Small-Cell Installation in Transportation Infrastructure—A Literature Review

The purpose of this report is to provide information to the Illinois Department of Transportation (IDOT) on small-cell deployment on infrastructure such as light poles and traffic signals. A literature review was conducted on the technical specifications and impacts of small-cell deployment. The report explores the use of small-cell systems and potential hazards of small-cell deployment from an electromagnetic field perspective. A survey was conducted to gather information at a state and local level on current and future trends of small-cell deployment. The information gathered from the survey was combined from a standpoint of current best practices. The report provides recommendations for contractual obligations for both the department of transportation (DOT) and the small-cell provider. The report also provides guidelines on the best locations for small cells from a functional, structural, and aesthetic standpoint. The conclusion is that small-cell deployment is in our near future and the benefits of this technology are broad and mostly unexplored. While challenges exist, with proper contractual risk mitigation, both DOT entities and small-cell providers can reap benefits from the expansion of technology.IDOT-R27-SP41Ope

Evolution of adaptive learning for nonlinear dynamic systems: a systematic survey

The extreme nonlinearity of robotic systems renders the control design step harder. The consideration of adaptive control in robotic manipulation started in the 1970s. However, in the presence of bounded disturbances, the limitations of adaptive control rise considerably, which led researchers to exploit some “algorithm modifications”. Unfortunately, these modifications often require a priori knowledge of bounds on the parameters and the perturbations and noise. In the 1990s, the field of Artificial Neural Networks was hugely investigated in general, and for control of dynamical systems in particular. Several types of Neural Networks (NNs) appear to be promising candidates for control system applications. In robotics, it all boils down to making the actuator perform the desired action. While purely control-based robots use the system model to define their input-output relations, Artificial Intelligence (AI)-based robots may or may not use the system model and rather manipulate the robot based on the experience they have with the system while training or possibly enhance it in real-time as well. In this paper, after discussing the drawbacks of adaptive control with bounded disturbances and the proposed modifications to overcome these limitations, we focus on presenting the work that implemented AI in nonlinear dynamical systems and particularly in robotics. We cite some work that targeted the inverted pendulum control problem using NNs. Finally, we emphasize the previous research concerning RL and Deep RL-based control problems and their implementation in robotics manipulation, while highlighting some of their major drawbacks in the field

State-of-charge and state-of-health prediction of lead-acid batteries with genetic algorithms

International audienceIn this paper, a state of charge (SoC) and state of health (SoH) estimator is presented for lead-acid batteries. The estimation strategy is based on adaptive control theory for online parameters identification. To speed up the estimator's convergence, the adaptation law is replaced by a genetic algorithm (GA). Therefore, robustness to parameters variation is also achieved and thus, accurate prediction with battery aging. Unlike other estimation strategies, only battery terminal voltage and current measurements are required. Results show high convergence and highlight the performance of the proposed estimator in predicting the SoC and SoH with high accuracy

Universal dynamic tracking control law for mobile robot trajectory tracking

This manuscript presents a universal control law for a class of complex dynamic systems, such as mobile robots. The trajectory tracking problem is among the major problems in the field of robotics. The proposed universal control law solves the trajectory tracking problem of mobile robots. Despite a large body of research on developing robot's control laws conducted in the literature, the dynamic effects of robots are often not taken into consideration while deriving control laws that define their trajectories. In most cases, the robot's trajectory tracking and/or stabilization problems are addressed based on its kinematic model due to simplicity. Therefore, the need for a universal control law based on the robot's dynamic and/or kinematic model is significant. Even though the feedback control theory is well-established in the field of robotics, the control laws for dynamic systems are typically more complex than system models themselves. Furthermore, control laws are required to be adapted depending on models of different mobile robot systems. Here, the development of universal control law is underscored, i.e., the paper is aimed at developing universal dynamic tracking control law that solves the trajectory tracking problem of a class of mobile robots. The controller is tested through a set of computer simulations using a differential drive mobile robot operating in an indoor planar environment

Observer-based adaptive control of PMSMs with disturbance compensation and speed estimation

In this paper, an observer-based adaptive control strategy is presented for permanent magnet synchronous machines (PMSMs). The adaptive control scheme achieves accurate tracking using the machine's inverse dynamics and an observer to approximate disturbance and speed used as feedback. The adaptive controller is validated through a set of simulations. Results show high speed tracking and estimation accuracy

Intelligent networked navigation of mobile robots with collision avoidance

A major part of mobile robotics research is focused on the navigation of a team of networked mobile robots in indoor and/or outdoor environments. The navigation is indeed an important module as it allows necessary control operations for mobile robots. In addition, this module empowers robots to maneuver efficiently and effectively. In most cases, this task requires an accurate mathematical model of the environment, path, and trajectory of robots. The fuzzy logic-based robot navigation strategy proved its power when mathematical models of robots and their operating environments are unknown precisely. In this paper, we propose a full-fledged robot navigation strategy where a team of mobile robots navigates along a predefined set of waypoints in an indoor environment while avoiding a collision. The proposed navigation system consists of two parts that are currently being developed. The first part of the navigation system described in this paper is the fuzzy logic control, which determines appropriate actuator commands for the robots' actuators. The second part is the Petri-net model. While the former part is employed to avoid obstacles and reaching waypoints, the latter one illustrates a collision avoidance strategy among multiple robots. A part of theoretical results presented in this paper is backed up by computer simulations