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Sarangapani Jagannathan
Researcher at Missouri University of Science and Technology
Publications - 429
Citations - 9376
Sarangapani Jagannathan is an academic researcher from Missouri University of Science and Technology. The author has contributed to research in topics: Control theory & Adaptive control. The author has an hindex of 48, co-authored 414 publications receiving 8228 citations. Previous affiliations of Sarangapani Jagannathan include National Chung Cheng University & University of California, Santa Barbara.
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Output Feedback Control of a Quadrotor UAV Using Neural Networks
TL;DR: It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle.
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Online Optimal Control of Affine Nonlinear Discrete-Time Systems With Unknown Internal Dynamics by Using Time-Based Policy Update
TL;DR: The Hamilton-Jacobi-Bellman equation is solved forward-in-time for the optimal control of a class of general affine nonlinear discrete-time systems without using value and policy iterations and the end result is the systematic design of an optimal controller with guaranteed convergence that is suitable for hardware implementation.
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Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems
TL;DR: This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form that is approximated using a linearly parameterized neural network in the context of event-based sampling.
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Reinforcement Learning Neural-Network-Based Controller for Nonlinear Discrete-Time Systems With Input Constraints
TL;DR: In this paper, an adaptive-critic-based neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of actuator constraints.
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Stochastic optimal control of unknown linear networked control system in the presence of random delays and packet losses
TL;DR: The proposed stochastic optimal control method uses an adaptive estimator (AE) and ideas from Q-learning to solve the infinite horizon optimal regulation of unknown NCS with time-varying system matrices and produces an optimal control scheme that operates forward-in-time manner for unknown linear systems.