Matteo Chiodo

Shape Memory Alloy (SMA) spring-based actuators are promising for wearable robotics, offering over 50% contraction, but are limited by slow thermal response, electrical safety risks, and unreliable mechanical connections. These deficiencies hinder their widespread adoption in human-interactive applications where safety and performance are critical. This work introduces a novel modular SMA spring actuator architecture to address these limitations. The actuator integrates borosilicate glass fiber sheaths for electrical and thermal isolation, and precision EDM-fabricated aluminum terminals for reliable mechanical coupling. A comprehensive theoretical framework, based on the validated static two-state model, was used for systematic spring dimensioning. The proposed architecture demonstrated complete electrical isolation between springs, enabling flexible series-parallel configurations. The actuator consistently achieved approximately 50% contraction recovery under loads of 38 N up to 66 N, with maximum displacement ranging from over 50 mm down to 35 mm. Cyclic actuation experiments under external loads up to 45 N indicate a stable displacement recovery of 40-42 mm and a stable temperature activation range of 68-70°C is reached in less than 100 activation cycles with no functional degradation in terms of actuation stroke (ΔL). This research presents a significant advancement in SMA actuator technology by simultaneously improving thermal safety, electrical isolation, and mechanical reliability. Furthermore, the actuator's performance and safety were validated at the application level through a case study involving a wearable device for shoulder abduction assistance, demonstrating effective muscular load compensation (confirmed by EMG analysis) and safe thermal operation. These findings pave the way for the development of safer and more efficient SMA-actuated systems, with potential applications in soft robotics, wearable exoskeletons, and human-robot interaction.

Background: Shape memory alloy spring actuators offer significant potential for advanced actuation systems in exoskeletons, medical devices, and robotics, but adoption has been limited by slow activation speeds and insufficient design guidelines for achieving rapid response times while maintaining structural integrity. Objective: This study aimed to establish comprehensive design parameters for nickel–titanium spring actuators capable of achieving sub-second activation times through systematic experimental characterization and performance optimization. Methods: Nine different nickel–titanium spring configurations with wire diameters ranging from 0.5 to 0.8 mm and spring indices from 6 to 8 were systematically evaluated using differential scanning calorimetry for thermal characterization, mechanical testing for material properties, high-current electrical activation studies spanning 5–11 A, infrared thermal distribution analysis, and laser displacement sensing for dynamic response measurement. Results: Dynamic testing achieved activation times below 1 s for currents exceeding 5 A, with maximum displacement recoveries reaching 600–800% strain recovery, while springs with intermediate spring index values of 6.5–7.5 provided optimal balance between force output and displacement range, and optimal activation involved moderate current levels of 5–7 A for thin wires and 8–11 A for thick wires. Conclusions: Systematic geometric optimization combined with controlled high-current density activation protocols enables rapid actuation response while maintaining structural integrity, providing essential design parameters for engineering applications requiring fast, reliable actuation cycles.

Shape Memory Alloy (SMA) actuators are pivotal in modern engineering due to their unique thermomechanical properties, but their inherent non-linearities, hysteresis, and temperature sensitivity pose significant control challenges. This systematic review evaluates artificial intelligence (AI)-based control methodologies to address these limitations, analyzing their efficacy in enhancing precision, adaptability, and reliability for SMA and Magnetic SMA (MSMA) systems. A PRISMA-guided literature review (2003–2025) identified 24 studies, which were categorized by control architectures (hybrid AI-linear, pure AI, adaptive, and model predictive control) and evaluated through quantitative metrics, including Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor. Results revealed hybrid AI-linear controllers as the dominant approach (36%), with online-trained neural networks achieving superior accuracy (+2.4%) over offline methods. Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (−7.7%). Sensorless strategies proved advantageous for MSMAs (+5.0%), leveraging intrinsic material properties like electrical resistance for state estimation. The analysis underscores AI’s capacity to mitigate hysteresis and non-linear dynamics, though material-specific optimization is critical: SMA systems favor dynamic control and MPC, whereas MSMAs benefit from sensorless AI and pure neural networks. Challenges persist in computational demands for online training and reinforcement learning’s exploration–exploitation trade-offs. Future research should prioritize adaptive algorithms for fatigue compensation, lightweight AI models for embedded deployment, and standardized benchmarking to bridge material-specific performance gaps. This synthesis establishes AI as a transformative paradigm for SMA actuation, enabling precise control in aerospace, biomedical, and soft robotics applications.