For the lower limb rehabilitation robot, how to better realize intention recognition is the key issue in the practical application. Recognition of the patient’s movement intention is a challenging research work, which needs to be studied from the shallow to the deep. Specifically, it is necessary to ensure that the movement intention of the normal person can be accurately recognized, and then improve the model to realize the recognition of the movement intention of the patients. Therefore, before studying the patient’s movement intention, it is essential to consider the normal person first, which is also for safety considerations. In recent years, a new Hill-based muscle model has been demonstrated to be capable of directly estimating the joint angle intention in an open-loop form. On this basis, by introducing a recurrent neural network (RNN), the whole prediction process can achieve more accuracy in a closed-loop form. However, for the traditional RNN algorithms, the activation function must be convex, which brings some limitations to the solution of practical problems. Especially, when the convergence speed of the traditional RNN model is limited in the practical applications, as the error continues to decrease, the convergence performance of the traditional RNN model will be greatly affected. To this end, a projected recurrent neural network (PRNN) model is proposed, which relaxes the condition of the convex function and can be used in the saturation constraint case. In addition, the corresponding theoretical proof is given, and the PRNN method with saturation constraint has been successfully applied in the experiment of intention recognition of lower limb movement compared with the traditional RNN model.

对于下肢康复机器人，如何更好地实现意图识别是实际应用中的关键问题。识别患者的运动意图是一项艰巨的研究工作，需要从浅到深进行研究。具体地，需要确保可以正确地识别正常人的运动意图，然后改进模型以实现对患者的运动意图的识别。因此，在研究患者的运动意图之前，必须首先考虑正常人，这也是出于安全考虑。近年来，新的基于Hill的肌肉模型已被证明能够以开环形式直接估计关节角度意图。在此基础上，通过引入递归神经网络（RNN），整个预测过程可以以闭环形式实现更高的准确性。但是，对于传统的RNN算法，激活函数必须是凸函数，这给解决实际问题带来了一些限制。特别是在实际应用中限制传统RNN模型的收敛速度时，随着误差的不断减小，传统RNN模型的收敛性能将受到很大影响。为此，提出了一种投影递归神经网络（PRNN）模型，该模型可以缓解凸函数的条件，并可以在饱和约束条件下使用。另外，给出了相应的理论证明，