Energy-oriented Modeling And Control of Robotic Systems

This research focuses on the energy-oriented control of robotic systems using an ultracapacitor as the energy source. The primary objective is to simultaneously achieve the motion task objective and to increase energy efficiency through energy regeneration. To achieve this objective, three aims have been introduced and studied: brushless DC motors (BLDC) control by achieving optimum current in the motor, such that the motion task is achieved, and the energy consumption is minimized. A proof-ofconcept study to design a BLDC motor driver which has superiority compare to an off-the-shelf driver in terms of energy regeneration, and finally, the third aim is to develop a framework to study energy-oriented control in cooperative robots. The first aim is achieved by introducing an analytical solution which finds the optimal currents based on the desired torque generated by a virtual. Furthermore, it is shown that the well-known choice of a zero direct current component in the direct-quadrature frame is sub-optimal relative to our energy optimization objective. The second aim is achieved by introducing a novel BLDC motor driver, composed of three independent regenerative drives. To run the motor, the control law is obtained by specifying an outer-loop torque controller followed by minimization of power consumption via online constrained quadratic optimization. An experiment is conducted to assess the performance of the proposed concept against an off-the-shelf driver. It is shown that, in terms of energy regeneration and consumption, the developed driver has better performance, and a reduction of 15% energy consumption is achieved. v For the third aim, an impedance-based control scheme is introduced for cooperative manipulators grasping a rigid object. The position and orientation of the payload are to be maintained close to a desired trajectory, trading off tracking accuracy by low energy consumption and maintaining stability. To this end, an optimization problem is formulated using energy balance equations. The optimization finds the damping and stiffness gains of the impedance relation such that the energy consumption is minimized. Furthermore, L2 stability techniques are used to allow for time-varying damping and stiffness in the desired impedance. A numerical example is provided to demonstrate the results

Design, Control, and Optimization of Robots with Advanced Energy Regenerative Drive Systems

We investigate the control and optimization of robots with ultracapacitor based regenerative drive systems. A subset of the robot joints are conventional, in the sense that external power is used for actuation. Other joints are energetically self-contained passive systems that use ultracapacitors for energy storage. An electrical interconnection known as the star configuration is considered for the regenerative drives that allows for direct electric energy redistribution among joints, and enables higher energy utilization efficiencies. A semi-active virtual control strategy is used to achieve control objectives. We find closed-form expressions for the optimal robot and actuator parameters (link lengths, gear ratios, etc.) that maximize energy regeneration between any two times, given motion trajectories. In addition, we solve several trajectory optimization problems for maximizing energy regeneration that admit closed-form solutions, given system parameters. Optimal solutions are shown to be global and unique. In addition, closed-form expressions are provided for the maximum attainable energy. This theoretical maximum places limits on the amount of energy that can be recovered. Numerical examples are provided in each case to demonstrate the results. For problems that don\u27t admit analytical solutions, we formulate the general nonlinear optimal control problem, and solve it numerically, based on the direct collocation method. The optimization problem, its numerical solution and an experimental evaluation are demonstrated using a PUMA manipulator with custom regenerative drives. Power flows, stored regenerative energy and efficiency are evaluated. Experimental results show that when following optimal trajectories, a reduction of about 10-22% in energy consumption can be achieved. Furthermore, we present the design, control, and experimental evaluation of an energy regenerative powered transfemoral prosthesis. Our prosthesis prototype is comprised of a passive ankle, and an active regenerative knee joint. A novel varying impedance control approach controls the prosthesis in both the stance and swing phase of the gait cycle, while explicitly considering energy regeneration. Experimental evaluation is done with an amputee test subject walking at different speeds on a treadmill. The results validate the effectiveness of the control method. In addition, net energy regeneration is achieved while walking with near-natural gait across all speeds

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

A novel hydraulic energy-storage-and-return prosthetic ankle : design, modelling and simulation

In an intact ankle, tendons crossing the joint store energy during the stance phase of walkingprior to push-off and release it during push-off, providing forward propulsion. Most prostheticfeet currently on the market – both conventional and energy storage and return (ESR) feet –fail to replicate this energy-recycling behaviour. Specifically, they cannot plantarflex beyondtheir neutral ankle angle (i.e. a 90° angle between the foot and shank) while generating theplantarflexion moment required for normal push-off. This results in a metabolic cost ofwalking for lower-limb amputees higher than for anatomically intact subjects, combined witha reduced walking speed.Various research prototypes have been developed that mimic the energy storage and returnseen in anatomically intact subjects. Many are unpowered clutch-and-spring devices thatcannot provide biomimetic control of prosthetic ankle torque. Adding a battery and electricmotor(s) may provide both the necessary push-off power and biomimetic ankle torque, butadd to the size, weight and cost of the prosthesis. Miniature hydraulics is commonly used incommercial prostheses, not for energy storage purposes, but rather for damping and terrainadaptation. There are a few examples of research prototypes that use a hydraulic accumulatorto store and return energy, but these turn out to be highly inefficient because they useproportional valves to control joint torque. Nevertheless, hydraulic actuation is ideally suitedfor miniaturisation and energy transfer between joints via pipes.Therefore, the primary aim of this PhD was to design a novel prosthetic ankle based on simpleminiature hydraulics, including an accumulator for energy storage and return, to imitate thebehaviour of an intact ankle. The design comprises a prosthetic ankle joint driving two cams,which in turn drive two miniature hydraulic rams. The “stance cam-ram system” captures theeccentric (negative) work done from foot flat until maximum dorsiflexion, by pumping oil intothe accumulator, while the “push-off system” does concentric (positive) work to power pushoff through fluid flowing from the accumulator to the ram. By using cams with specific profiles,the new hydraulic ankle mimics intact ankle torque. Energy transfer between the knee andthe ankle joints via pipes is also envisioned.A comprehensive mathematical model of the system was defined, including all significantsources of energy loss, and used to create a MATLAB simulation model to simulate theoperation of the new device over the whole gait cycle. A MATLAB design program was alsoimplemented, which uses the simulation model to specify key components of the new designto minimise energy losses while keeping the device size acceptably small.The model’s performance was assessed to provide justification for physical prototyping infuture work. Simulation results show that the new device almost perfectly replicates thetorque of an intact ankle during the working phases of the two cam-ram systems. Specifically,78% of the total eccentric work done by the prosthetic ankle over the gait cycle is returnedas concentric work, 14% is stored and carried forward for future gait cycles, and 8.21% is lost.A design sensitivity study revealed that it may be possible to reduce the energy lost to 5.83%of the total eccentric work. Finally, it has been shown that the main components of the system– cams, rams, and accumulator - could be physically realistic, matching the size and mass ofthe missing anatomy