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This paper introduces a Lyapunov-based control strategy alongside two filtering methods for controlling and estimating the evolution of coherence vector elements from sequential homodyne measurements. The methods include traditional quantum filtering and a novel extended Kalman filter, which explicitly addresses the dynamics of a stochastic master equation with correlated noise, thereby ensuring the quantum properties of the estimated state variable by design. We also explore scenarios where the system’s Hamiltonian is unknown, demonstrating that both filters exhibit reduced performance with increased estimation errors. To address this, we propose a multiple model estimation scheme applicable to either filter. The estimated density operator is then controlled using the proposed switching-based Lyapunov scheme, which guarantees noise-to-state practical stability in probability. We demonstrate the effectiveness of our approach in stabilizing a qubit coupled to a leaky cavity under homodyne detection, even with uncertainty in the resonance frequency.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system’s safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system’s safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Using Lyapunov theory, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

The Reinforcement Learning (RL) paradigm is showing promising results as a generic purpose framework for solving decision-making problems (e.g., robotics, games, finance). The aim of this work is to reduce the learning barriers and inspire young students, researchers and educators to use RL as an obvious tool to solve robotics problems. This paper provides an intelligible step-by-step RL problem formulation and the availability of an easy-to-use interactive simulator for students at various levels (e.g., undergraduate, bachelor, master, doctorate), researchers and educators. The interactive tool facilitates the familiarization with the key concepts of RL, its problem formulation and implementation. In this work, RL is used for solving a robotics 2D navigational problem where the robot needs to avoid collisions with obstacles while aiming to reach a goal point. A navigational problem is simple and convenient for educational purposes, since the outcome is unambiguous (e.g., the goal is reached or not, a collision happened or not). Due to a lack of open-source graphical interactive simulators concerning the field of RL, this paper combines theoretical exposition with an accessible practical tool to facilitate the apprehension. The results demonstrated are produced by a Python script that is released as open-source to reduce the learning barriers in such innovative research topic in robotics.

One of the primary challenges in solving the State Estimation (SE) problem in low voltage networks is the presence of Gross Errors (GE). If the SE model fails to accurately estimate state variables in the presence of GE, the system operator may receive a distorted image of the power network, potentially leading to unforeseen disruptions, blackouts, and significant economic losses. The classical weighted least squares method, which is commonly used in such problems, exhibits low accuracy when simultaneous GE occurs. This paper provides a comprehensive analysis comparing robust M-estimators designed to handle GE, such as Hachtel and the largest normalized residual test, and presents a novel SE method called the Adaptive Maximum Correntropy Criterion (AMCC). The AMCC employs the maximum correntropy criterion for the loss function and an interior point methodology. Additionally, an adaptive scheme is employed to automatically adjust a high parameter associated with the shape of the related gamma function. We show that, in a real low voltage network, the AMCC exhibits superior accuracy with smaller root-mean-square errors compared to the other estimators studied.

Recently, with the establishment of new thermal regulation, the energy efficiency of buildings has increased significantly, and various deep learning-based methods have been presented to accurately forecast the heating load demand of buildings. However, all of these methods are executed on a dataset with specific distribution and do not have the property of global forecasting, and have no guarantee of data privacy against cyber-attacks. This paper presents a novel approach to heating load demand forecasting based on Cyber-Secure Federated Deep Learning (CSFDL). The suggested CSFDL provides a global super-model for forecasting heating load demand of different local clients without knowing their location and, most importantly, without revealing their privacy. In this study, a CSFDL global server is trained and tested considering the heating load demand of 10 different clients in their building environment. The presented results, including a comparative study, prove the viability and accuracy of the proposed procedure.

With more sensors being installed by utilities for accurate control of power grids, there is a growing risk of vulnerability to sophisticated data integrity attacks such as false data injection (FDI), circumventing current bad data detection schemes resulting in inaccurate state estimation solutions. While diverse automated detectors to battle FDI have been grown, such methodologies underestimate the strong analytical abilities of humans. This is while most proposed automated methods need observant human control. Although automated methods provide opportunities to improve scalability, humans can cope with exceptions and new attack trends. In this paper, to address the ever-increasing cyber-attack challenge in power systems, a visualization based attack detection framework using deep learning techniques is developed to provide human security researchers with improved techniques to uncover trends, identify outliers, recognize correlations, and communicate their results. Specifically, we first encode multivariate systems state time-series data into 2D colored images and then utilize a carefully designed deep convolutional neural network (CNN) classifier. The proposed method is developed to allow network operators to immediately capture and visually understand the statistical features of a network attack at a glance. The proposed method has been evaluated on the IEEE 14-bus and IEEE 118-bus systems. Our experiments show that the proposed framework accomplishes high classification accuracy.

We address the distributed energy management problem of the economic dispatch of grids in order to balance the power demand and supply. By manipulating the primal problem, we show that the resulting dual problem can be solved by using a decentralized linearized alternating direction method of multipliers. As consequence, in this paper, we propose an optimization based algorithm that enjoys the advantages of both the primal and the dual domain methods. Contrary to the Alternating Direction Method of Multipliers (ADMM), the proposed algorithm exhibits a lower computational cost not requiring at each main step iteration an explicit resolution of a local optimization problem. Moreover, we show through computer simulations that for the IEEE 14-bus and the IEEE 39-bus distribution test system the proposed algorithm achieves a significantly higher convergence rate when compared with recent approaches in the literature to solve the proposed economic dispatch problem.

We analyze the existence of constrained ballistic hopping paths on a nonuniform polygonal chain. This analysis has practical significance in constrained path planning applications for jumping robots where robot dynamic constraints, uneven surface structure, and non-uniform surface properties are considered. We derive closed-form conditions to satisfy i) damage-free robot landing ii) non-sliding of the robot iii) actuator saturation, and iv) intermediate terrain avoidance constraints. Using the closed-form conditions, we propose a traversability algorithm to determine the path existence between two given points on the polygonal chain. Correspondingly, a path generation approach is discussed to generate an optimal constrained hopping path with minimum number of intermediate hops and least take-off speed per hop. Applicability and viability of the proposed algorithms are demonstrated through computer simulations in a realistic terrain scenario with robot jumping motion uncertainties and disturbances.

This article addresses the problem of path following of marine vehicles along straight lines in the presence of currents by resorting to an inner-outer control loop strategy, with due account for the presence of currents. The inner-outer loop control structures exhibit a fast-slow temporal scale separation that yields simple “rules of thumb” for controller tuning. Stated intuitively, the inner-loop dynamics should be much faster than those of the outer loop. Conceptually, the procedure described has three key advantages: (i) it decouples the design of the inner and outer control loops, (ii) the structure of the outer-loop controller does not require exact knowledge of the vehicle dynamics, and (iii) it provides practitioners a very convenient method to effectively implement path-following controllers on a wide range of vehicles. The path-following controller discussed in this article is designed at the kinematic outer loop that commands the inner loop with the desired heading angles while the vehicle moves at an approximately constant speed. The key underlying idea is to provide a seamless implementation of path-following control algorithms on heterogeneous vehicles, which are often equipped with heading autopilots. To this end, we assume that the heading control system is characterized in terms of an IOS-like relationship without detailed knowledge of vehicle dynamics parameters. This paper quantitatively evaluates the combined inner-outer loop to obtain a relationship for assessing the combined system’s stability. The methods used are based on nonlinear control theory, wherein the cascade and feedback systems of interest are characterized in terms of their IOS properties. We use the IOS small-gain theorem to obtain quantitative relationships for controller tuning that are applicable to a broad range of marine vehicles. Tests with AUVs and one ASV in real-life conditions have shown the efficacy of the path-following control structure developed.

This paper presents results on recent developments pertaining to the coordinated motion control of a fleet of marine robotic vehicles. Specifically, we address the Cooperative Moving Path Following (CMPF) motion control problem, that consists of steering the robotic vehicles along a priori specified geometric paths that jointly move according to a target frame, while achieving a pre-defined coordination objective. To this end, each vehicle will need to communicate with their neighbors in order to cooperatively solve the CMPF task. Two distinct robust Moving Path Following motion control strategies for achieving robustness on the moving path following tasks are proposed. Experimental results demonstrating the application of CMPF to marine vehicles in the context of source localization and tracking of underwater targets are presented backed with stability and convergence guarantees.

Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.