{'fraction_non_alphanumeric': 0.025495750708215296, 'fraction_numerical': 0.0, 'mean_word_length': 5.1988304093567255, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Finite-horizon neural network-based optimal control design for affine nonlinear continuous-time systems

In this paper, the finite-horizon optimal control design for affine nonlinear continuous-time systems in the presence of known system dynamics is presented. A neural network (NN) is utilized to learn the time-varying solution of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward in time manner. To handle the time varying nature of the value function, the NN with constant weights and time-varying activation function is considered. The update law for tuning the NN weights is derived based on normalized gradient descent approach. To satisfy the terminal constraint and ensure stability, additional terms, one corresponding to the terminal constraint, and the other to stabilize the nonlinear system are added to the novel updating law. A uniformly ultimately boundedness of the non-autonomous closed-loop system is verified by using standard Lyapunov theory. The effectiveness of the proposed method is verified by simulation results.