Modeling of Conductive Shape Memory Polymer Nanocomposites Based Structure From a Control Perspective

Shape memory polymers (SMPs) can recover their original shape under external stimulus such as light, heat, pH, humidity, and electric power. However, the applications of SMPs are limited by the number of shapes they can memorize and whether or not these shapes can be precisely and repeatedly controlled. Although a vision based PID controller has been shown by the authors to be capable of controlling the deflection angle of a SMP structure, the repeatability and precision are still low. In order to enhance the robustness and repeatability of the SMP shape control system, in this paper, the macro-scale behavior model of the SMP structure from the control perspective is proposed and the unknown parameters are identified using real-time vision, temperature, and resistance signals.