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 Neural Network (2)
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 MIMO systems (1)
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Articles 1  8 of 8
FullText Articles in Operations Research, Systems Engineering and Industrial Engineering
Reinforcement LearningBased Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani
Reinforcement LearningBased Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel neural network (NN) based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multiinputmultioutput (MIMO) discretetime strict feedback nonlinear systems. Reinforcement learning in discrete time is proposed for the output feedback controller, which uses three NN: 1) a NN observer to estimate the system states with the inputoutput data; 2) a critic NN to approximate certain strategic utility function; and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. The magnitude constraints are manifested as saturation nonlinearities in the output feedback ...
Block Phase CorrelationBased Automatic Drift Compensation For Atomic Force Microscopes, Qinmin Yang, Eric W. Bohannan, Jagannathan Sarangapani
Block Phase CorrelationBased Automatic Drift Compensation For Atomic Force Microscopes, Qinmin Yang, Eric W. Bohannan, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Automatic nanomanipulation and nanofabrication with an Atomic Force Microscope (AFM) is a precursor for nanomanufacturing. In ambient conditions without stringent environmental controls, nanomanipulation tasks require extensive human intervention to compensate for the many spatial uncertainties of the AFM. Among these uncertainties, thermal drift is especially hard to solve because it tends to increase with time and cannot be compensated simultaneously by feedback. In this paper, an automatic compensation scheme is introduced to measure and estimate drift. This information can be subsequently utilized to compensate for the thermal drift so that a realtime controller for nanomanipulation can be designed as if ...
Decentralized DiscreteTime Neural Network Controller For A Class Of Nonlinear Systems With Unknown Interconnections, Jagannathan Sarangapani
Decentralized DiscreteTime Neural Network Controller For A Class Of Nonlinear Systems With Unknown Interconnections, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel decentralized neural network (NN) controller in discretetime is designed for a class of uncertain nonlinear discretetime systems with unknown interconnections. Neural networks are used to approximate both the uncertain dynamics of the nonlinear systems and the unknown interconnections. Only local signals are needed for the decentralized controller design and the stability of the overall system can be guaranteed using the Lyapunov analysis. Further, controller redesign for the original subsystems is not required when additional subsystems are appended. Simulation results demonstrate the effectiveness of the proposed controller. The NN does not require an offline learning phase and the weights ...
A Robust Controller For The Manipulation Of Micro Scale Objects, Qinmin Yang, Jagannathan Sarangapani
A Robust Controller For The Manipulation Of Micro Scale Objects, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A suite of novel robust controllers is presented for the manipulation and handling of microscale objects in a microelectromechanical system (MEMS) where adhesive, surface tension, friction and van der Waals forces are dominant. Moreover, these forces are typically unknown. The robust controller overcomes the unknown system dynamics and ensures the performance in the presence of actuator constraints by assuming that the upper bounds on these forces are known. On the other hand, for the robust adaptive controller, the unknown forces are estimated online. Using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the closedloop manipulation error is shown for ...
EnergyEfficient Rate Adaptation Mac Protocol For Ad Hoc Wireless Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
EnergyEfficient Rate Adaptation Mac Protocol For Ad Hoc Wireless Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Resource constraints in ad hoc wireless networks require that they are energy efficient during both transmission and rate adaptation. In this paper, we propose a novel energyefficient rate adaptation protocol that selects modulation schemes online to maximize throughput based on channel state while saving energy. This protocol uses the distributed power control (DPC) algorithm (M. Zawodniok et al., 2004) to accurately determine the necessary transmission power and to reduce the energy consumption. Additionally, the transmission rate is altered using energy efficiency as a constraint to meet the required throughput, which is estimated with queue fill ratio. Moreover, backoff scheme is ...
Neural NetworkBased Control Of Nonlinear DiscreteTime Systems In NonStrict Form, Jagannathan Sarangapani, Zheng Chen, Pingan He
Neural NetworkBased Control Of Nonlinear DiscreteTime Systems In NonStrict Form, Jagannathan Sarangapani, Zheng Chen, Pingan He
Electrical and Computer Engineering Faculty Research & Creative Works
A novel reinforcement learningbased adaptive neural network (NN) controller, also referred as the adaptivecritic NN controller, is developed to deliver a desired tracking performance for a class of nonstrict feedback nonlinear discretetime systems in the presence of bounded and unknown disturbances. The adaptive critic NN controller architecture includes a critic NN and two action NNs. The critic NN approximates certain strategic utility function whereas the action neural networks are used to minimize both the strategic utility function and the unknown dynamics estimation errors. The NN weights are tuned online so as to minimize certain performance index. By using gradient descentbased ...
Predictive Congestion Control Mac Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
Predictive Congestion Control Mac Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Available congestion control schemes, for example transport control protocol (TCP), when applied to wireless networks results in a large number of packet drops, unfairness with a significant amount of wasted energy due to retransmissions. To fully utilize the hop by hop feedback information, a suite of novel, decentralized, predictive congestion control schemes are proposed for wireless sensor networks in concert with distributed power control (DPC). Besides providing energy efficient solution, embedded channel estimator in DPC predicts the channel quality. By using the channel quality and node queue utilizations, the onset of network congestion is predicted and congestion control is initiated ...
Neural Network Based Nearly Optimal HamiltonJacobiBellman Solution For Affine Nonlinear DiscreteTime Systems, Jagannathan Sarangapani, Zheng Chen
Neural Network Based Nearly Optimal HamiltonJacobiBellman Solution For Affine Nonlinear DiscreteTime Systems, Jagannathan Sarangapani, Zheng Chen
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, we consider the use of nonlinear networks towards obtaining nearly optimal solutions to the control of nonlinear discretetime systems. The method is based on leastsquares successive approximation solution of the Generalized HamiltonJacobiBellman (HJB) equation. Since successive approximation using the GHJB has not been applied for nonlinear discretetime systems, the proposed recursive method solves the GHJB equation in discretetime on a welldefined region of attraction. The definition of GHJB, PreHamiltonian function, HJB equation and method of updating the control function for the affine nonlinear discrete time systems are proposed. A neural network is used to approximate the GHJB ...