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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)
Formation control of network of multi-agent systems with heterogeneous nonlinear dynamics is formulated as an optimal tracking problem and a decentralized controller is developed using the framework of 'adaptive critics' to solve the optimal control problem. The reference signal is assumed available only in online implementation so its dynamics is(More)
The problem of decentralized control of multi-agent nonlinear systems is solved by introducing the concept of virtual agents to generate reference trajectories to be tracked by the actual agents. The tracking problem as an optimal control problem is formulated in the framework of approximate dynamic programming. Solutions are obtained using `single network(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)