基于神经模糊技术的电液控制系统设计
Design of an Electro-Hydraulic System Using Neuro-Fuzzy Techniques
摘要：
对性能和质量的日益增长的需求使驱动系统成为工业过程逐步自动化的基础部分。如果需要精确和快速的性能，它们的常规模型变得不合适并且范围有限。因此，重要的是将学习能力融入驱动系统，以提高其实时准确性，成为具有一定智能程度的自主智能体。为了研究这一挑战，本章提出了一个学习控制系统的发展，使用神经模糊技术的跟踪控制器的设计实验电液致动器。本章首先介绍了执行器的神经模糊建模过程。介绍了学习算法，描述了实验系统，给出了建模步骤，包括执行器代表变量的选择、训练和测试数据集的获取、执行器神经模糊逆模型的获取。在本章的第二部分中，我们使用提取的神经模糊模型和它的学习能力，设计基于反馈误差学习技术的执行器位置控制器。通过一组实验结果，我们展示了控制器的泛化性能，它的学习能力，在实时实现的初始神经模糊逆模型，其补偿行动，提高电液跟踪性能。
英文摘要：
Increasing demands in performance and quality make drive systems fundamental parts in the progressive automation of industrial processes. Their conventional models become inappropriate and have limited scope if one requires a precise and fast performance. So, it is important to incorporate learning capabilities into drive systems in such a way that they improve their accuracy in realtime, becoming more autonomous agents with some degree of intelligence. To investigate this challenge, this chapter presents the development of a learning control system that uses neuro-fuzzy techniques in the design of a tracking controller to an experimental electro-hydraulic actuator. We begin the chapter by presenting the neuro-fuzzy modeling process of the actuator. This part surveys the learning algorithm, describes the laboratorial system, and presents the modeling steps as the choice of actuator representative variables, the acquisition of training and testing data sets, and the acquisition of the neuro-fuzzy inverse-model of the actuator. In the second part of the chapter, we use the extracted neuro-fuzzy model and its learning capabilities to design the actuator position controller based on the feedback-error-learning technique. Through a set of experimental results, we show the generalization properties of the controller, its learning capability in actualizing in realtime the initial neuro-fuzzy inverse-model, and its compensation action improving the electro-hydraulics tracking performance.
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