In this study, the problem of intercepting a manoeuvring target is posed in a zero-sum differential game problem for a class of strict-feedback non-linear systems with output and input constraints. By introducing a ba...
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In this study, the problem of intercepting a manoeuvring target is posed in a zero-sum differential game problem for a class of strict-feedback non-linear systems with output and input constraints. By introducing a barrier Lyapunov function and an auxiliary system to deal with the output constraints and input constraints, respectively, a novel backstepping feedforward controller is designed to transform the tracking problem for strict-feedback systems into an equivalence differential game problem for affine systems. Subsequently, a zero-sum differential game strategy is developed by using the adaptive dynamic programming technique. A critic network is constructed to learn the Nash equilibrium of the Hamilton-Jacobi-Isaacs equation online. The convergence properties of the proposed backstepping-based differential games are developed by utilising the Lyapunov method. Finally, the effectiveness of the proposed strategy is demonstrated by simulation using missile-target interception system.
In this paper, a novel dual iterative Q-learning algorithm is developed to solve the optimal battery management and control problems in smart residential environments. The main idea is to use adaptivedynamic programm...
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ISBN:
(纸本)9781479945511
In this paper, a novel dual iterative Q-learning algorithm is developed to solve the optimal battery management and control problems in smart residential environments. The main idea is to use adaptivedynamicprogramming (ADP) technique to obtain the optimal battery management and control scheme iteratively for residential energy systems. In the developed dual iterative Q-learning algorithm, two iterations, including external and internal iterations, are introduced, where internal iteration minimizes the total cost of power loads in each period and the external iteration makes the iterative Q function converge to the optimum. For the first time, the convergence property of iterative Q-learning method is proven to guarantee the convergence property of the iterative Q function. Finally, numerical results are given to illustrate the performance of the developed algorithm.
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