This article proposes a dynamic model and a tracking control strategy for a dielectric elastomer (DE) actuator based on the model predictive controller (MPC). First, the dynamic model of the DE actuator is established, which can describe its asymmetric hysteresis, creep, and even rate-dependent hysteresis behaviors simultaneously. Then, on the basis of the established dynamic model, an inverse compensation controller (ICC) is designed to compensate the hysteresis and creep nonlinearities of the DE actuator on its tracking control. Moreover, the MPC is designed to cooperate with the ICC, which can overcome the influences of the modeling error and uncertainties on the control precision. Finally, several experiments demonstrate the effectiveness of the proposed dynamic model and control strategy.

Soft dielectric elastomer actuators (SDEAs) have manifested tremendous potentials in the field of soft robots. However, the tracking control of the SDEA still confronts enormous challenges because of its complicated nonlinear characteristics. In this paper, a nonlinear proportional-integral-derivative (NPID) controller is devised to fulfil the tracking control of the SDEA. A dynamic model of the SDEA is established, and then applied to construct a simulation system to coarsely tune the parameters of the NPID controller based on the iterative optimisation algorithm. Subsequently, according to the results of the coarse tuning, the parameters of the NPID controller are finely tuned in practical experiments. Finally, the NPID controller of the SDEA is proved to be effective via several tracking control experiments with different reference trajectories.

The dielectric elastomer actuator (DEA) is an intelligent device with an actuation-sensing integration capacity, which exhibits prospective applications in the field of soft robotics. Previous studies mainly focus on the actuation capacity of the DEA, while the studies on its sensing capacity and the practical realization of its actuation-sensing integration still confront great challenges. This article presents a self-sensing motion control scheme to realize the tracking control objective of the DEA, thus leaving out the use of the external displacement sensor. First, a self-sensing model of the DEA is established based on the nonlinear autoregressive with exogenous inputs (NARX) neural network to predict its own displacement. Then, by adopting the dynamic model of the DEA as the control object, a simulation environment based on the iterative learning control architecture is built to obtain the feedforward control sequence for tracking a target trajectory. Next, to enhance the control quality, the proportional–integral feedback controller is designed to combine with the feedforward control sequence to handle the uncertainties in practical experiments, in which the feedback signal is the output of the self-sensing model. Finally, several trajectory tracking control experiments are performed. The maximum value of the relative root-mean-square errors for all experimental results is less than 3.60%, which illustrates the effectiveness of the presented self-sensing motion control scheme.

The high reliance on traditional Chinese medicine (TCM) knowledge and practical skills has led to prolonged training cycles for therapists and a significant talent shortage, posing a severe challenge to the standardization of TCM physiotherapy in rehabilitation. Meanwhile, most physiotherapy robots based on rigid robotics technology suffer from insufficient safety and adaptability, resulting in unsatisfactory treatment effects. This paper presents a novel wearable pneumatic soft physiotherapy device, designed to achieve high‐safety and personalized physiotherapy for shoulder and neck acupoint massage. The device integrates four soft massage actuators, four embedded thin‐film stress sensors, two miniature airflow sources, and a power supply system, enhancing its portability. Additionally, a hybrid control strategy combining feedforward inverse compensation and PID feedback is proposed to regulate and customize the therapy intensity on human acupoints. Experimental results demonstrate that the developed device can apply therapeutic intensities ranging from 0 to 3.45 N to human acupoints, effectively adapting to the human shoulder and neck region to achieve good wearing comfort, and performing safe acupoint massage with customized physiotherapy intensities. This study explores and realizes the integration of emerging soft robotics technology with TCM, providing a paradigm for the development of more soft physiotherapy devices.