Real-Time Modular Deep Neural Network-Based Adaptive Control of Nonlinear Systems
Real-Time Modular Deep Neural Network-Based Adaptive Control of Nonlinear Systems
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DOI：
10.1109/lcsys.2021.3081361
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发表时间：
2022-01-01
影响因子：
3
通讯作者：
Dixon, Warren E.
中科院分区：
文献类型：
--
作者：
Le, Duc M.;Greene, Max L.;Dixon, Warren E.
针对不确定控制-仿射非线性系统，提出了一种实时深度神经网络自适应控制体系，用于跟踪时变期望轨迹。采用基于李雅普诺夫的分析方法建立了输出层权值的自适应律和内层权值自适应律的约束条件。与现有的神经网络和基于深度神经网络的控制方法不同，该方法建立了一个框架，可以实时同时更新任意深度深度神经网络的多层权值。实时控制器和权值更新律使系统能够跟踪时变轨迹，同时补偿未知漂移动力学和参数深度神经网络的不确定性。采用基于非光滑lyapunov的分析方法来保证半全局渐近跟踪。对比数值模拟结果验证了该方法的有效性。
A real-time deep neural network (DNN) adaptive control architecture is developed for uncertain control-affine nonlinear systems to track a time-varying desired trajectory. A Lyapunov-based analysis is used to develop adaptation laws for the output-layer weights and develop constraints for inner-layer weight adaptation laws. Unlike existing works in neural network and DNN-based control, the developed method establishes a framework to simultaneously update the weights of multiple layers for a DNN of arbitrary depth in real-time. The real-time controller and weight update laws enable the system to track a time-varying trajectory while compensating for unknown drift dynamics and parametric DNN uncertainties. A nonsmooth Lyapunov-based analysis is used to guarantee semi-global asymptotic tracking. Comparative numerical simulation results are included to demonstrate the efficacy of the developed method.