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Hierarchical rule‐base reduction (HRBR) was used to create a new form of waypoint navigation control for a skid‐steer vehicle, which consists of a multiple input‐single output nonlinear fuzzy angular velocity controller. HRBR enabled an increase in system complexity by only selecting the rules most influential on state errors. The membership functions of the fuzzy controller employed a trapezoidal structure with a completely symmetric rule‐base. The work was motivated by a desire to find the limitations of fuzzy control in terms of accuracy and robustness across environments. To accomplish this, an examination of the proposed controller is completed by employing test courses. The test courses examine the effects of steering disturbance, phase lag, and overshoot. The controller's performance was compared with existing waypoint navigation controllers, pure pursuit, and the Huskić Buck Zell (HBZ) controller. After initial controller development/tuning was completed in the simulation, the controllers were tested outdoors. The HRBR fuzzy was found to outperform the pure pursuit and HBZ in simulation (36.74% and 80.56% improvement in Root Mean Square Error (RMSE) on one course) and on concrete with mixed results on a surface with different dynamics, namely grass (23.13% and 55.64% improvement in RMSE averaging the two on the same course). If a moderate amount of tuning of the fuzzy controller had not been performed in simulation, failure across different environments would be more likely. The present study is important as it illustrates the potential effectiveness of the HRBR for control applications beyond waypoint navigation. [ABSTRACT FROM AUTHOR]

Designing controllers for skid-steered wheeled robots is complex due to the interaction of the tires with the ground and wheel slip due to the skid-steer driving mechanism, leading to nonlinear dynamics. Due to the recent success of reinforcement learning algorithms for mobile robot control, the Deep Deterministic Policy Gradients (DDPG) was successfully implemented and an algorithm was designed for continuous control problems. The complex dynamics of the vehicle model were dealt with and the advantages of deep neural networks were leveraged for their generalizability. Reinforcement learning was used to gather information and train the agent in an unsupervised manner. The performance of the trained policy on the six degrees of freedom dynamic model simulation was demonstrated with ground force interactions. The system met the requirement to stay within the distance of half the vehicle width from reference paths. [ABSTRACT FROM AUTHOR]

In this paper, a novel Mahalanobis-Taguchi system (MTS)-based fault detection, isolation, and prognostics scheme is presented. The proposed data-driven scheme utilizes the Mahalanobis distance (MD)-based fault clustering and the progression of MD values over time. MD thresholds derived from the clustering analysis are used for fault detection and isolation. When a fault is detected, the prognostics scheme, which monitors the progression of the MD values, is initiated. Then, using a linear approximation, time to failure is estimated. The performance of the scheme has been validated via experiments performed on rolling element bearings inside the spindle headstock of a microcomputer numerical control (CNC) machine testbed. The bearings have been instrumented with vibration and temperature sensors and experiments involving healthy and various types of faulty operating conditions have been performed. The experiments show that the proposed approach renders satisfactory results for bearing fault detection, isolation, and prognostics. Overall, the proposed solution provides a reliable multivariate analysis and real-time decision making tool that (1) presents a single tool for fault detection, isolation, and prognosis, eliminating the need to develop each separately and (2) offers a systematic way to determine the key features, thus reducing analysis overhead. In addition, the MTS-based scheme is process independent and can easily be implemented on wireless motes and deployed for real-time monitoring, diagnostics, and prognostics in a wide variety of industrial environments. [DOI: 10.1115/1.4002545] Keywords: Mahalanobis-Taguchi system, real-time decision making, Mahalanobis distance-based fault detection, isolation and prognostics, rolling element bearings

This paper presents a novel radio-frequency identification (RFID)-based smart freezer using a new inventory-management scheme for extremely low temperature environments. The proposed solution utilizes backpressure inventory control, systematic selection of antenna configuration, and antenna power control. The proposed distributed-inventory-control (DIC) scheme dictates the amount of items transferred through the supply chain. When a high item visibility is ensured, the control scheme maintains the desired level of inventory at each supply-chain echelon. The performance of the DIC scheme is guaranteed using a Lyapunov-based analysis. The proposed RFID antenna-configuration design methodology coupled with locally asymptotically stable distributed power control ensures a 99% read rate of items while minimizing the required number of RFID antennas in the confined cold chain environments with non-RF-friendly materials. The proposed RFID-based smart-freezer performance is verified through simulations of supply chain and experiments on an industrial freezer testbed operating at -100[degrees]F. Index Terms--Inventory control, low-temperature chemical management, passive radio-frequency identification (RFID), power control.

Forward operating bases are temporary military contingency bases established to support and facilitate tactical operations on foreign soil. Operational and logistical inefficiencies, excessive resource demands, and increased costs are some of the issues caused by poor initial planning of contingency bases. In this article, a modeling method is introduced to increase planning capabilities for base camps using an object-oriented approach. The model addresses issues faced by the Department of Defense related to unexpected second-, third-, and higher-order effects observed on base camps, determined to be caused by the interactions between the base camp utility systems. The model is based on a coupled mathematical system of equations that captures the relationships between various base camp subsystems and their respective inputs and outputs. Subsystems are objectified and their various inputs and outputs are parameterized to solve the system of equations simultaneously each time there is a change in base camp design. [ABSTRACT FROM PUBLISHER]

ABSTRACT Contingency basing presents a planner with numerous design decisions driven by multiple design criteria such as the number of soldiers, base permanency, base location, and other factors. The operational environment of the base is not static either; design requirements change as the mission changes. In this work, we introduce a model-based systems engineering approach to elicit design and operational needs while dealing with the design complexity of constructing a contingency base. The model includes the key facility types that can make a contingency base, interactions between facility types, and required utilities for each facility type. The model elements are kept at an abstract level so the details can be altered as required by the customer needs. Pairing the model with an external analysis tool allows for quick development and testing. Properties of the facility types can be altered either in the model or the analysis tool, and reflected in both. Using the model- based systems engineering concepts of reusability, these elements can be saved and re-used in future base designs allowing for a rapid and adaptable design process. In addition, the sharing of information visually with Object Management Group's Systems Modeling Language™ diagrams enhances the ability to collaborate with nonengineering subject matter experts within the design domain. By graphically showing the conditions and layout of the proposed contingency base, Department of Defense personnel not trained in modeling and simulation were able to interact with the engineering designs and identify gaps in the proposed architecture. [ABSTRACT FROM AUTHOR]

A novel Mahalanobis Taguchi System (MTS) based fault detection, isolation, and prognostics scheme is presented. The proposed scheme fuses data from multiple sensors into a single system level performance metric using Mahalanobis Distance (MD), and generates fault clusters based on MD values. MD thresholds derived from the clustering analysis are used for fault detection and isolation. When a fault is detected, the prognostics scheme, which monitors the progression of the MD values over time, is initiated. Then, using a linear approximation, time to failure is estimated. The performance of the scheme has been validated via experiments performed on a mono-block centrifugal water pump testbed. The pump has been instrumented with vibration, pressure, temperature, and flow sensors; and experiments involving healthy and various types of faulty operating conditions have been performed. The experiments show that the proposed approach renders satisfactory results for centrifugal water pump fault detection, isolation, and prognostics. Overall, the proposed solution provides a reliable multivariate analysis and real-time decision making tool that 1) fuses data from multiple sensors into a single system level performance metric; 2) extends MTS by providing a single tool for fault detection, isolation, and prognosis, eliminating the need to develop each separately; and 3) offers a systematic way to determine the key parameters, thus reducing analysis overhead. In addition, the MTS-based scheme is process independent, and can easily be implemented on wireless motesref refid="fnote1"/ id="fnote1" asterisk="no" paraA mote, also known as a sensor node, is a node in a wireless sensor network.para, and deployed for real-time monitoring, diagnostics, and prognostics in a wide variety of industrial environments. [ABSTRACT FROM PUBLISHER]