Optimal Mixed Tracking/Impedance Control With Application to Transfemoral Prostheses With Energy Regeneration

We design an optimal passivitybased tracking/impedance control system for a robotic manipulator with energy regenerative electronics, where the manipulator has both actively and semi-actively controlled joints. The semi-active joints are driven by a regenerative actuator that includes an energy-storing element. Method: External forces can have a large influence on energy regeneration characteristics. Impedance control is used to impose a desired relationship between external forces and deviation from reference trajectories. Multi-objective optimization (MOO) is used to obtain optimal impedance parameters and control gains to compromise between the two conflicting objectives of trajectory tracking and energy regeneration. We solve the MOO problem under two different scenarios: 1) constant impedance; and 2) timevarying impedance. Results: The methods are applied to a transfemoral prosthesis simulation with a semi-active knee joint. Normalized hypervolume and relative coverage are used to compare Pareto fronts, and these two metrics show that time-varying impedance provides better performance than constant impedance. The solution with time-varying impedance with minimum tracking error (0.0008 rad) fails to regenerate energy (loses 9.53 J), while a solution with degradation in tracking (0.0452 rad) regenerates energy (gains 270.3 J). A tradeoff solution results in fair tracking (0.0178 rad) and fair energy regeneration (131.2 J). Conclusion: Our experimental results support the possibility of net energy regeneration at the semi-active knee joint with human-like tracking performance. Significance: The results indicate that advanced control and optimization of ultracapacitor-based systems can significantly reduce power requirements in transfemoral prostheses

Optimal Mixed Tracking/Impedance Control With Application to Transfemoral Prostheses With Energy Regeneration

We design an optimal passivitybased tracking/impedance control system for a robotic manipulator with energy regenerative electronics, where the manipulator has both actively and semi-actively controlled joints. The semi-active joints are driven by a regenerative actuator that includes an energy-storing element. Method: External forces can have a large influence on energy regeneration characteristics. Impedance control is used to impose a desired relationship between external forces and deviation from reference trajectories. Multi-objective optimization (MOO) is used to obtain optimal impedance parameters and control gains to compromise between the two conflicting objectives of trajectory tracking and energy regeneration. We solve the MOO problem under two different scenarios: 1) constant impedance; and 2) timevarying impedance. Results: The methods are applied to a transfemoral prosthesis simulation with a semi-active knee joint. Normalized hypervolume and relative coverage are used to compare Pareto fronts, and these two metrics show that time-varying impedance provides better performance than constant impedance. The solution with time-varying impedance with minimum tracking error (0.0008 rad) fails to regenerate energy (loses 9.53 J), while a solution with degradation in tracking (0.0452 rad) regenerates energy (gains 270.3 J). A tradeoff solution results in fair tracking (0.0178 rad) and fair energy regeneration (131.2 J). Conclusion: Our experimental results support the possibility of net energy regeneration at the semi-active knee joint with human-like tracking performance. Significance: The results indicate that advanced control and optimization of ultracapacitor-based systems can significantly reduce power requirements in transfemoral prostheses

Energy Regeneration and Environment Sensing for Robotic Leg Prostheses and Exoskeletons

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, limitations in automated control and energy-efficient actuation have impeded their transition from research laboratories to real-world environments. With regards to control, the current automated locomotion mode recognition systems being developed rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, here a multi-generation environment sensing and classification system powered by computer vision and deep learning was developed to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. To support this initiative, the “ExoNet” database was developed – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a novel hierarchical labelling architecture. Over a dozen state-of-the-art deep convolutional neural networks were trained and tested on ExoNet for large-scale image classification and automatic feature engineering. The benchmarked CNN architectures and their environment classification predictions were then quantitatively evaluated and compared using an operational metric called “NetScore”, which balances the classification accuracy with the architectural and computational complexities (i.e., important for onboard real-time inference with mobile computing devices). Of the benchmarked CNN architectures, the EfficientNetB0 network achieved the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore. These comparative results can inform the optimal architecture design or selection depending on the desired performance of an environment classification system. With regards to energetics, backdriveable actuators with energy regeneration can improve the energy efficiency and extend the battery-powered operating durations by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, the evaluation and control of these regenerative actuators has focused on steady-state level-ground walking. To encompass real-world community mobility more broadly, here an energy regeneration system, featuring mathematical and computational models of human and wearable robotic systems, was developed to simulate energy regeneration and storage during other locomotor activities of daily living, specifically stand-to-sit movements. Parameter identification and inverse dynamic simulations of subject-specific optimized biomechanical models were used to calculate the negative joint mechanical work and power while sitting down (i.e., the mechanical energy theoretically available for electrical energy regeneration). These joint mechanical energetics were then used to simulate a robotic exoskeleton being backdriven and regenerating energy. An empirical characterization of an exoskeleton was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration and storage with the exoskeleton parameters. The performance calculations showed that regenerating electrical energy during stand-to-sit movements provide small improvements in energy efficiency and battery-powered operating durations. In summary, this research involved the development and evaluation of environment classification and energy regeneration systems to improve the automated control and energy-efficient actuation of next-generation robotic leg prostheses and exoskeletons for real-world locomotor assistance