A novel interval type-2 intuition fuzzy brain emotional learning network model (IT2IFBELC) which depends only on the input and output data is proposed for the rehabilitation robot, which is different from model-based control algorithms that require exact dynamic model knowledge of the rehabilitation robot. The proposed model takes advantage of the type-2 intuition fuzzy theory and brain emotional neural network, and this is no rule initially; then, the structure and parameters of IT2IFBELC are tuned online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through an interval type-2 intuition fuzzy inference system (IT2IFIS), and then the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the whole controller is composed of three parts, including the ideal sliding mode controller, the interval type-2 intuition fuzzy brain emotional learning network controller, and a powerful robust compensation controller, and then one Lyapunov function is designed to guarantee the rapid convergence of the control systems. For further illustrating the superiority of this model, several models are studied here for comparison, and the results show that the interval type-2 intuition fuzzy brain emotional learning network model can obtain better satisfactory control performance and be suitable to deal with the influence of the uncertainty of the rehabilitation robot.

中文翻译：

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基于间隔2型直觉模糊脑情感学习网络的多自由度康复机器人数据驱动控制

针对康复机器人，提出了一种仅依赖于输入和输出数据的新型区间2直觉模糊脑情感学习网络模型（IT2IFBELC），该模型不同于基于模型的控制算法，该模型需要精确的康复动态模型知识机器人。所提出的模型利用了2型直觉模糊理论和大脑情感神经网络，这在开始时就不是规则。然后，通过梯度法和Lyapunov函数同时在线调整IT2IFBELC的结构和参数。系统输入数据流通过间隔2型直觉模糊推理系统（IT2IFIS）直接导入到神经网络中，然后将结果随后传递到感官和情感渠道中，共同产生网络的最终输出。也就是说，整个控制器由理想滑模控制器，区间2型直觉模糊脑情感学习网络控制器和强大的鲁棒补偿控制器三部分组成，然后设计了一个Lyapunov函数来保证快速的运动。控制系统的融合。为了进一步说明该模型的优越性，本文研究了几种模型进行比较，结果表明，区间2型直觉模糊脑情感学习网络模型可以获得较好的满意的控制性能，并适合于处理该模型的影响。康复机器人的不确定性。区间2型直觉模糊脑情感学习网络控制器，功能强大的鲁棒补偿控制器，然后设计一个Lyapunov函数来保证控制系统的快速收敛。为了进一步说明该模型的优越性，本文研究了几种模型进行比较，结果表明，区间2型直觉模糊脑情感学习网络模型可以获得较好的满意的控制性能，并适合于处理该模型的影响。康复机器人的不确定性。区间2型直觉模糊脑情感学习网络控制器，功能强大的鲁棒补偿控制器，然后设计一个Lyapunov函数来保证控制系统的快速收敛。为了进一步说明该模型的优越性，本文研究了几种模型进行比较，结果表明，区间2型直觉模糊脑情感学习网络模型可以获得较好的满意的控制性能，并适合于处理该模型的影响。康复机器人的不确定性。