedback parameter and state estimation for second order linear systems. ACC 2017: 5672-5677 - [c18]Rushikesh Kamalapurkar:
Simultaneous state and parameter estimation for second-order nonlinear systems. CDC 2017: 2164-2169 - [c17]Rushikesh Kamalapurkar, Warren E. Dixon, Andrew R. Teel:
On reduction of differential inclusions and Lyapunov stability. CDC 2017: 5499-5504 - [i18]Rushikesh Kamalapurkar, Justin R. Klotz, Patrick Walters, Warren E. Dixon:
Model-based reinforcement learning in differential graphical games. CoRR abs/1702.08584 (2017) - [i17]Rushikesh Kamalapurkar:
Simultaneous State and Parameter Estimation for Second-Order Nonlinear Systems. CoRR abs/1703.07068 (2017) - [i16]Rushikesh Kamalapurkar, Warren E. Dixon, Andrew R. Teel:
On reduction of differential inclusions and Lyapunov stability. CoRR abs/1703.07071 (2017) - [i15]Patrick Walters, Rushikesh Kamalapurkar, Forrest Voight, Eric M. Schwartz, Warren E. Dixon:
Online Approximate Optimal Station Keeping of a Marine Craft in the Presence of a Current. CoRR abs/1710.10511 (2017) - 2016
- [j11]Rushikesh Kamalapurkar, Patrick Walters, Warren E. Dixon:
Model-based reinforcement learning for approximate optimal regulation. Autom. 64: 94-104 (2016) - [j10]Rushikesh Kamalapurkar, Joel A. Rosenfeld, Warren E. Dixon:
Efficient model-based reinforcement learning for approximate online optimal control. Autom. 74: 247-258 (2016) - [j9]Rushikesh Kamalapurkar, Nicholas R. Fischer, Serhat Obuz, Warren E. Dixon:
Time-Varying Input and State Delay Compensation for Uncertain Nonlinear Systems. IEEE Trans. Autom. Control. 61(3): 834-839 (2016) - [j8]Teng-Hu Cheng, Qiang Wang, Rushikesh Kamalapurkar, Huyen T. Dinh, Matthew J. Bellman, Warren E. Dixon:
Identification-Based Closed-Loop NMES Limb Tracking With Amplitude-Modulated Control Input. IEEE Trans. Cybern. 46(7): 1679-1690 (2016) - [i14]Rushikesh Kamalapurkar:
Online Output-Feedback Parameter and State Estimation for Second Order Linear Systems. CoRR abs/1609.05879 (2016) - [i13]Rushikesh Kamalapurkar, Joel A. Rosenfeld, Anup Parikh, Andrew R. Teel, Warren E. Dixon:
A Corollary for Switched Nonsmooth Systems with Applications to Switching in Adaptive Control. CoRR abs/1609.05880 (2016) - 2015
- [j7]Rushikesh Kamalapurkar, Huyen T. Dinh, Shubhendu Bhasin, Warren E. Dixon:
Approximate optimal trajectory tracking for continuous-time nonlinear systems. Autom. 51: 40-48 (2015) - [j6]Marcus Johnson, Rushikesh Kamalapurkar, Shubhendu Bhasin, Warren E. Dixon:
Approximate N -Player Nonzero-Sum Game Solution for an Uncertain Continuous Nonlinear System. IEEE Trans. Neural Networks Learn. Syst. 26(8): 1645-1658 (2015) - [c16]Rushikesh Kamalapurkar, Joel A. Rosenfeld, Warren E. Dixon:
State following (StaF) kernel functions for function approximation part II: Adaptive dynamic programming. ACC 2015: 521-526 - [c15]Joel A. Rosenfeld, Rushikesh Kamalapurkar, Warren E. Dixon:
State following (StaF) kernel functions for function approximation Part I: Theory and motivation. ACC 2015: 1217-1222 - [c14]Justin R. Klotz, Lindsey Andrews, R. Kamalapurkar, Warren E. Dixon:
Decentralized monitoring of leader-follower networks of uncertain nonlinear systems. ACC 2015: 1393-1398 - [c13]Patrick Walters, Rushikesh Kamalapurkar, Warren E. Dixon:
Approximate optimal online continuous-time path-planner with static obstacle avoidance. CDC 2015: 650-655 - [c12]Anup Parikh, Rushikesh Kamalapurkar, Hsi-Yuan Chen, Warren E. Dixon:
Homography based visual servo control with scene reconstruction. CDC 2015: 6972-6977 - [i12]Rushikesh Kamalapurkar, Nicholas R. Fischer, Serhat Obuz, Warren E. Dixon:
Time-Varying Input and State Delay Compensation for Uncertain Nonlinear Systems. CoRR abs/1501.03810 (2015) - [i11]Rushikesh Kamalapurkar, Joel A. Rosenfeld, Warren E. Dixon:
Efficient model-based reinforcement learning for approximate online optimal. CoRR abs/1502.02609 (2015) - [i10]Rushikesh Kamalapurkar, Lindsey Andrews, Patrick Walters, Warren E. Dixon:
Model-based reinforcement learning for infinite-horizon approximate optimal tracking. CoRR abs/1506.00685 (2015) - [i9]Rushikesh Kamalapurkar, Ben Reish, Girish Chowdhary, Warren E. Dixon:
Concurrent learning for parameter estimation using dynamic state-derivative estimators. CoRR abs/1507.08903 (2015) - [i8]Anup Parikh, Rushikesh Kamalapurkar, Warren E. Dixon:
Integral Concurrent Learning: Adaptive Control with Parameter Convergence without PE or State Derivatives. CoRR abs/1512.03464 (2015) - 2014
- [j5]Huyen T. Dinh, R. Kamalapurkar, Shubhendu Bhasin, Warren E. Dixon:
Dynamic neural network-based robust observers for uncertain nonlinear systems. Neural Networks 60: 44-52 (2014) - [j4]Nicholas R. Fischer, Zhen Kan, Rushikesh Kamalapurkar, Warren E. Dixon:
Saturated RISE Feedback Control for a Class of Second-Order Nonlinear Systems. IEEE Trans. Autom. Control. 59(4): 1094-1099 (2014) - [c11]Justin Klotz, R. Kamalapurkar, Warren E. Dixon:
Concurrent learning-based network synchronization. ACC 2014: 796-801 - [c10]Rushikesh Kamalapurkar, Justin Klotz, Warren E. Dixon:
Model-based reinforcement learning for on-line feedback-Nash equilibrium solution of N-player nonzero-sum differential games. ACC 2014: 3000-3005 - [c9]Patrick Walters, Rushikesh Kamalapurkar, Lindsey Andrews, Warren E. Dixon:
Online approximate optimal path-following for a mobile robot. CDC 2014: 4536-4541 - [c8]Rushikesh Kamalapurkar, Lindsey Andrews, Patrick Walters, Warren E. Dixon:
Model-based reinforcement learning for infinite-horizon approximate optimal tracking. CDC 2014: 5083-5088 - [c7]Lindsey Andrews, Justin Klotz, R. Kamalapurkar, Warren E. Dixon:
Adaptive dynamic programming for terminally constrained finite-horizon optimal control problems. CDC 2014: 5095-5100 - 2013
- [j3]Shubhendu Bhasin, R. Kamalapurkar, Marcus Johnson, Kyriakos G. Vamvoudakis, Frank L. Lewis, Warren E. Dixon:
A novel actor-critic-identifier architecture for approximate optimal control of uncertain nonlinear systems. Autom. 49(1): 82-92 (2013) - [j2]Shubhendu Bhasin, R. Kamalapurkar, Huyen T. Dinh, Warren E. Dixon:
Robust Identification-Based State Derivative Estimation for Nonlinear Systems. IEEE Trans. Autom. Control. 58(1): 187-192 (2013) - [j1]Nicholas R. Fischer, Rushikesh Kamalapurkar, Warren E. Dixon:
LaSalle-Yoshizawa Corollaries for Nonsmooth Systems. IEEE Trans. Autom. Control. 58(9): 2333-2338 (2013) - [c6]Rushikesh Kamalapurkar, Huyen T. Dinh, Patrick Walters, Warren E. Dixon:
Approximate optimal cooperative decentralized control for consensus in a topological network of agents with uncertain nonlinear dynamics. ACC 2013: 1320-1325 - [c5]Huyen T. Dinh, Nicholas R. Fischer, R. Kamalapurkar, Warren E. Dixon:
Output feedback control for uncertain nonlinear systems with slowly varying input delay. ACC 2013: 1745-1750 - [c4]Rushikesh Kamalapurkar, Patrick Walters, Warren E. Dixon:
Concurrent learning-based approximate optimal regulation. CDC 2013: 6256-6261 - [i7]Rushikesh Kamalapurkar, Huyen T. Dinh, Shubhendu Bhasin, Warren E. Dixon:
Approximately Optimal Trajectory Tracking for Continuous Time Nonlinear Systems. CoRR abs/1301.7664 (2013) - [i6]Rushikesh Kamalapurkar, Patrick Walters, Warren E. Dixon:
Concurrent learning-based approximate optimal regulation. CoRR abs/1304.3477 (2013) - [i5]Rushikesh Kamalapurkar, Huyen T. Dinh, Patrick Walters, Warren E. Dixon:
Approximate optimal cooperative decentralized control for consensus in a topological network of agents with uncertain nonlinear dynamics. CoRR abs/1304.3479 (2013) - [i4]Rushikesh Kamalapurkar, Justin Klotz, Ryan Downey, Warren E. Dixon:
Supporting Lemmas for RISE-based Control Methods. CoRR abs/1306.3432 (2013) - [i3]Patrick Walters, Rushikesh Kamalapurkar, Warren E. Dixon:
Online Approximate Optimal Station Keeping of an Autonomous Underwater Vehicle. CoRR abs/1310.0063 (2013) - [i2]Patrick Walters, Rushikesh Kamalapurkar, Lindsey Andrews, Warren E. Dixon:
Online Approximate Optimal Path-Following for a Kinematic Unicycle. CoRR abs/1310.0064 (2013) - [i1]Rushikesh Kamalapurkar, Justin Klotz, Warren E. Dixon:
Concurrent learning-based online approximate feedback-Nash equilibrium solution of N-player nonzero-sum differential games. CoRR abs/1310.1384 (2013) - 2012
- [c3]Nicholas R. Fischer, R. Kamalapurkar, Norman G. Fitz-Coy, Warren E. Dixon:
Lyapunov-based control of an uncertain Euler-Lagrange system with time-varying input delay. ACC 2012: 3919-3924 - [c2]Nicholas R. Fischer, R. Kamalapurkar, Nitin Sharma, Warren E. Dixon:
RISE-based control of an uncertain nonlinear system with time-varying state delays. CDC 2012: 3502-3507 - 2011
- [c1]Huyen T. Dinh, R. Kamalapurkar, Shubhendu Bhasin, Warren E. Dixon:
Dynamic neural network-based robust observers for second-order uncertain nonlinear systems. CDC/ECC 2011: 7543-7548
Coauthor Index
[j34] [c28] [j20] [j19] [j18] [j17] [j16] [j15] [j14] [j13] [j12] [c17] [i18] [i16] [i15] [j11] [j10] [j9] [j8] [i13] [j7] [j6] [c16] [c15] [c14] [c13] [c12] [i12] [i11] [i10] [i9] [i8] [j5] [j4] [c11] [c10] [c9] [c8] [c7] [j3] [j2] [j1] [c6] [c5] [c4] [i7] [i6] [i5] [i4] [i3] [i2] [i1] [c3] [c2] [c1]
Ryan V. Self
aka: Ryan Self
aka: Ryan Self
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