This article proposes a novel control strategy based on a broad fuzzy neural network (BFNN) which is subjected to contact with the unknown environment. Compared with the conventional fuzzy neural network (NN), a prominent feature can be achieved by taking the advantage of the broad learning system (BLS) to explicitly tackle the problem of how to choose a sufficient number of NN units to approximate the unknown dynamic model. Aiming at providing a soft compliant contact scheme without the requirement of the environment model, an adaptive impedance learning is developed to establish the optimal interaction between the robot and the environment. Meanwhile, the problems related to the state constraints are addressed by incorporating a barrier Lyapunov function (BLF) into the design of a trajectory tracking controller. The proposed method can achieve desired tracking and interaction performance while guaranteeing the stability of the closed-loop system. In addition, simulation and experimental studies are performed to verify the effectiveness of BFNN under optimal impedance control with a two degree-of-freedom (DOF) manipulator and a Baxter robot, respectively.

中文翻译：

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广义模糊神经自适应控制下的最优机器人-环境交互

本文提出了一种基于广泛模糊神经网络（BFNN）的新型控制策略，该网络受到与未知环境的接触。与传统的模糊神经网络（NN）相比，通过利用广泛的学习系统（BLS）的优势明确解决如何选择足够数量的神经网络单元来逼近未知动态模型的问题，可以实现一个突出的特征. 旨在提供一种不需要环境模型的软柔性接触方案，开发了自适应阻抗学习来建立机器人与环境之间的最佳交互。同时，通过将障碍李雅普诺夫函数（BLF）结合到轨迹跟踪控制器的设计中来解决与状态约束相关的问题。所提出的方法可以在保证闭环系统稳定性的同时实现所需的跟踪和交互性能。此外，还进行了仿真和实验研究，以分别使用两个自由度 (DOF) 机械手和 Baxter 机器人验证 BFNN 在最优阻抗控制下的有效性。