基于学习的鲁棒自适应评判控制研究进展

Research Progress on Learning-based Robust Adaptive Critic Control

在作为人工智能核心技术的机器学习领域,强化学习是一类强调机器在与环境的交互过程中进行学习的方法,其重要分支之一的自适应评判技术与动态规划及最优化设计密切相关.为了有效地求解复杂动态系统的优化控制问题,结合自适应评判,动态规划和人工神经网络产生的自适应动态规划方法已经得到广泛关注,特别在考虑不确定因素和外部扰动时的鲁棒自适应评判控制方面取得了很大进展,并被认为是构建智能学习系统和实现真正类脑智能的必要途径.本文对基于智能学习的鲁棒自适应评判控制理论与主要方法进行梳理,包括自学习鲁棒镇定,自适应轨迹跟踪,事件驱动鲁棒控制,以及自适应H_∞控制设计等,并涵盖关于自适应评判系统稳定性、收敛性、最优性以及鲁棒性的分析.同时,结合人工智能、大数据、深度学习和知识自动化等新技术,也对鲁棒自适应评判控制的发展前景进行探讨.

In the machine learning field, the core technique of artificial intelligence, reinforcement learning is a class of strategies focusing on learning during the interaction process between machine and environment. As an important branch of reinforcement learning, the adaptive critic technique is closely related to dynamic programming and optimization design.In order to effectively solve optimal control problems of complex dynamical systems, the adaptive dynamic programming approach was proposed by combining adaptive critic, dynamic programming with artificial neural networks and has been attracted extensive attention. Particularly, great progress has been obtained on robust adaptive critic control design with uncertainties and disturbances. Now, it has been regarded as a necessary outlet to construct intelligent learning systems and achieve true brain-like intelligence. This paper presents a comprehensive survey on the learning-based robust adaptive critic control theory and methods, including self-learning robust stabilization, adaptive trajectory tracking, event-driven robust control, and adaptive H∞ control design. Therein, it covers a general analysis for adaptive critic systems in terms of stability, convergence, optimality, and robustness. In addition, considering novel techniques such as artificial intelligence,big data, deep learning, and knowledge automation, it also discusses future prospects of robust adaptive critic control.