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In this paper, neural networks are used to approximately solve the finite-horizon constrained input H∞ state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game by approximating the cost using a Neural Network. It is shown that the neural network approximation converges uniformly to the game-value function...
Fixed-final time constrained input optimal control laws using neural networks to solve Hamilton-Jacobi-Bellman (HJB) equations for general affine in the input nonlinear systems are proposed. A neural network is used to approximate the time-varying cost function using the method of least-squares on a pre-defined region and hence solve the HJB. The result is a neural network nearly optimal constrained...
In this paper, a switched nonlinear observer design for a varying-order switched system is presented with convergence conditions. It has been implemented to estimate the state variables of the evaporator under varying refrigerant phase transitions which is a switched system with varying order. The convergence of the switched observer is proved using contraction theory. The switched observer design...
This work presents the design of a model-based nonlinear observer of an evaporator (a two-phase flow heat exchanger) for advanced dynamic control of air conditioning and refrigeration systems. A control-oriented, low-order model is derived for the evaporator. Based on sensor measurements of the evaporating temperature, the nonlinear observer can be used to estimate the heat transfer rate and the length...
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