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In this paper, the output feedback based finite-horizon near optimal regulation of nonlinear affine discrete-time systems with unknown system dynamics is considered by using neural networks (NNs) to approximate Hamilton-Jacobi-Bellman (HJB) equation solution. First, a NN-based Luenberger observer is proposed to reconstruct both the system states and the control coefficient matrix. Next, reinforcement...
In this paper, the fixed final-time near optimal output regulation of affine nonlinear discrete-time systems with unknown system dynamics is considered. First, a neural network (NN)-based observer is proposed to reconstruct both the system state vector and control coefficient matrix. Next, actor-critic structure is utilized to approximate the time-varying solution of the Hamilton-Jacobi-Bellman (HJB)...
This paper develops a novel neural network (NN) based finite-horizon approximate optimal control of nonlinear continuous-time systems in affine form when the system dynamics are complete unknown. First an online NN identifier is proposed to learn the dynamics of the nonlinear continuous-time system. Subsequently, a second NN is utilized to learn the time-varying solution, or referred to as value function,...
In this paper, finite-horizon optimal control design for affine nonlinear discrete-time systems with totally unknown system dynamics is presented. First, a novel neural network (NN)-based identifier is utilized to learn the control coefficient matrix. This identifier is used together with the action-critic-based scheme to learn the time-varying solution, or referred to as value function, of the Hamilton-Jacobi-Bellman...
In this paper, the finite-horizon optimal control design for affine nonlinear continuous-time systems in the presence of known system dynamics is presented. A neural network (NN) is utilized to learn the time-varying solution of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward in time manner. To handle the time varying nature of the value function, the NN with constant weights and...
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