Adaptive Control of Flexible Joint Robots Derived from Arm Energy Considerations

Almost all industrial robots exhibit joint flexibility due to mechanical compliance of their gear boxes. In this paper we outline a design of an adaptive controller for flexible joint robots based on the arms energy. The desired actuator trajectory in a flexible joint robot is dependent not only on the desired kinematic trajectory of the link but also on the link dynamics. Unfortunately, link dynamic parameters are unknown in most cases, as a result the desired actuator trajectory is also unknown. To overcome this difficulty, a number of control schemes have suggested the use of acceleration and link jerk feedback. In this paper we describe a control scheme which does not use link jerk or acceleration. The control law we derive is based on the energy of the arm deviating from the desired trajectory and it has two stages with two corresponding adaptation laws. The first stage drives the actuator and the joints to a desired manifold, the second controller then seeks to drive the joints to their desired trajectory. On application of our first controller there is an apparent structural reduction of the order of the system. This apparent reduction in the structure is exploited by our second stage controller. Our control scheme does not require link acceleration or jerk measurements, and the numerical differentiation of the velocity signal, or the inversion of the inertial matrices are also unnecessary. Simulations are presented to verify the validity of the control scheme. The superiority of the proposed scheme over existing rigid robot adaptive schemes is also illustrated through simulation

Effect of Rainflow Cycle Number on Fatigue Lifetime of an Arm of a Vehicle Suspension System

Fatigue is considered as one of the main cause ofmechanical properties degradation of mechanical parts.Reliabilities methods are appropriate for fatigue analysis usinguncertainties that exist in fatigue material or process parameters.Current work aims the study of the effect of the Rainflow cyclenumber on fatigue lifetime of an upper arm of the vehiclesuspension system. The major part of the fatigue damage inducedin suspension arm is caused by two main classes of parameters.First parameter characterizes materials properties and a secondone describes equivalent force generated by road excitation andpassenger’s number. Therefore, representative sampling ofYoung's modulus and equivalent loading are selected as inputparameters to conduct repetitive finite elements simulations byMonte Carlo (MC) algorithm. Strain-life approach based onManson-coffin and Ramberg-Osgood equations is used in orderto determine fatigue lifetime of each combination of inputparameters. Thereafter, response surface is built according topreselected performance function. A PYTHON script wasdeveloped to automatize finite element simulations of the upperarm according to a design of experiments. Preliminary resultsshow Rainflow primary cycles to have significant effect onobtained cycle’s number to fracture. Load generated byexcitation road have a remarkable quasi-linear inverselyproportional effect on fatigue lifetime

Using Facebook advertising data to describe the socio-economic situation of Syrian refugees in Lebanon

While the fighting in the Syrian civil war has mostly stopped, an estimated 5.6 million Syrians remain living in neighboring countries1. Of these, an estimated 1.5 million are sheltering in Lebanon. Ongoing efforts by organizations such as UNHCR to support the refugee population are often ineffective in reaching those most in need. According to UNHCR's 2019 Vulnerability Assessment of Syrian Refugees Report (VASyR), only 44% of the Syrian refugee families eligible for multipurpose cash assistance were provided with help, as the others were not captured in the data. In this project, we are investigating the use of non-traditional data, derived from Facebook advertising data, for population level vulnerability assessment. In a nutshell, Facebook provides advertisers with an estimate of how many of its users match certain targeting criteria, e.g., how many Facebook users currently living in Beirut are “living abroad,” aged 18–34, speak Arabic, and primarily use an iOS device. We evaluate the use of such audience estimates to describe the spatial variation in the socioeconomic situation of Syrian refugees across Lebanon. Using data from VASyR as ground truth, we find that iOS device usage explains 90% of the out-of-sample variance in poverty across the Lebanese governorates. However, evaluating predictions at a smaller spatial resolution also indicate limits related to sparsity, as Facebook, for privacy reasons, does not provide audience estimates for fewer than 1,000 users. Furthermore, comparing the population distribution by age and gender of Facebook users with that of the Syrian refugees from VASyR suggests an under-representation of Syrian women on the social media platform. This work adds to growing body of literature demonstrating the value of anonymous and aggregate Facebook advertising data for analysing large-scale humanitarian crises and migration events

Using Facebook advertising data to describe the socio-economic situation of Syrian refugees in Lebanon

While the fighting in the Syrian civil war has mostly stopped, an estimated 5.6 million Syrians remain living in neighboring countries1. Of these, an estimated 1.5 million are sheltering in Lebanon. Ongoing efforts by organizations such as UNHCR to support the refugee population are often ineffective in reaching those most in need. According to UNHCR's 2019 Vulnerability Assessment of Syrian Refugees Report (VASyR), only 44% of the Syrian refugee families eligible for multipurpose cash assistance were provided with help, as the others were not captured in the data. In this project, we are investigating the use of non-traditional data, derived from Facebook advertising data, for population level vulnerability assessment. In a nutshell, Facebook provides advertisers with an estimate of how many of its users match certain targeting criteria, e.g., how many Facebook users currently living in Beirut are “living abroad,” aged 18–34, speak Arabic, and primarily use an iOS device. We evaluate the use of such audience estimates to describe the spatial variation in the socioeconomic situation of Syrian refugees across Lebanon. Using data from VASyR as ground truth, we find that iOS device usage explains 90% of the out-of-sample variance in poverty across the Lebanese governorates. However, evaluating predictions at a smaller spatial resolution also indicate limits related to sparsity, as Facebook, for privacy reasons, does not provide audience estimates for fewer than 1,000 users. Furthermore, comparing the population distribution by age and gender of Facebook users with that of the Syrian refugees from VASyR suggests an under-representation of Syrian women on the social media platform. This work adds to growing body of literature demonstrating the value of anonymous and aggregate Facebook advertising data for analysing large-scale humanitarian crises and migration events

Robust adaptive control of flexible joint robots

During the 1980\u27s, joint stiffness of industrial robots was experimentally observed and described by constant torsional springs. It was concluded that neglecting the joint flexibility in control strategies limits the robot\u27s ability to perform high speed and high precision operations. Robots were introduced into industrial environments to increase production and to lower costs. The need for reprogramming with different loads and tasks wasted valuable time, therefore adaptive instead of fixed control laws were desirable. The desired actuator trajectory in a flexible joint robot is dependent not only on the desired kinematic trajectory of the link but also on the link dynamics. Unfortunately, link dynamic parameters are unknown in most cases, as a result the desired actuator trajectory is also unknown. To overcome this difficulty, a number of control schemes require the feedback of link acceleration and link jerk. In this thesis we describe three control schemes for flexible joint robots which do not use link jerk or acceleration. One of the controllers is suitable for trajectory tracking when the robot parameters are known in advance. The other two control laws are derived from candidate Lyapunov functions which resemble the energy of the arm deviating from the desired trajectory. Trajectory tracking and adaptation of robot arm parameters are possible with two of the controllers described in this thesis. Our control schemes do not require the numerical differentiation of the velocity signal, or the inversion of the inertial matrices