Titlebook: Adaptive Dynamic Programming: Single and Multiple Controllers; Ruizhuo Song,Qinglai Wei,Qing Li Book 2019 Science Press, Beijing and Sprin

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https://doi.org/10.1007/978-981-13-1712-5Optimal control; Multi-player games; Adaptive dynamic programming; Nonlinear systems; Neural network-bas

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https://doi.org/10.1007/978-3-031-46375-4he weighted sum technology, the original multi-objective optimal control problem is transformed to the single one. An ADP method is established for nonlinear time-delay systems to solve the optimal control problem. To demonstrate the presented iterative performance index function sequence is converg

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https://doi.org/10.1007/978-3-031-46375-4tuation, this chapter proposes multiple actor-critic structures to obtain the optimal control via input-output data for unknown nonlinear systems. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into one of several categories. Different performance

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Polyphony: Authorship and Power,obi–Bellman (HJB) equation. Off-policy learning allows the iterative performance index and iterative control to be obtained by completely unknown dynamics. Critic and action networks are used to get the iterative control and iterative performance index, which execute policy evaluation and policy imp

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Lakshmi Bandlamudi,E. V. Ramakrishnangorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index fu

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Bakhtinian Explorations of Indian Cultureing (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks (NN) are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or