ABSTRACT In this paper, an adaptive optimized control strategy based on deep neural networks (DNNs) and an accelerated gradient method is developed for a class of nonlinear strict‐feedback systems to achieve prescribed tracking performance via a dynamic‐memory event‐triggered mechanism (DMETM). Initially, DNNs are applied to approximate both the unknown dynamic functions and the combined unknown functions that contain the performance index. Subsequently, an accelerated gradient adaptive strategy based on the first‐order Taylor series is employed to estimate the DNN weights in real‐time, and auxiliary weight estimation is introduced through the first adaptive law to couple with the second adaptive law. Although the coupled adaptive structure increases the complexity of the analysis, it achieves significantly faster learning convergence and improves transient tracking performance while maintaining stability. Moreover, the virtual controller and the actual controller are constructed using optimized backstepping technology, with two ‐class functions introduced to meet the prescribed accuracy tracking. Lastly, the DMETM is used to reduce the communication burden while ensuring uniform boundedness of all signals. Simulation results are provided to verify the effectiveness of the control strategy.
Wu et al. (Thu,) studied this question.
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