Research On Modeling And Control For DEAP Flexible Bionic Actuator

The dielectric electroactive polymer(DEAP) is a novel type of smart materials, which belongs to electronic type of electroactive polymer(EAP). The strain limitation of DEAP is 30% under the high voltage. The merits of DEAP are high energy density, noiselessness, large deformation, flexibility and so on, so it is extremely suitable for the actuator material of bionic robots. However, several chanllenges, such as hysteresis effect, creep, uncertainty, nonlinearity, widely arise in bionic actuator based on DEAP material, therefore, a lot of control strategies can not be applied in DEAP actuator system directly. The main contents of this paper are research of modeling and control strategies based on DEAP actuator. The modeling for DEAP actuator includes the hysteresis modeling based on Prandtl-Ishlinskii model and its parameter identification. The research on control strategies is conducted in two aspects. The first one does not need the physical model of DEAP actuator, which includes generalized predictive control based on T-S model and active disturbance rejection control(ADRC), and the second one need the physical model of DEAP actuator, which includes two kinds of adaptive sliding mode control.The main research contents are as follows:(1) Aiming at the electromechanical characteristic of DEAP actuator system, T-S fuzzy model is adopted to model for DEAP actuator and the generalized predictive control is given based on the model. The experimental results illustrate that this control strategy has very high tracking accuracy and fast response speed.(2) Regarding the hysteresis of DEAP actuator as part of disturbance of the system, a linear active disturbance rejection controller is designed for the system. The experimental results show that the controller has strong robustness and high tracking accuracy.(3) Based on a nonlinear transformation of input, Prandtl-Ishlinskii model is used for hysteresis modeling of DEAP system, and differential evolution algorithm is adopted for parameter identification. Finally, an inverse controller is designed based on the model. The tracking accuracy can reach within 4%.(4) Regarding the DEAP actuator system as the connection of Prandtl-Ishlinskii model with a second order linear system, two sliding mode controllers are designed. The first one adopts the method of hysteresis compensation offline, then a integrator is cascaded with the compensated system, finally an adaptive sliding mode controller is designed. The chattering can be suppressed very well. The second one adopts the least square method for parameter identification online, then an adaptive sliding mode controller is design.