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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, the Bellman equation is used to solve the stochastic optimal control of unknown linear discrete-time system with communication imperfections including random delays, packet losses and quantization. A dynamic quantizer for the sensor measurements is proposed which essentially provides system states to the controller. To eliminate the effect of the quantization error, the dynamics of...
In this paper, direct dynamic programming techniques are utilized to solve the Hamilton Jacobi-Bellman equation forward-in-time for the optimal tracking control of general affine nonlinear discrete-time systems using online approximators (OLA's). The proposed approach, referred as adaptive dynamic programming (ADP), is utilized to solve the infinite horizon optimal tracking control of affine nonlinear...
Discrete time approximate dynamic programming (ADP) techniques have been widely used in the recent literature to determine the optimal or near optimal control policies for nonlinear systems. However, an inherent assumption of ADP requires at least partial knowledge of the system dynamics as well as the value of the controlled plant one step ahead. In this work, a novel approach to ADP is attempted...
A nonaffine discrete-time system represented by the nonlinear autoregressive moving average with eXogenous input (NARMAX) representation with unknown nonlinear system dynamics is considered. An equivalent affinelike representation in terms of the tracking error dynamics is first obtained from the original nonaffine nonlinear discrete-time system so that reinforcement-learning-based near-optimal neural...
Automating the task of nanomanipulation is extremely important since it is tedious for humans. This paper proposes an atomic force microscope (AFM) based force controller to push nanoparticles on the substrates. A block phase correlation-based algorithm is embedded into the controller for the compensation of the thermal drift which is considered as the main external uncertainty during nanomanipulation...
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