Operator based Robust Nonlinear Control Design to an Ionic Polymer Metal Composite with Uncertainties and Input Constraints

Abstract

Abstract:In this paper, robust nonlinear control design to an ionic polymer metal composite (IPMC) with uncertainties and input constraints is studied. The IPMC is a novel smart polymer material, and many potential applications for low mass high displacement actuators in biomedical and robotic systems have been shown. In general, the IPMC has highly nonlinear property, and the control input is subject to some constraints to ensure safety and longer service life of IPMC. Moreover, there exist uncertainties caused by identifying some physical parameters and approximate calculation in dynamic model. As a result, considering measurement error of parameters and model error, a practical nonlinear model is obtained, and a nonlinear robust control design with uncertainties and input constraints using operator-based robust right coprime factorization is proposed. The effectiveness of the proposed control method based on obtained nonlinear model is confirmed by simulation and experimental results.

Challenges and Perspectives in Control of Ionic Polymer-Metal Composite (IPMC) Actuators: A Survey

“…In [28]- [30] the PI controller was used to track the curvature of an IPMC actuator. In both [28] and [29] the tracking PI controller supplemented the operator-based robust right coprime factorization method, intended to stabilize the nonlinear system with uncertainties and input constraints (as well as hysteresis in [28]). In [30] the robust stable control system of IPMC from [28], [29] was identified as an equivalent transfer function.…”

“…In both [28] and [29] the tracking PI controller supplemented the operator-based robust right coprime factorization method, intended to stabilize the nonlinear system with uncertainties and input constraints (as well as hysteresis in [28]). In [30] the robust stable control system of IPMC from [28], [29] was identified as an equivalent transfer function. Then, using the obtained transfer function, the authors applied a multi-objective particle swarm algorithm to find the optimal gains K p , K i of the PI controller.…”

“…The parameters of PI controller were optimized by the back-propagation neural network. On the basis of simulations the authors compared the proposed method with the PID controller and the right coprime factorization technique (see [28], [29]). As a result, the sliding mode controller showed the best performance, especially in the presence of disturbance.…”