## 1 Introduction
After completing the joint angle prediction based on the sEMG, there are two types of control methods that allow a rehabilitation exoskeleton to improve human-computer interaction performance and realize the flexible movement of the upper limb rehabilitation training [15]. Passive control mainly facilitates the human-machine coupling process using physical structures. Active control means that the sensor at the end of the robot actively detects the information of the interaction between the system and the environment, feeds it back to the control system, and completes the trajectory correction according to the feedback amount to achieve the purpose of compliant control [16]. The proportional-integral-differential (PID) control scheme adopted by the MITMANUS product developed by the Massachusetts Institute of Technology in the United States [17], the dual-mirror rehabilitation training scheme adopted by Stanford University [18], and the passive mode in the arm therapy robot ARMin all have certain effects [19]. Although passive control methods can help subjects follow the predetermined trajectory with high accuracy, they do not guarantee the comfort and safety of the subjects' upper limb activities [15]. Thus, active compliance control emerges as the times require. The force/position hybrid control method proposed by [PERSON] [20] et al. decomposes the corresponding interactive forces and positions into their respective orthogonal subspaces and controls each of them to achieve a certain compliance effect. The impedance control and admittance control algorithms proposed by [PERSON] et al. [21] use the relationship between the force and the position of the robot end to adjust the dynamic impedance characteristics of the system so that the system can express external compliance. [PERSON] et al. [22] proposed a hybrid active control algorithm combined with human motion intention and verified the effectiveness of the control algorithm based on the human upper limb model and the minimum continuous motion estimation model. [PERSON] et al. [23] proposed a variable admittance control method based on human intentions, environmental forces, and environmental stiffness to address physical human-robot interaction (pHRI) coupled to environments of unknown stiffness. To the best knowledge of the authors, the original contributions of this article are summarized in the following points:1. We proposed an sEMG-based LSSVM joint angle prediction model to obtain the patient's motion intention. According to the physiological characteristics of sEMG, the preprocessing and feature extraction were completed, and the feature optimization of time difference compensation was introduced for the time delay problem that exists in real-time processing. 2. An adaptive sliding mode controller based on an RBF network approximation was proposed for exoskeleton control, which improved the fast approximation ability of the model and the compensation for uncertainty disturbance. The error of the trajectory tracking was dynamically adjusted by acquiring the human-computer interaction force to ensure the flexibility of the rehabilitation process. The remainder of the paper is organized as follows: Section 2 briefly introduces the joint angle prediction method based on sEMG. In Section 3, the controller design based on an RBF network was proposed. Section 4 presents the experimental settings and results. The conclusion and future work are presented in Section 5