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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...
For many control applications, identifying an optimal operating point by maximizing/minimizing a performance function is important. This paper applies the extremum-seeking method to nonaffine, nonlinear discrete-time systems stabilized by an optimal adaptive controller. First, a novel averaging method is used for the nonlinear discrete-time systems to show that their output unique extrema are stable...
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...
A novel multilayer discrete-time neural net paradigm is presented for the identification of multi-input multi-output (MIMO) nonlinear dynamical systems. The major novelty of this approach is a rigorous proof of identification error convergence that reveals a requirement for a new identifier structure and nonstandard weight tuning algorithms. The NN identifier includes modified delta rule weight tuning...
A family of implicit self-tuning regulators (STR) is presented, based on Lyapunov analysis techniques for the control of a class of multi-input and multi-output (MIMO) dynamical systems. Linearity in the parameters is assumed to hold, but the 'estimation error' is considered to be nonzero; this allows control of a larger class of systems and also has the effect of producing a robust controller. Moreover,...
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