Inverter based resources (IBRs) are often independently owned and controlled to inject power and provide ancillary services to the grid according to their interconnection agreements. To prevent any unauthorized control mode change (i.e., an insider attack), an AI/ML-based automatic detector is developed to identify the current control mode. The detector uses a set of both steady state and transient metrics from control system theory to learn and classify dynamic responses of IBRs, and it utilizes time response analysis, subspace decomposition, and vector analysis to produce transient features that are machine-learnable. This set of dynamic metrics resolves the ambiguity embedded in the steady state responses of IBRs operating in different control modes. It is shown that, by training a random forest classifier simply on an IBR operating under three common control modes, the AI/ML model can successfully detect control modes by calculating and analyzing transient features on time-series data from a standard power meter.

With an increase in the number of electric vehicles (EVs) and battery storage systems (BSSs), there are major challenges in the distribution grid to maintain a scaling control structure. Also, with the vehicle-to-grid (V2G) technology, EVs can now inject power into the grid for voltage regulation. This article proposes a new distributed real-time alternating direction method of multipliers (ADMM) technique to control EVs and BSSs for voltage regulation while maximizing their utility function. More specifically, a continuous-domain real-time optimization and control algorithm is developed in closed form, which exchanges relevant information among the neighboring nodes through the communication network and optimizes a combined convex objective of EVs and BSSs welfare and voltage regulation with power flow equations as constraints. Convergence analysis is provided using the Lyapunov direct approach, and simulation results are included to illustrate the effectiveness of the proposed scheme.Note to Practitioners—This article is motivated by the problems that an electric distribution system is facing today due to the penetration of electric vehicles and battery storage systems (BSSs). The presence of a large number of electric vehicles (EVs) and BSSs is causing a substantial degradation of the quality and reliability of the power grid. Despite the adverse effects, EVs and BSSs can be controlled and used as a power source to benefit the grid. Also, this control of EVs and BSS cannot be done by a centralized body since this would be nonscalable and would require huge communication bandwidth. To tackle this ever scaling problem, this article develops a continuous-domain multiagent distributed algorithm to control the EVs and BSSs and utilize them to maintain the grid voltage within the normal operating range while also satisfying the consumers by maximizing their welfare. The algorithm was developed in the continuous domain since, in most of the other algorithms that are iterative and in discrete time, the accuracy of the optimal solution greatly depends on the sampling time, and thus, it is not robust to changes that are prevalent with distributed energy resources such as EVs and BSSs.

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.

In this paper, we present an approach to local coverage path (CP) planning for a circular mobile sensor. Our family of algorithms provides completed and overlapped coverage control, curvature and path length control, localization, choice of observer's placement based on sensing range, and first-order differentiable CP. Our family of algorithms begins by partitioning the target region (TR) into several regular triangulations (RTs). Based on the size of the RT and the sensing range, the number and location of observers are determined for all RT. All observers found are then used as waypoints (WPs) to generate baseline CP with the Traveling Salesman Problem's nearest neighbor algorithm. The proposed algorithm solved the problems of how to design a differentiable and collision-free CP for all sensing range, limited and sufficient, while providing completed coverage control, overlapped coverage control, curvature and path length control, localization, collision avoidance, and the choice of observer's placement based on sensing range and the environment. The main technical contributions of the proposed approach is to provide a holistic solution that segments any TR, uses triangulation to determine the observation WPs and then compute the smooth and collision-free CP. Computer simulations are provided to validate the effectiveness and correctness of our algorithms.

The dynamics of an n-link robot is passive from torque to velocity. However, the dynamics of the robot is not passive from torque to position, but is passivity-short. Bilateral teleoperation, that is a negative feedback interconnection of the master and the slave robots, proposed in this paper can be applied to both passive and passivity-short systems. The proposed control is employed through individual compensation for each of the robots. This teleoperation is stable with/without communication delay between the two robots, and it also performs better compared to the existing methods, in terms of minimizing the lag and the synchronization error between position or velocity of the two robots and the reference. The proposed model can be adopted in many applications, instead of the existing approaches, as many systems in practice are passivity-short.

In this paper, we proposed a coverage path (CP) planning approach for a mobile robot moving in a dynamically changing environment. Our family of algorithms begins by finding the first path around the center of interior obstacles/disks of a given target region (TR) through the Graham scan algorithm. The first path is then used as a foundational path to generate a collision free, first order differentiable, and observable CP through a complete set of input and output transformation algorithms which together form the rainbow CP planning approach. The last algorithm of the rainbow CP planning approach finds a sufficient number of observation points on the CP needed to observe the TR. The novelty of our rainbow CP planning approach is that it partitions the TR into different shapes with different properties needed to obtain complete coverage while achieving first order path differentiability. The main technical contributions of the proposed approach is to provide a holistic solution that segments any TR, uses triangulation to determine the line of sights and observation points, and computes the collision-free CP within a quadratic runtime. The proposed method can be readily generalized to address problems of higher dimensions, and it is scalable with respect to the size of TR and the number of robots. Computer simulations are used to illustrate the effectiveness and correctness of the proposed approach.

The increasing number of electric vehicles (EVs) on highways calls for the installment of adequate charging infrastructure. Since charging infrastructure has limited capacity, EVs need to wait at a charging station to get charged, and their waiting times may differ significantly from one location to another. This paper aims at developing a strategy to coordinate the queues among the charging stations, with only local information about traffic flows and the status of EV charging stations along a bidirectional highway, so that excessively long waiting times can be avoided. Specifically, a distributed algorithm is presented to schedule EV flows into neighboring charging stations, so that EVs are all appropriately served along the highway and that all the charging resources are uniformly utilized. In addition, a distributed decision making policy is developed to influence the aggregate number of EVs entering any given service station, so that each EV makes an appropriate decision (i.e., whether or not it should enter the next charging station) by contributing positively to meeting the desired queue length at service stations and by considering its own battery constraint. Performance improvement of the proposed strategy is illustrated via one of the highways in the United States, namely the Florida Turnpike.

Short-term forecasting is frequently identified as an important tool for the effective management of wind generation. However, forecasting errors, inherent to the point forecasts, increase requirements for energy storage and can affect optimal system operation. Probabilistic forecasts can help tackle this issue by providing a proper characterization of forecasting errors in the optimization process. This paper proposes a multivariate model of forecasting data for wind generation. Predictive uncertainty intervals of wind power can be obtained by sampling from the proposed model. The main goal is to use empirical data models without linear or Gaussian approximations of the distributional or temporal variations. The predictive modeling is utilized within a case study of an energy storage system. A modified robust convex programming is used to maintain the practical robustness and feasibility of the solution based on the sampled scenarios from the model.

Human motion prediction is becoming more and more important issue in the filed of wearable robots or biorobotics. This paper provides an initial experimental result for human motion prediction. In detail, the prediction method for ternary choice among rock-paper-scissors is presented using temporal patterns of muscle activations (Electromyography, in short EMG) controlling hand motion of subject. Initial burst part of EMG is prior to the onset of actual movement by dozens to hundreds milliseconds. Using this property, the proposed method makes the ternary choice prediction among rock-paper-scissors as soon as 10% motion variation of any finger is detected. It is shown experimentally that the success rate of the proposed prediction method is over 95%.

We developed an intelligent agent, RoboLeader, that could assist human operators in route planning for a team of ground robots. We compared the operators' target detection performance in the 4-robot and 8-robot conditions. Results showed that the participants detected significantly less targets and had significantly worse situation awareness when there were 8 robots compared to the 4-robot condition. Those participants with higher spatial ability detected more targets than did those with lower spatial ability. Participants' self-assessed workload was affected by the number of robots under control, their gender, and their attentional control ability.

In this paper, we present two near-optimal methods to determine the real-time collision-free path for a mobile vehicle moving in a dynamically changing environment. The proposed designs are based on the polynomial parameterization of feasible trajectories by explicitly taking into account boundary conditions, kinematic constraints, and collision-avoidance criteria. The problems of finding optimal solutions to the parameterized feasible trajectories are then formulated with respect to a near-minimal control-energy performance index and a near-shortest distance performance index, respectively. The obtained optimal solutions are analytical and suitable for practical applications which may require real-time trajectory planning and replanning. Computer simulations are provided to validate the effectiveness of the proposed near-optimal trajectory-planning methods.

In this paper, the cooperative control problem in the presence of communication time delays is addressed for a broad class of multi-input-multi-output dynamical systems in the canonical form with arbitrary relative degrees. A new sampled-data predictive cooperative control is proposed by only using the delayed output feedback information and a set of less restrictive conditions on the connectivity of time varying sensor/communication networks are established. No fixed leader or time invariant sensor/communication networks are assumed. In particular, through the elaborated switching between the decoupled control and the proposed predictive cooperative control, the convergence of the overall system is rigorously proved under a mild condition on the choice of sampling period. Simulation results on the cooperative consensus for a group of mobile robots are provided to illustrate the effectiveness of the proposed approach

In this paper, the problem of determining an optimal collision-free path is studied for a mobile robot moving in a dynamically changing environment. A polynomial parametrization is used to represent the family of mobile robot’s paths and to solve for feasible trajectories. Then a suitable performance index is set up and the trajectory generation problem is formulated as a constrained optimization problem subject to motion constraints and the collision avoidance criterion. The optimal solution is analytical and simple. It is good for realtime trajectory planning. Finally, the framework is applied to a car-like mobile robot. The validity of our approach is supported by computer simulations.

This paper addresses the problem of determining a collision-free path for a mobile robot moving in a dynamically changing environment. By explicitly considering kinematic, model of the robot, the family of feasible trajectories and their corresponding steering controls are derived in a closed form. Then, a new collision avoidance condition is developed for the dynamically changing environment, it consists of a time criterion and a geometrical criterion, and it has explicit physical meanings in both the transformed space and the original working space. By imposing the avoidance condition, one can determine the corresponding steering angle for collision avoidance in a closed form. Such a path meets all boundary conditions, is continuous, and can be updated in real time once a change in the environment is detected. Simulations show that the proposed method is effective.

The problem of determining a collision-free path for a mobile robot moving in a dynamically changing environment is addressed in this paper. By explicitly considering a kinematic model of the robot, the family of feasible trajectories and their corresponding steering controls are derived in a closed form and are expressed in terms of one adjustable parameter for the purpose of collision avoidance. Then, a new collision-avoidance condition is developed for the dynamically changing environment, which consists of a time criterion and a geometrical criterion, and it has explicit physical meanings in both the transformed space and the original working space. By imposing the avoidance condition, one can determine one (or a class of) collision-free path(s) in a closed form. Such a path meets all boundary conditions, is twice differentiable, and can be updated in real time once a change in the environment is detected. The solvability condition of the problem is explicitly found, and simulations show that the proposed method is effective.

In this paper, 2-D shape recognition is done using a combination of recursive search of landmarks, landmark-based invariant features, and a Fuzzy-ART neural-network classifier. To make this novel combination work well, a upper limit is imposed on the number of total landmarks allowed, and this maximum size is then translated into fixed dimensions of invariant features and into the neural processing of the features. It is shown that the recursive landmark search approximates very well any smooth 2-D shape contour, that the shape features used are independent of perspective transformation, and that, when combined with a Fuzzy-ART classifier, unknown features can be efficiently learned on-line to identify multiple distinct objects. An illustrative example is used to demonstrate effectiveness of the proposed algorithm.

This paper illustrates a new learning control for tracking robot manipulators in the presence of electrical dynamics using Lyapunov direct method. It has been shown that under the proposed learning control robotic systems are always guaranteed to be asymptotically stable with respect to the number of trials. The proposed control is also robust in the sense that the exact knowledge of the nonlinear dynamics is not required except for bounding functions on the magnitude.

Presents an approach for designing robust tracking controllers for rigid-link flexible-joint (RLFJ) robot manipulators with redundant actuators in the joints. With the proposed controller, the authors prove that the link tracking error is globally uniformly ultimately bounded (GUUB) in spite of additive bounded disturbances, parametric uncertainty, and other modeling uncertainty. The authors also illustrate how the load at each joint is shared by two actuators. Finally simulation results are presented to illustrate the effectiveness of the proposed controller.>

Hand/eye calibration is the process of identifying the unknown position and orientation of the camera frame with respect to the robot hand frame, when the camera is rigidly mounted on the robot hand. While computationally slightly more involved, one-stage iterative algorithms have two distinguished advantages over traditional two-stage linear approaches: 1) they are less sensitive to noise, and 2) they can handle cases in which the camera orientation information is not available. A more compact and lower dimensional identification Jacobian is derived. The Jacobian, which relates measurement residuals to pose error parameters of the unknown hand/eye transformation, is a crucial component of one-stage iterative algorithms. The derivation procedure for the new Jacobian is straightforward and simple, owing to an alternative mathematical formulation of the hand/eye calibration problem. Observability conditions of the pose error parameters in the unknown hand/eye transformation are also provided based on this identification Jacobian.>

Two robust controllers are developed for the position and force control of a single n-joint nonredundant manipulator. The control strategies are shown to apply when the manipulator is unconstrained and when it is constrained by a rigid environment. Starting with the model of the robot dynamics and constraints in joint space, a dynamic equation is developed with respect to a set of task space coordinate variables. Using this task space model, the two control strategies are developed for the manipulator, and are shown to be robust with respect to uncertainty in the system. The robust control strategies have the following characteristics: (1) exponential convergence for motion tracking, (2) the controller is continuous for all times of interest, and (3) the controller is the same for constrained and unconstrained motion.>

The authors illustrate how the nonadaptive part of the desired compensation adaptive law (DCAL) is a special case of a class of controllers that can be used to obtain a global stability result for the trajectory following problem for robot manipulators. This class of robot controllers is a simple linear proportional derivative (PD) controller plus additional nonlinear terms that are used to compensate for uncertain nonlinear dynamics. To analyze the stability of this class of controllers, Lyapunov's second method is used to derive a global uniform ultimate boundedness (GUUB) stability result for the tracking error. How the controller gains can be adjusted to obtain better tracking performance in spite of the uncertainty present in the robot manipulator dynamic equation is shown.>

For the trajectory following problem of a robot manipulator, a novel linear learning control law, consisting of the conventional proportional-integral-differential (PID) control law, with respect to position tracking error, and an iterative learning term, is provided. The learning part is a linear feedback control of position, velocity, and acceleration errors. It is shown that this learning control can make the position, velocity, and acceleration tracking errors asymptotically stable in the presence of highly nonlinear dynamics. It is also shown that the proposed control is robust in the sense that no knowledge about nonlinear dynamics is required except for its bounding function. As a consequence, neither linear approximation of nonlinear dynamics nor repeatability of robot motion is required. A constructive algorithm for choosing the control gains is also provided. A simple two-degrees-of-freedom manipulator was simulated to test the proposed learning control law.>

The commenters point out an error in the adaptive impedance-control approach for robotic manipulators given in the above-titled paper by W.-S. Lu and Q.-H. Meng (ibid., vol.7, no.3, p.408-15, June 1991). Lu and Meng assert that their adaptive impedance-control scheme can compensate for uncertainty in the force measurements; however, it is shown that it is possible for the uncertainty in the force measurements to cause the parameter estimates to go unbounded. To remedy this possible instability problem, an additional auxiliary controller is suggested. In their reply, Lu and Meng fail to find any error in the Lyapunov analysis of their algorithm. They are not convinced that one should seek a theoretically asymptotically stable solution with a controller that has a highly undesirable feature.>