IEEE Trans Neural Netw Learn Syst. 2018 Apr;29(4):970-980. doi: 10.1109/TNNLS.2017.2651104. Epub 2017 Feb 1.
In this paper, the problem of adaptive actor-critic (AC) tracking control is investigated for a class of continuous-time nonlinear systems with unknown nonlinearities and quantized inputs. Different from the existing results based on reinforcement learning, the tracking error constraints are considered and new critic functions are constructed to improve the performance further. To ensure that the tracking errors keep within the predefined time-varying boundaries, a tracking error transformation technique is used to constitute an augmented error system. Specific critic functions, rather than the long-term cost function, are introduced to supervise the tracking performance and tune the weights of the AC neural networks (NNs). A novel adaptive controller with a special structure is designed to reduce the effect of the NN reconstruction errors, input quantization, and disturbances. Based on the Lyapunov stability theory, the boundedness of the closed-loop signals and the desired tracking performance can be guaranteed. Finally, simulations on two connected inverted pendulums are given to illustrate the effectiveness of the proposed method.
本文针对一类具有未知非线性和量化输入的连续时间非线性系统，研究了自适应动作-评价器（AC）跟踪控制问题。与基于强化学习的现有结果不同，本文考虑了跟踪误差约束，并构建了新的评价函数以进一步提高性能。为了确保跟踪误差保持在预定义的时变边界内，使用跟踪误差变换技术构成增广误差系统。引入特定的评价函数，而不是长期成本函数，来监督跟踪性能并调整 AC 神经网络（NN）的权重。设计了一种具有特殊结构的新型自适应控制器，以减小 NN 重构误差、输入量化和干扰的影响。基于 Lyapunov 稳定性理论，可以保证闭环信号的有界性和期望的跟踪性能。最后，通过两个连接的倒立摆的仿真验证了所提方法的有效性。
