# Characterization and Joint Control Study of Pneumatic Artificial Muscles
## 1 Introduction
PID (Proportional Integral Derivative) control is one of the most classical and widely used control theories in the field of control. Some scholars, [PERSON] et al. [10], applied it to performance prediction and validated it on the temperature thermodynamic model of coke ovens. Scholars [PERSON] et al. [11] proposed an explanation to combine PID control with nonlinear systems and used it to establish a three-dimensional closed-loop nonlinear system parameter set to verify the control effectiveness of PID in nonlinear systems. Moreover, scholars [PERSON] et al. [12] designed a new algorithm combining PID control and a hybrid neural network to achieve control speed and accuracy of the hydraulic control system of an unmanned platform. They verified its good control effect on the engine and hydraulic pump in the hydraulic system by simulation and experiment. The above industrial field control for PID is mainly used in thermodynamics, fluid drive, and the neural network, as predictive control is above the application. It will be combined in the pneumatic artificial muscle above the application; the pneumatic artificial muscle combined with the PID control effect and the actual use of demand are not well understood. Hence, a PID control-driven pneumatic artificial muscle has an extremely practical significance and broad market application value. In addition, there are other studies on control strategies for pneumatic artificial muscles. Scholars [PERSON] [13] relied on sliding mode control as a control strategy, in which the mathematical model and load changes no longer influence after the joint system enters the sliding region; it also belongs to nonlinear intelligent control with relatively good robustness. [PERSON] [14] used multilayer self-organizing neural networks to control the joint position of artificial pneumatic muscles, which had some control effect but could not eliminate coordinate oscillations. [PERSON] et al. [15] continuously adjusted the real-time parameters on the host computer by identifying the system parameters online and facing the real-time changing system requirements. Through this continuous change, the system is learning to achieve its target control effect. However, since the pneumatic artificial muscle is a regular repetitive motion, but also a careful consideration of the system's economy, this control method will not be applied. In summary, the use of these strategies requires the establishment of a precise mathematical model of the robot joints, which often affects the effectiveness of such control methods when the model parameters or the applied loads change due to the complexity and unknown nature of the internal and external conditions, combined with the excellent suppleness of the pneumatic artificial muscles themselves. In practical applications, the best control strategy for lower limb rehabilitation robots driven by pneumatic artificial muscles is mainly position control. The position control strategy requires high precision and signal stability of the control system. The classical PID controller is mostly used in medium and large industrial fields such as aerospace, hydraulic fluid field, etc., but it is rarely used in medical or rehabilitation areas for the control of pneumatic artificial muscles as drive elements for lower limb rehabilitation robots. Therefore, in this paper, the joints of a lower limb rehabilitation robot are studied, and an adaptive Proportional Integral Derivative (PID) controller is designed for pneumatic artificial muscles driven by high-speed switching valves, the implementation of which does not depend on an accurate mathematical model for pneumatic artificial muscles.