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To synthesize fixed-final-time control-constrained optimal controllers for discrete-time nonlinear control-affine systems, a single neural network (NN)-based controller called the Finite-horizon Single Network Adaptive Critic is developed in this paper. Inputs to the NN are the current system states and the time-to-go, and the network outputs are the(More)
A model-based reinforcement learning algorithm is developed in this paper for fixed-final-time optimal control of nonlinear systems with soft and hard terminal constraints. Convergence of the algorithm, for linear in the weights neural networks, is proved through a novel idea by showing that the training algorithm is a contraction mapping. Once trained, the(More)
The problem of optimal switching and control of switching systems with nonlinear subsystems is investigated in this paper. An approximate dynamic programming-based algorithm is proposed for learning the optimal cost-to-go function based on the switching instants and the initial conditions. The global optimal switching times for every selected initial(More)
The contents, as presented here, are final. The source files for the numerical analyses can be found at [44]. Abstract: The problem of optimal switching and control of nonlinear switching systems with controlled subsystems is investigated in this study where the mode sequence and the switching times between the modes are unspecified. An approximate dynamic(More)