Intelligent adaptive learning and control for discrete-time nonlinear uncertain systems in multiple environments
Intelligent adaptive learning and control for discrete-time nonlinear uncertain systems in multiple environments
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DOI：
10.1016/j.neucom.2021.07.046
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发表时间：
2021-08-07
期刊：
影响因子：
6
通讯作者：
Dai, Shi-Lu
中科院分区：
文献类型：
--
作者：
Zhang, Jingting;Yuan, Chengzhi;Dai, Shi-Lu
研究了一类离散非线性不确定系统在多种环境下的自适应学习与控制问题。结合离线和在线学习方法，提出了一种新的智能学习控制框架。具体而言，在离线学习模式下，首先提出了一种基于确定性学习（DL）的自适应动态学习方法，以实现在每个预期的个体环境下的相关非线性不确定系统动态的局部准确识别，并获得学习到的知识，并存储在一组恒定的径向基函数神经网络模型。然后，利用学习到的知识，进一步设计了一种在线自适应学习控制方案，包括：（i）由多个基于经验的控制器和一个基于DL的自适应学习控制器组成的在线自适应学习控制机制，旨在为每个单独环境下的对象提供期望的控制性能;以及（ii）由多个识别估计器和基于DL的识别器组成的基于学习的识别机制，旨在识别活动环境并在真实的时间内调度适当的控制策略。为了保证系统在环境过渡期间的稳定性，进一步开发了鲁棒准滑模控制器，并将其嵌入到整个控制器结构中。通过这种新的智能自适应学习控制框架，整个系统不仅能够通过重新利用从离线和在线学习中获得的知识来适应任何预期的（预定义的）环境，而且还能够通过在线主动获取新知识来适应意外的（新的）环境。仿真研究验证了这种新框架的有效性和优势。(c)2021爱思唯尔有限公司版权所有。
This paper focuses on an adaptive learning and control problem for a class of discrete-time nonlinear uncertain systems operating under multiple environments. A novel intelligent learning control framework is proposed by using a combination of offline and online learning methods. Specifically, in the offline learning mode, a deterministic learning (DL) based adaptive dynamics learning approach is first proposed to achieve locally-accurate identification of associated nonlinear uncertain system dynamics under each anticipated individual environment, and the learned knowledge is obtained and stored in a set of constant radial basis function neural network models. Then, with the learned knowledge, an online adaptive learning control scheme is further developed, which consists of: (i) an online adaptive learning control mechanism composed of multiple experience-based controllers and a DL-based adaptive learning controller, aiming to provide desired control performance for the plant operating under each individual environment; and (ii) a learning-based recognition mechanism composed of multiple recognition estimators and a DL-based identifier, aiming to recognize the active environment and schedule appropriate control strategies in real time. To guarantee the system stability during environment transition, a robust quasi-sliding-mode controller is further developed and embedded in the overall controller architecture. With this new intelligent adaptive learning control framework, the overall system is capable of adapting not only to any anticipated (pre-defined) environment by re-utilizing the knowledge obtained from both offline and online learning, but also to unanticipated (new) environments by actively acquiring new knowledge online. Simulation studies are conducted to verify the effectiveness and advantages of this new framework. (c) 2021 Elsevier B.V. All rights reserved.