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.

Liquid crystalline elastomers (LCEs) are unique smart polymer materials which have attracted many research investigations. They exhibited exceptional physical properties due to the combination of rubber elasticity and liquid crystalline anisotropy, such as the reversible and controllable shape change and mechanical actuation under the external stimuli, and soft elasticity, etc. They have high potential of application in the field of actuators, soft robots, artificial muscles and microelectromechanical systems (MEMS), etc. The formation of composites is an effective way to improve the performances of polymers and expand their application scope. Due to the unique structure and remarkable mechanical, electrical, thermal, and surface properties, carbon nanotubes (CNTs) are often studied as a second phase to produce polymer nanocomposites with high performance. The studies on polymer nanocomposites based on LCEs and CNTs also have been strongly developed in recent years. The relevant research work aimed at different aspects including the preparation of CNT-LCE nanocomposites, the control of nanocomposite structure, the interaction between the CNTs and LCE matrices, the instillation of new physical properties into LCEs and the application of CNT-LCE nanocomposites, etc. This article reviews the exciting research work on these aspects, and some general and effective strategies for the development of CNT-LCE nanocomposites are summarized.

This paper presents a control strategy for the position and posture control of a planar four-link underactuated manipulator with a passive second link based on a back-propagation neural network (BPNN) model. First, using the differential evolution (DE) optimization algorithm, the target angles of all links are calculated. The third and fourth links are then controlled from their initial angles to zero. Next, the control process is divided into two steps: the control objective of the passive link is implemented in the first step, and the control objectives of the active links are implemented in the second step. In the first step, the system is reduced to a planar virtual Pendubot, a BPNN model based on the DE algorithm is trained to obtain the coupling relationship between the links of this Pendubot. This model gives the speed of the first link to move the passive link to approach the target angle. Then a fuzzy controller is designed to indirectly control the passive link to its target angle. In the second step, the position mode of the servo controller is adopted to control all active links to their target angles. Experimental results verified the effectiveness of the proposed control strategy.

Dielectric Elastomer Actuator (DEA) shows the complex characteristics such as the inherent nonlinearity, intricate electromechanical coupling, time-dependent viscoelastic behavior, etc., which make it difficult to establish a dynamic model. This paper proposes a dynamic model based on the physical properties of DE materials to describe the above characteristics. The Gent model and the generalized Kelvin model are used to describe the free energy and the viscoelastic characteristics of DEA, respectively. Based on the experimental results, the differential evolution algorithm is used to identify the model parameters. Comparing the experimental data with the model prediction, the validity and generalization of the model are demonstrated.