Switching of both local ferroelectric and magnetic domains in multiferroic Bi0.9La0.1FeO3 thin film by mechanical force

Cross-coupling of ordering parameters in multiferroic materials by multiple external stimuli other than electric field and magnetic field is highly desirable from both practical application and fundamental study points of view. Recently, mechanical force has attracted great attention in switching of ferroic ordering parameters via electro-elastic coupling in ferroelectric materials. In this work, mechanical force induced polarization and magnetization switching were investigated in a polycrystalline multiferroic Bi0.9La0.1FeO3 thin film using a scanning probe microscopy system. The piezoresponse force microscopy and magnetic force microscopy responses suggest that both the ferroelectric domains and the magnetic domains in Bi0.9La0.1FeO3 film could be switched by mechanical force as well as electric field. High strain gradient created by mechanical force is demonstrated as able to induce ferroelastic switching and thus induce both ferroelectric dipole and magnetic spin flipping in our thin film, as a consequence of electro-elastic coupling and magneto-electric coupling. The demonstration of mechanical force control of both the ferroelectric and the magnetic domains at room temperature provides a new freedom for manipulation of multiferroics and could result in devices with novel functionalities

Motion Sensors-Based Human Behavior Recognition And Analysis

Human behavior recognition and analysis have been considered as a core technology that can facilitate a variety of applications. However, accurate detection and recognition of human behavior is still a big challenge that attracts a lot of research efforts. Among all the research works, motion sensors-based human behavior recognition is promising as it is low cost, low power, and easy to carry. In this dissertation, we use motion sensors to study human behaviors. First, we present Ultigesture (UG) wristband, a hardware platform for detecting and analyzing human behavior. The hardware platform integrates an accelerometer, gyroscope, and compass sensor, providing a combination of (1) fully open Application Programming Interface (API) for various application development, (2) appropriate form factor for comfortable daily wear, and (3) affordable cost for large scale adoption. Second, we study the hand gesture recognition problem when a user performs gestures continuously. we propose a novel continuous gesture recognition algorithm. It accurately and automatically separates hand movements into segments, and merges adjacent segments if needed, so that each gesture only exists in one segment. Then, we apply the Hidden Markov Model to classify each segment into one of predefined hand gestures. Experiments with human subjects show that the recognition accuracy is 99.4% when users perform gestures discretely, and 94.6% when users perform gestures continuously. Third, we study the hand gesture recognition problem when a user is moving. We propose a novel mobility-aware hand gesture segmentation algorithm to detect and segment hand gestures. We also propose a Convolutional Neural Network to classify hand gestures with mobility noises. For the leave-one-subject-out cross-validation test, experiments with human subjects show that the proposed segmentation algorithm achieves 94.0% precision, and 91.2% recall when the user is moving. The proposed hand gesture classification algorithm is 16.1%, 15.3%, and 14.4% more accurate than state-of-the-art work when the user is standing, walking, and jogging, respectively. Finally, we present a tennis ball speed estimation system, TennisEye, which uses a racket-mounted motion sensor to estimate ball speed. We divide the tennis shots into three categories: serve, groundstroke, and volley. For a serve, we propose a regression model to estimate the ball speed. In addition, we propose a physical model and a regression model for both groundstroke and volley shots. Under the leave-one-subject-out cross-validation test, evaluation results show that TennisEye is 10.8% more accurate than the state-of-the-art work

Optimal tracking control for uncertain nonlinear systems with prescribed performance via critic-only ADP

This paper addresses the tracking control problem for a class of nonlinear systems described by Euler-Lagrange equations with uncertain system parameters. The proposed control scheme is capable of guaranteeing prescribed performance from two aspects: 1) A special parameter estimator with prescribed performance properties is embedded in the control scheme. The estimator not only ensures the exponential convergence of the estimation errors under relaxed excitation conditions but also can restrict all estimates to pre-determined bounds during the whole estimation process; 2) The proposed controller can strictly guarantee the user-defined performance specifications on tracking errors, including convergence rate, maximum overshoot, and residual set. More importantly, it has the optimizing ability for the trade-off between performance and control cost. A state transformation method is employed to transform the constrained optimal tracking control problem to an unconstrained stationary optimal problem. Then a critic-only adaptive dynamic programming algorithm is designed to approximate the solution of the Hamilton-Jacobi-Bellman equation and the corresponding optimal control policy. Uniformly ultimately bounded stability is guaranteed via Lyapunov-based stability analysis. Finally, numerical simulation results demonstrate the effectiveness of the proposed control scheme

Reinforcement learning-based approximate optimal control for attitude reorientation under state constraints

This paper addresses the attitude reorientation problems of rigid bodies under multiple state constraints. A novel reinforcement learning (RL)-based approximate optimal control method is proposed to make the trade-off between control cost and performance. The novelty lies in that it guarantees constraint handling abilities on attitude forbidden zones and angular-velocity limits. To achieve this, barrier functions are employed to encode the constraint information into the cost function. Then an RL-based learning strategy is developed to approximate the optimal cost function and control policy. A simplified critic-only neural network (NN) is employed to replace the conventional actor-critic structure once adequate data is collected online. This design guarantees the uniform boundedness of reorientation errors and NN weight estimation errors subject to the satisfaction of a finite excitation condition, which is a relaxation compared with the persistent excitation condition that is typically required for this class of problems. More importantly, all underlying state constraints are strictly obeyed during the online learning process. The effectiveness and advantages of the proposed controller are verified by both numerical simulations and experimental tests based on a comprehensive hardware-in-loop testbed

Wind Turbine Fault-Tolerant Control via Incremental Model-Based Reinforcement Learning

A reinforcement learning (RL) based fault-tolerant control strategy is developed in this paper for wind turbine torque & pitch control under actuator & sensor faults subject to unknown system models. An incremental model-based heuristic dynamic programming (IHDP) approach, along with a critic-actor structure, is designed to enable fault-tolerance capability and achieve optimal control. Particularly, an incremental model is embedded in the critic-actor structure to quickly learn the potential system changes, such as faults, in real-time. Different from the current IHDP methods that need the intensive evaluation of the state and input matrices, only the input matrix of the incremental model is dynamically evaluated and updated by an online recursive least square estimation procedure in our proposed method. Such a design significantly enhances the online model evaluation efficiency and control performance, especially under faulty conditions. In addition, a value function and a target critic network are incorporated into the main critic-actor structure to improve our method’s learning effectiveness. Case studies for wind turbines under various working conditions are conducted based on the fatigue, aerodynamics, structures, and turbulence (FAST) simulator to demonstrate the proposed method’s solid fault-tolerance capability and adaptability. Note to Practitioners —This work achieves high-performance wind turbine control under unknown actuator & sensor faults. Such a task is still an open problem due to the complexity of turbine dynamics and potential uncertainties in practical situations. A novel data-driven and model-free control strategy based on reinforcement learning is proposed to handle these issues. The designed method can quickly capture the potential changes in the system and adjust its control policy in real-time, rendering strong adaptability and fault-tolerant abilities. It provides data-driven innovations for complex operational tasks of wind turbines and demonstrates the feasibility of applying reinforcement learning to handle fault-tolerant control problems. The proposed method has a generic structure and has the potential to be implemented in other renewable energy systems

Understanding the Impact of Adversarial Robustness on Accuracy Disparity

While it has long been empirically observed that adversarial robustness may be at odds with standard accuracy and may have further disparate impacts on different classes, it remains an open question to what extent such observations hold and how the class imbalance plays a role within. In this paper, we attempt to understand this question of accuracy disparity by taking a closer look at linear classifiers under a Gaussian mixture model. We decompose the impact of adversarial robustness into two parts: an inherent effect that will degrade the standard accuracy on all classes due to the robustness constraint, and the other caused by the class imbalance ratio, which will increase the accuracy disparity compared to standard training. Furthermore, we also show that such effects extend beyond the Gaussian mixture model, by generalizing our data model to the general family of stable distributions. More specifically, we demonstrate that while the constraint of adversarial robustness consistently degrades the standard accuracy in the balanced class setting, the class imbalance ratio plays a fundamentally different role in accuracy disparity compared to the Gaussian case, due to the heavy tail of the stable distribution. We additionally perform experiments on both synthetic and real-world datasets to corroborate our theoretical findings. Our empirical results also suggest that the implications may extend to nonlinear models over real-world datasets. Our code is publicly available on GitHub at https://github.com/Accuracy-Disparity/AT-on-AD.Comment: Accepted at ICML 202

Reinforcement learning-based wind farm control : towards large farm applications via automatic grouping and transfer learning

The high system complexity and strong wake effects bring significant challenges to wind farm operations. Conventional wind farm control methods may lead to degraded power generation efficiency. A reinforcement learning (RL)-based approach is proposed in this paper to handle these issues, which can increase the long-term farm-level power generation subject to strong wake effects while without requiring analytical wind farm models. The proposed method is significantly distinct from existing RL-based wind farm control approaches, whose computational complexities usually increase heavily with the increase of total turbine numbers. In contrast, our method can greatly reduce training loads and enhance learning efficiency via two novel designs: (1) automatic grouping and (2) multi-agent-based transfer learning (MATL). Automatic Grouping can divide a large wind farm into small turbine groups by analyzing the aerodynamic interactions between turbines and utilizing some key principles from the graph theory. It enables the separated conduction of RL algorithms on small turbine groups, avoiding the complex training process and high computational costs of applying RL on the entire farm. Based on Automatic Grouping, MATL can further reduce the computational complexity by allowing agents (i.e. wind turbines) to inherit control policies under potential group changes. Case studies with a dynamical simulator show that the proposed method achieves clear power generation increases than the benchmark. It also dramatically reduces computational costs compared with typical RL-based wind farm control methods, paving the way for the application of RL in general wind farms

Wind-farm power tracking via preview-based robust reinforcement learning

This paper aims to address the wind-farm power tracking problem, which requires the farm's total power generation to track time-varying power references and therefore allows the wind farm to participate in ancillary services such as frequency regulation. A novel preview-based robust deep reinforcement learning (PR-DRL) method is proposed to handle such tasks which are subject to uncertain environmental conditions and strong aerodynamic interactions among wind turbines. To our knowledge, this is for the first time that a data-driven model-free solution is developed for wind-farm power tracking. Particularly, reference signals are treated as preview information and embedded in the system as specially designed augmented states. The control problem is then transformed into a zero-sum game to quantify the influence of unknown wind conditions and future reference signals. Built upon the $H_\infty$ control theory, the proposed PR-DRL method can successfully approximate the resulting zero-sum game's solution and achieve wind-farm power tracking. Time-series measurements and long short-term memory (LSTM) networks are employed in our DRL structure to handle the non-Markovian property induced by the time-delayed feature of aerodynamic interactions. Tests based on a dynamic wind farm simulator demonstrate the effectiveness of the proposed PR-DRL wind farm control strategy