Deep Neural Network-Based Approximate Optimal Tracking for Unknown Nonlinear Systems

• 成果类型: Article
• 署名作者: Greene, Max L.; Bell, Zachary I.; Nivison, Scott; Dixon, Warren E.
• 署名单位: Johns Hopkins University; State University System of Florida; University of Florida
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3246761
• 发表日期: 2023
• 页码: 3171-3177
• 关键词: mathematical models; trajectory; Real-time systems; computational modeling; Adaptation models; Extrapolation; COSTS; Adaptive control; Neural Networks; nonlinear control; Reinforcement Learning

摘要

The infinite horizon optimal tracking problem is solved for a deterministic, control-affine, unknown nonlinear dynamical system. A deep neural network (DNN) is updated in real time to approximate the unknown nonlinear system dynamics. The developed framework uses a multitimescale concurrent learning-based weight update policy, with which the output layer DNN weights are updated in real time, but the internal DNN features are updated discretely and at a slower timescale (i.e., with batch-like updates). The design of the output layer weight update policy is motivated by a Lyapunov-based analysis, and the inner features are updated according to existing DNN optimization algorithms. Simulation results demonstrate the efficacy of the developed technique and compare its performance to existing techniques.