Stable and robust neural network controllers

Neural networks are expressive function approimators that can be employed for state estimation in control problems. However, control systems with machine learning in the loop often lack stability proofs and performance guarantees, which are crucial for safety-critical applications. In this work, a feedback controller using a feedforward neural network of arbitrary size to estimate unknown dynamics is suggested. The controller is designed for solving a general trajectory tracking problem for a broad class of two-dimensional nonlinear systems. The controller is proven to stabilize the closed-loop system, such that it is input-to-state and finite-gain Lp-stable from the neural network estimation error to the tracking error. Furthermore, the controller is proven to make the tracking error globally and exponentially converge to a ball centered at the origin. When the neural network estimate is updated discretely, or the state measurements are affected by bounded noise, the convergence bound is shown to be dependent on the Lipschitz constant of the neural network estimator. In light of this, we demonstrate how regularization techniques can be beneficial when utilizing deep learning in control. Experiments on simulated data confirm the theoretical results.acceptedVersio

Experimental Results for Set-based Control within theSingularity-robust Multiple Task-priority Inverse KinematicsFramework

Inverse kinematics algorithms are commonly used in robotic systems to achieve desired behavior, and several methods exist to ensure the achievement of numerous tasks simultaneously. The multiple task-priority inverse kinematics framework allows a consideration of tasks in a prioritized order by projecting task velocities through the null-spaces of higher priority tasks. Recent results have extended this framework from equality tasks to also handling set-based tasks, i.e. tasks that have an interval of valid values. The purpose of this paper is to further investigate and experimentally validate this algorithm and its properties. In particular, this paper presents experimental results where a number of both set-based and equality tasks have been implemented on the 6 Degree of Freedom UR5 which is an industrial robotic arm from Universal Robots. The experiments validate the theoretical results.(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization

Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in their flight envelope as compared to experienced human pilots, thereby restricting the conditions UAVs can operate in and the types of missions they can accomplish autonomously. This paper proposes a deep reinforcement learning (DRL) controller to handle the nonlinear attitude control problem, enabling extended flight envelopes for fixed-wing UAVs. A proof-of-concept controller using the proximal policy optimization (PPO) algorithm is developed, and is shown to be capable of stabilizing a fixed-wing UAV from a large set of initial conditions to reference roll, pitch and airspeed values. The training process is outlined and key factors for its progression rate are considered, with the most important factor found to be limiting the number of variables in the observation vector, and including values for several previous time steps for these variables. The trained reinforcement learning (RL) controller is compared to a proportional-integral-derivative (PID) controller, and is found to converge in more cases than the PID controller, with comparable performance. Furthermore, the RL controller is shown to generalize well to unseen disturbances in the form of wind and turbulence, even in severe disturbance conditions.Comment: 11 pages, 3 figures, 2019 International Conference on Unmanned Aircraft Systems (ICUAS

Stability Analysis for Set-based Control within the Singularity-robust Multiple Task-priority Inverse Kinematics Framework

Inverse kinematics algorithms are commonly used in robotic systems to accomplish desired behavior, and several methods exist to ensure the achievement of several tasks simultaneously. The multiple task-priority inverse kinematics framework allows tasks to be considered in a prioritized order by projecting task velocities through the nullspaces of higher priority tasks. This paper extends this framework to handle set-based tasks, i.e. tasks with a range of valid values, in addition to equality tasks, which have a specific desired value. Examples of such tasks are joint limit and obstacle avoidance. The proposed method is proven to ensure asymptotic convergence of the equality task errors and the satisfaction of all high-priority set-based tasks. Simulations results confirm the effectiveness of the proposed approach.(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Using self-made automata to teach STEM in early childhood teacher education

In recent decades, an increasing number of countries have integrated science, technology, engineering, and mathematics (STEM) into their curricula for early childhood education and care (ECEC). In contrast to this trend, many ECEC professionals are still reluctant about the idea of teaching STEM to young children. A reason for this might be too little experience with and knowledge about STEM. One way to tackle this problem is to address STEM in ECEC teacher education in a way that is engaging, motivating, and practical, and shows ECEC student teachers appropriate ideas for how to teach STEM in a playful and child-centred way. This case study aims to present and analyse an innovative approach to ECEC teacher training. We let the student teachers build their own automata (toys that have mechanical moving parts) to promote a better understanding of STEM. The students were highly motivated, assessed the approach as exciting and relevant, and consequently could successfully reflect on STEM content and pedagogy.publishedVersio

Neural Network-based Model Predictive Control with Input-to-State Stability

Learning-based controllers, and especially learning-based model predictive controllers, have been used for a number of different applications with great success. In spite of good performance, a lot of these cases lack stability guarantees. In this paper we consider a scenario where the dynamics of a nonlinear system are unknown, but where input and output data are available. A prediction model is learned from data using a neural network, which in turn is used in a nonlinear model predictive control scheme. The closed-loop system is shown to be input-to-state stable with respect to the prediction error of the learned model. The approach is tested and verified in simulations, by employing the controller to a benchmark system, namely a continuous stirred tank reactor plant. Simulations show that the proposed controller successfully drives the system from random initial conditions, to a reference equilibrium point, even in the presence of noise. The results also verify the theoretical stability result.acceptedVersio

Wire-arc additive manufacturing of structures with overhang: Experimental results depositing material onto fixed substrate

As additive manufacturing (AM) technology grows both more advanced and more available, the challenges and limitations are also made more evident. Most existing solutions for AM build structures layer by layer using strictly vertical material deposition. As each layer must vertically adhere to the previous layer, support structures must be added if there are to be any kinds of overhangs. For methods requiring the build to be performed within a chamber, the size of the structure is also very limited. The research presented in this paper explores possible solutions to these challenges, focusing on wire-arc additive manufacturing in order to effectively build structures that can not easily be constructed using in-box, layer-based methods for AM. By non-vertical material deposition using an industrial robot manipulator, metal structures with overhangs are built onto a fixed, horizontal surface without any support structures. Cross sections of two different structures are examined by optical microscopy and hardness measurements to reveal potential differences between the areas with and without intersections or overhang.publishedVersio

Impact of multi-professional, scenario-based training on postpartum hemorrhage in Tanzania: A quasi-experimental, pre- vs. post-intervention study

Background Tanzania has a relatively high maternal mortality ratio of 410 per 100,000 live births. Severe postpartum hemorrhage (PPH) is a major cause of maternal deaths, but in most cases, it is preventable. However, most pregnant women that develop PPH, have no known risk factors. Therefore, preventive measures must be offered to all pregnant women. This study investigated the effects of multi-professional, scenario-based training on the prevention and management of PPH at a Tanzanian zonal consultant hospital. We hypothesized that scenario-based training could contribute to improved competence on PPH-management, which would result in improved team efficiency and patient outcome. Methods This quasi-experimental, pre-vs. post-interventional study involved on-site multi-professional, scenario-based PPH training, conducted in a two-week period in October 2013 and another 2 weeks in November 2014. Training teams included nurses, midwives, doctors, and medical attendants in the Department of Obstetrics and Gynecology. After technical skill training on the birthing simulator MamaNatalie®, the teams practiced in realistic scenarios on PPH. Each scenario was followed by debriefing and repeated scenario. Afterwards, the group swapped roles and the observers became the participants. To evaluate the effects of training, we measured patient outcomes by determining blood transfusion rates. Patient data were collected by randomly sampling Medical birth registry files from the pre-training and post-training study periods (n = 1667 and 1641 files, respectively). Data were analyzed with the Chi-square test, Mann-Whitney U-test, and binary logistic regression. Results The random patient samples (n = 3308) showed that, compared to pre-training, post-training patients had a 47% drop in whole blood transfusion rates and significant increases in cesarean section rates, birth weights, and vacuum deliveries. The logistic regression analysis showed that transfusion rates were significantly associated with the time period (pre- vs. post-training), cesarean section, patients tranferred from other hospitals, maternal age, and female genital mutilation and cutting. Conclusions We found that multi-professional, scenario-based training was associated with a significant, 47% reduction in whole blood transfusion rates. These results suggested that training that included all levels of maternity staff, repeated sessions with realistic scenarios, and debriefing may have contributed to reduced blood transfusion rates in this high-risk maternity setting.publishedVersio