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This paper describes the establishment of an enhanced mathematical model and an inversion-based controller based on the proposed model for a trilayer conjugated polymer actuator that will steer a cochlear implant through a 3-D structure. The multilayer electroactive polymer actuator that operates in air will suit many biomedical applications. We propose to use viscoelastic models for the conducting polymer and membrane layers of the actuator so that its mechanical properties can be incorporated into the actuator more accurately. The proposed model accurately predicts the frequency response of the electrical admittance and curvature of the conjugated polymer actuators, and its efficacy for different actuators has been experimentally evaluated. In addition, an inversion-based controller without an external sensor for position feedback data has successfully been evaluated to further validate the ability of the proposed model for sensorless position control of the actuators.

In this paper, we present a new model to describe the displacement response of a trilayer conjugated polymer actuator that can operate in dry environments. The proposed model based on the working principle of conjugated polymer actuators includes diffusion impedances combined with double layer capacitors and charge transfer resistors. The parameters of the model are estimated by using a nonlinear least square estimation method by comparing simulation and experimental results. The proposed model is very useful in predicting the impedance as well as the displacement response of the polymer actuators accurately. Based on the proposed model, an inversion-based controller not requiring an external sensor for position feedback data is implemented and compared with the experimental results. The experimental position control results have confirmed the feasibility and efficacy of the proposed model in describing the time response of the actuators and their position control without using externally provided position data.

This article investigates the effect of two uncertain parameters on a recent new model of conjugated polymer actuators. These uncertain parameters are the diffusion coefficient (D) and the double-layer capacitance (Cdl), which are difficult to measure directly. The model sensitivity to these parameters is analysed and a parameter estimation study is performed using artificially generated data. The parameter estimation method used in this article is based on a Bayesian cost function, and gives us an insight on how much the estimation can be trusted, which is useful information for the design of controllers. Results indicate that for controllers to be designed effectively using this model, the double-layer capacitance is the best known parameter and should therefore be designed for with greater confidence in its value, while the controller should be much more robust with respect to the diffusion coefficient, which should be treated as a stochastic variable for a certain range of possible values.

In this paper, a Prandtl-Ishlinskii hysteresis model (PI) is used to build a rate-independent hysteresis model for a class of conducting polymer actuators typified by tri-layer conjugated polymer actuators. Firstly, an off-line method is proposed to identify a discretization density function for the hysteresis model, and then a linear transfer function for the actuator is identified using the PI inverse model. Secondly, a neural network approach is proposed to realize an adaptive on-line identification method for the density function of the PI hysteresis model. In the back propagation (BP) algorithm for the neural network, the discretization PI operator is considered as an operational function of the neural network and the density function is considered as the power value. Finally, the simulation and experimental results are presented to demonstrate the validity of the model identification method and the actuator model.

This paper presents the effect of three uncertain parameters on a recent model of conjugated polymer actuators and implements a robust controller based on the model developed for this actuator. These uncertain parameters are the diffusion coefficient (D), the charge capacitance (C d ) and the charge-to-strain ratio (α), which are difficult to measure directly. The parameter estimation method used in this article is based on a Bayesian cost function, and gives us an insight on how much the estimation can be trusted, which is useful information for the design of controllers. Results indicate that the charge capacitance is the best known parameter and should therefore be designed for with greater confidence in its value, while the controller should be much more robust with respect to the diffusion coefficient and the charge-to-strain ratio. Based on this finding, a robust controller based on the quantitative feedback theory is implemented and compared with a conventional PID controller. This results in an improvement of the control performance.

This paper proposes an adaptive sliding mode control methodology to enhance the positioning ability of conducting polymer actuators typified by tri-layer conjugated polymer actuators. This is motivated by the search for an effective control strategy to command such actuators to a desired configuration in the presence of parametric uncertainties and unmodeled disturbances. After analyzing the stability of the adaptive sliding mode control system, experiments were conducted to demonstrate its satisfactory tracking ability, based on a series of experimental results. Implementation of the control law requires a valid model of the conducting polymer actuator and boundaries of the uncertainties and disturbances. Based on the theoretical and experimental results presented, the adaptive sliding mode control methodology is very attractive in the field of smart actuators which contain significant uncertainties and disturbances.