Neural network control of nonlinear discrete-time systems in affine form in the presence of communication network

Neural network control of nonlinear discrete-time systems in affine form in the presence of communication network. Xu, H., Sahoo, A., & Jagannathan, S. 2014.
abstract bibtex

© 2015 by World Scientific Publishing Co. Pte. Ltd. Stochastic adaptive control of a nonlinear system enclosed by a communication network or referred to as a nonlinear networked control system (NNCS) is a challenging problem due to the presence of unknown network imperfections such as network-induced delays and packet losses. Moreover, the known system dynamics of the original nonlinear system become uncertain and stochastic after the incorporation of the network imperfections due to the communication network within the feedback loop. Therefore, first, a novel NNCS representation incorporating the system uncertainties and network imperfections are derived in this chapter. Subsequently, an online neural network (NN) identifier is developed to identify the control coefficient matrix of the stochastic nonlinear discrete-time system for the purpose of the controller design. Further, critic and action NNs are proposed along with identified system dynamics to determine time-based stochastic optimal adaptive control of NNCS in a forward-in-time manner. Lyapunov stability theory is utilized to demonstrate that all the closed-loop signals are uniformly ultimately bounded (UUB) in the mean. Eventually, to reduce the network traffic between the nonlinear system and the controller, an emerging event-triggered control scheme is developed for the NNCS. The performance of both the controllers is contrasted via simulation.

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 title = {Neural network control of nonlinear discrete-time systems in affine form in the presence of communication network},
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 year = {2014},
 source = {Frontiers of Intelligent Control and Information Processing},
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 abstract = {© 2015 by World Scientific Publishing Co. Pte. Ltd. Stochastic adaptive control of a nonlinear system enclosed by a communication network or referred to as a nonlinear networked control system (NNCS) is a challenging problem due to the presence of unknown network imperfections such as network-induced delays and packet losses. Moreover, the known system dynamics of the original nonlinear system become uncertain and stochastic after the incorporation of the network imperfections due to the communication network within the feedback loop. Therefore, first, a novel NNCS representation incorporating the system uncertainties and network imperfections are derived in this chapter. Subsequently, an online neural network (NN) identifier is developed to identify the control coefficient matrix of the stochastic nonlinear discrete-time system for the purpose of the controller design. Further, critic and action NNs are proposed along with identified system dynamics to determine time-based stochastic optimal adaptive control of NNCS in a forward-in-time manner. Lyapunov stability theory is utilized to demonstrate that all the closed-loop signals are uniformly ultimately bounded (UUB) in the mean. Eventually, to reduce the network traffic between the nonlinear system and the controller, an emerging event-triggered control scheme is developed for the NNCS. The performance of both the controllers is contrasted via simulation.},
 bibtype = {book},
 author = {Xu, H. and Sahoo, A. and Jagannathan, S.}
}

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Pte. Ltd. Stochastic adaptive control of a nonlinear system enclosed by a communication network or referred to as a nonlinear networked control system (NNCS) is a challenging problem due to the presence of unknown network imperfections such as network-induced delays and packet losses. Moreover, the known system dynamics of the original nonlinear system become uncertain and stochastic after the incorporation of the network imperfections due to the communication network within the feedback loop. Therefore, first, a novel NNCS representation incorporating the system uncertainties and network imperfections are derived in this chapter. Subsequently, an online neural network (NN) identifier is developed to identify the control coefficient matrix of the stochastic nonlinear discrete-time system for the purpose of the controller design. Further, critic and action NNs are proposed along with identified system dynamics to determine time-based stochastic optimal adaptive control of NNCS in a forward-in-time manner. Lyapunov stability theory is utilized to demonstrate that all the closed-loop signals are uniformly ultimately bounded (UUB) in the mean. Eventually, to reduce the network traffic between the nonlinear system and the controller, an emerging event-triggered control scheme is developed for the NNCS. 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