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This paper presents a novel stochastic event-based near optimal control strategy to regulate a networked control system (NCS) represented as an uncertain nonlinear continuous time system. An online stochastic actor-critic neural network (NN) based approach is utilized to achieve the near optimal regulation in the presence of network constraints, such as, network induced time-varying delays and random...
In this paper, an event-based near optimal control of uncertain nonlinear discrete time systems is presented by using input-output data and approximate dynamic programming (ADP). The nonlinear system dynamics in affine form are transformed into an input-output form. Then, three neural networks (NN) with event sampled input-output vector are used, namely, the identifier NN to relax the knowledge of...
This paper proposes neural network (NN) approximation-based event-triggered control of multiple-input and multiple output (MIMO) nonlinear discrete-time systems in the context of limited communication over the network. Unlike the traditional NN-based discrete-time control, the weights are updated non-periodically and only at the trigger instants. The Lyapunov direct approach is utilized to arrive...
In this paper, a neural network (NN) based adaptive event-triggered control is developed for a single input and single output (SISO) uncertain nonlinear continuous time system. An explicit design of the event-triggered controller using NN approximation and feedback linearization is presented. The controller dynamics are approximated by using two single layer NNs. In addition, novel weight update laws...
In this paper, the design of a neural network (NN) based adaptive model-based event-triggered control of an uncertain single input single output (SISO) nonlinear discrete time system in affine form is presented. The controller uses an adaptive estimator consisting of a single-layer NN not only to approximate the internal dynamics of an affine nonlinear discrete-time system but also to provide an estimate...
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