Torque Saturation in Bipedal Robotic Walking through Control Lyapunov Function Based Quadratic Programs

This paper presents a novel method for directly incorporating user-defined control input saturations into the calculation of a control Lyapunov function (CLF)-based walking controller for a biped robot. Previous work by the authors has demonstrated the effectiveness of CLF controllers for stabilizing periodic gaits for biped walkers, and the current work expands on those results by providing a more effective means for handling control saturations. The new approach, based on a convex optimization routine running at a 1 kHz control update rate, is useful not only for handling torque saturations but also for incorporating a whole family of user-defined constraints into the online computation of a CLF controller. The paper concludes with an experimental implementation of the main results on the bipedal robot MABEL

Psychological distress after stroke and aphasia: the first six months

Objective: We explored the factors that predicted psychological distress in the first six months post stroke in a sample including people with aphasia. Design: Prospective longitudinal observational study. Setting and subjects: Participants with a first stroke from two acute stroke units were assessed while still in hospital (baseline) and at three and six months post stroke. Main measures: Distress was assessed with the General Health Questionnaire-12. Other measures included: NIH Stroke Scale, Barthel Index, Frenchay Aphasia Screening Test, Frenchay Activities Index, MOS Social Support Scale and social network indicators. Logistic regression was used to identify predictors of distress at each stage post stroke; and to determine what baseline factors predicted distress at six months. Results: Eighty-seven participants were able to self-report on measures used, of whom 32 (37%) had aphasia. 71 (82%) were seen at six months, including 11 (16%) with aphasia. Predictors of distress were: stroke severity at baseline; low social support at three months; and loneliness and low satisfaction with social network at six months. The baseline factors that predicted distress at six months were psychological distress, loneliness and low satisfaction with social network (Nagelkerke R2 = 0.49). Aphasia was not a predictor of distress at any time point. Yet, at three months post stroke 93% of those with aphasia experienced high distress, as opposed to 50% of those without aphasia (χ2 (1) = 8.61, P<0.01). Conclusions: Factors contributing to distress after stroke vary across time. Loneliness and low satisfaction with one’s social network are particularly important and contribute to long-term psychological distress

Anti-cancer Action of Metal Complexes: Electron Transfer and Oxidative Stress?

Evidence is presented in support of an electron transfer mechanism for various metal complexes possessing anti-neoplastic properties. Cyclic voltammetry was performed on several metallocenes, bis(acetato)bis(imidazole)Cu(II), and coordination compounds (Cu or Fe) of the anti-tumor agents, bipyridine, phenanthroline, hydroxyurea, diethyldithiocarbamate, and α, α1-bis(8-hydroxyquinolin-7-yl)-4-methoxytoluene. The favorable reduction potentials ranged from +0.5 to -0.5 V. Electrochemical behavior is correlated in some cases with structure and physiological activity. Relevant literature data are discussed

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller

Adaptive Safety with Control Barrier Functions

Adaptive Control Lyapunov Functions (aCLFs) were introduced 20 years ago, and provided a Lyapunov-based methodology for stabilizing systems with parameter uncertainty. The goal of this paper is to revisit this classic formulation in the context of safety-critical control. This will motivate a variant of aCLFs in the context of safety: adaptive Control Barrier Functions (aCBFs). Our proposed approach adaptively achieves safety by keeping the system’s state within a safe set even in the presence of parametric model uncertainty. We unify aCLFs and aCBFs into a single control methodology for systems with uncertain parameters in the context of a Quadratic Program (QP) based framework. We validate the ability of this unified framework to achieve stability and safety in an Adaptive Cruise Control (ACC) simulation

Role of material properties and mesostructure on dynamic deformation and shear instability in Al-W granular composites

Dynamic experiments with Al-W granular/porous composites revealed qualitatively different behavior with respect to shear localization depending on bonding between Al particles. Two-dimensional numerical modeling was used to explore the mesomechanics of the large strain dynamic deformation in Al-W granular/porous composites and explain the experimentally observed differences in shear localization between composites with various mesostructures. Specifically, the bonding between the Al particles, the porosity, the roles of the relative particle sizes of Al and W, the arrangements of the W particles, and the material properties of Al were investigated using numerical calculations. It was demonstrated in simulations that the bonding between the "soft" Al particles facilitated shear localization as seen in the experiments. Numerical calculations and experiments revealed that the mechanism of the shear localization in granular composites is mainly due to the local high strain flow of "soft" Al around the "rigid" W particles causing localized damage accumulation and subsequent growth of the meso/macro shear bands/cracks. The "rigid" W particles were the major geometrical factor determining the initiation and propagation of "kinked" shear bands in the matrix of "soft" Al particles, leaving some areas free of extensive plastic deformation as observed in experiments and numerical calculations.Comment: 10 pages, 14 figures, submitted to Journal of Applied Physic

A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety

In this letter we seek to quantify the ability of learning to improve safety guarantees endowed by Control Barrier Functions (CBFs). In particular, we investigate how model uncertainty in the time derivative of a CBF can be reduced via learning, and how this leads to stronger statements on the safe behavior of a system. To this end, we build upon the idea of Input-to-State Safety (ISSf) to define Projection-to-State Safety (PSSf), which characterizes degradation in safety in terms of a projected disturbance. This enables the direct quantification of both how learning can improve safety guarantees, and how bounds on learning error translate to bounds on degradation in safety. We demonstrate that a practical episodic learning approach can use PSSf to reduce uncertainty and improve safety guarantees in simulation and experimentally