Proceedings of the ASME Dynamic Systems and Control Conference. ASME Dynamic Systems and Control Conference最新文献

Despite its proven success in a wide variety of applications, the atomic force microscope (AFM) remains limited by its slow imaging rate. One approach to overcome this challenge is to rely on algorithmic approaches that reduce the imaging time not by scanning faster but by scanning less. Such schemes are particularly useful on older instruments as they can provide significant gains despite the existing (slow) hardware. At the same time, algorithms for sub-sampling can yield even greater improvements in imaging rate when combined with advanced scanners that can be retrofitted into the system. In this work, we focus on the use of a dual-stage piezoelectric scanner coupled with a particular scanning algorithm known as Local Circular Scan (LCS). LCS drives the tip of the AFM along a circular trajectory while using feedback to center that circle on a sample edge and to move the circle along the feature, thus reducing imaging time by concentrating the samples to the region of interest. Dual-stage systems are well-suited to LCS as the algorithm is naturally described in terms of a high-frequency, short range path (the scanning circle) and a slower, long range path (the track along the sample). However, control of the scanner is not straightforward as the system is multi-input, single-output. Here we establish controllability and observability of the scanning stage, allowing us to develop individual controllers for the long-range and short-range actuators through the principle of separation. We then use an internal model controller for the short range actuator to track a sinusoidal input (to generate the circular motion) and a state-space set-point tracking controller for the long range actuator. The results are demonstrated through simulation.

This paper presents the design of a series elastic actuator and a higher level controller for said actuator to assist the motion of a user's hand in a linkage based hand exoskeleton. While recent trends in the development of exoskeleton gloves has been to exploit the advantages of soft actuators, their size and power requirements limit their adoption. On the other hand, a series elastic actuator can provide compliant assistance to the wearer while remaining compact and lightweight. Furthermore, the linkage based mechanism integrated with the SEA offers repeatability and accuracy to the hand exoskeleton. By measuring the user's motion intention through compression of the elastic elements in the actuator, a virtual dynamic system can be utilized that assists the users in performing the desired motion while ensuring the motion stability of the overall system. This work describes the detailed design of the actuator followed by performance tests using a simple PD controller on the integrated robotic exoskeleton prototype. The performance of the proposed high level controller is tested using the integrated exoskeleton glove mechanism for a single finger, using two types of input motion. Preliminary results are discussed as well as plans for integrating the proposed actuator and high level controller into a complete hand exoskeleton prototype to perform intelligent grasping.

Having unified representations of human walking gait data is of paramount importance for wearable robot control. In the rehabilitation robotics literature, control approaches that unify the gait cycle of wearable robots are more appealing than the conventional approaches that rely on dividing the gait cycle into several periods, each with their own distinct controllers. In this article we propose employing algebraic curves to represent human walking data for wearable robot controller design. In order to generate algebraic curves from human walking data, we employ the 3L fitting algorithm, a tool developed in the pattern recognition literature for fitting implicit polynomial curves to given datasets. For an impedance model of the knee joint motion driven by the hip angle signal, we provide conditions by which the generated algebraic curves satisfy a robust relative degree condition throughout the entire walking gait cycle. The robust relative degree property makes the algebraic curve representation of walking gaits amenable to various nonlinear output tracking controller design techniques.

Physical interaction with tools is ubiquitous in functional activities of daily living. While tool use is considered a hallmark of human behavior, how humans control such physical interactions is still poorly understood. When humans perform a motor task, it is commonly suggested that the central nervous system coordinates the musculo-skeletal system to minimize muscle effort. In this paper, we tested if this notion holds true for motor tasks that involve physical interaction. Specifically, we investigated whether humans minimize muscle forces to control physical interaction with a circular kinematic constraint. Using a simplified arm model, we derived three predictions for how humans should behave if they were minimizing muscular effort to perform the task. First, we predicted that subjects would exert workless, radial forces on the constraint. Second, we predicted that the muscles would be deactivated when they could not contribute to work. Third, we predicted that when moving very slowly along the constraint, the pattern of muscle activity would not differ between clockwise (CW) and counterclockwise (CCW) motions. To test these predictions, we instructed human subjects to move a robot handle around a virtual, circular constraint at a constant tangential velocity. To reduce the effect of forces that might arise from incomplete compensation of neuro-musculoskeletal dynamics, the target tangential speed was set to an extremely slow pace (~1 revolution every 13.3 seconds). Ultimately, the results of human experiment did not support the predictions derived from our model of minimizing muscular effort. While subjects did exert workless forces, they did not deactivate muscles as predicted. Furthermore, muscle activation patterns differed between CW and CCW motions about the constraint. These findings demonstrate that minimizing muscle effort is not a significant factor in human performance of this constrained-motion task. Instead, the central nervous system likely prioritizes reducing other costs, such as computational effort, over muscle effort to control physical interactions.

The use of actuators with inherent compliance, such as series elastic actuators (SEAs), has become traditional for robotic systems working in close contact with humans. SEAs can reduce the energy consumption for a given task compared to rigid actuators, but this reduction is highly dependent on the design of the SEA's elastic element. This design is often based on natural dynamics or a parameterized optimization, but both approaches have limitations. The natural dynamics approach cannot consider actuator constraints or arbitrary reference trajectories, and a parameterized elastic element can only be optimized within the given parameter space. In this work, we propose a solution to these limitations by formulating the design of the SEA's elastic element as a non-parametric convex optimization problem, which yields a globally optimal conservative elastic element while respecting actuator constraints. Convexity is proven for the case of an arbitrary periodic reference trajectory with a SEA capable of energy regeneration. We discuss the optimization results for the tasks defined by the human ankle motion during level-ground walking and the natural motion of a single mass-spring system with a nonlinear spring. For all these tasks, the designed SEA reduces energy consumption and satisfies the actuator's constraints.

Although dynamic walking methods have had notable successes in control of bipedal robots in the recent years, still most of the humanoid robots rely on quasi-static Zero Moment Point controllers. This work is an attempt to design a highly stable controller for dynamic walking of a human-like model which can be used both for control of humanoid robots and prosthetic legs. The method is based on using time-based trajectories that can induce a highly stable limit cycle to the bipedal robot. The time-based nature of the controller motivates its use to entrain a model of an amputee walking, which can potentially lead to a better coordination of the interaction between the prosthesis and the human. The simulations demonstrate the stability of the controller and its robustness against external perturbations.

This paper presents the performance characterization of the MAHI Exo-II, an upper extremity exoskeleton for stroke and spinal cord injury (SCI) rehabilitation, as a means to validate its clinical implementation and to provide depth to the literature on the performance characteristics of upper extremity exoskeletons. Individuals with disabilities arising from stroke and SCI need rehabilitation of the elbow, forearm, and wrist to restore the ability to independently perform activities of daily living (ADL). Robotic rehabilitation has been proposed to address the need for high intensity, long duration therapy and has shown promising results for upper limb proximal joints. However, upper limb distal joints have historically not benefitted from the same focus. The MAHI Exo-II, designed to address this shortcoming, has undergone a static and dynamic performance characterization, which shows that it exhibits the requisite qualities for a rehabilitation robot and is comparable to other state-of-the-art designs.

Amputee locomotion can benefit from recent advances in robotic prostheses, but their control systems design poses challenges. Prosthesis control typically discretizes the nonlinear gait cycle into phases, with each phase controlled by different linear controllers. Unfortunately, real-time identification of gait phases and tuning of controller parameters limit implementation. Recently, biped robots have used phase variables and virtual constraints to characterize the gait cycle as a whole. Although phase variables and virtual constraints could solve issues with discretizing the gait cycle, the virtual constraints method from robotics does not readily translate to prosthetics because of hard-to-measure quantities, like the interaction forces between the user and prosthesis socket, and prosthesis parameters which are often altered by a clinician even for a known patient. We use the simultaneous stabilization approach to design a low-order, linear time-invariant controller for ankle prostheses independent of such quantities to enforce a virtual constraint. We show in simulation that this controller produces suitable walking gaits for a simplified amputee model.