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In this paper, robust control problems are investigated for nonlinear continuous-time systems. A momentum-based gradient descent (GD) approach is developed to enhance the convergence performance of parameters in adaptive dynamic programming (ADP). By introducing the idea of momentum, the oscillation in the process of GD is alleviated and the selection of the learning rate becomes more flexible. Under the framework of ADP, the robust control problem is transformed into the optimal control problem by modifying the cost function. To avoid limitations of the initial admissible condition, an additional term is employed in the computation of the current gradient. Based on the online policy iteration algorithm, the momentum-based GD approach is constructed as an improved learning algorithm to optimize the critic network weights. Finally, a simulation is conducted to verify the effectiveness of the established learning strategy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.
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ISSN: 1934-1768
年份: 2024
页码: 2432-2437
语种: 英文
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