Implementation and Investigation of a Compact, Powerful System for Diagnosis and Control of Shape Memory Alloys in Technical Applications

In this paper we present the development, implementation and testing of a compact system for diagnosis and control of actuators based on metallic shape memory alloys (SMA). Using NiTi-SMA, very compact, cost-effective and lightweight actuation systems can be realized. In applications where the SMA is activated by internal Joule heating or its condition is diagnosed by the self-sensing of its electrical resistance, an electrical system capable of reliably measuring very small resistance changes (< 1 ohm) without affecting the phase-state of the SMA is required. In addition, the system must offer the possibility to evaluate the nonlinear, hysteresis-afflicted behavior of the SMA and to handle this difficulty, e.g. utilizing a model-based control. This paper presents a simple compact and adaptive system based on a microcontroller that meets these requirements. Detailed functional tests were carried out with the system to establish a correlation between the change in electrical resistance in the range < 200 mOhm and the current strain state of the actuator. For this purpose, a first series of tests was performed, with the SMA wires working against a constant load. In a second tests series, the SMA wires worked against springs of different stiffness. The use of a microcontroller enables simple implementation of different control strategies. The control system for the non-linear resistance change utilizes a fuzzy logic which divides the control algorithm into three regimes. In the regime of the martensitic phase transformation a PI-controller is used. The state of actuators with an absolute electrical resistance < 1 Ohm and a resistance change < 200 mohm associated with the phase transformation can be precisely measured and controlled with an accuracy < 10 mohm. The system can be configured with little effort for different tasks and shape memory systems of different sizes. Furthermore, it is possible to implement more complex control algorithms up to model-based controllers.