Pradeepkumar Ashok

A Visualization Framework for Real Time Decision Making in a Multi-Input Multi-Output System

Human beings have the capacity to make quick and accurate decisions when multiple objectives are involved provided they have access to all the relevant information. Accurate visual measures/decision surfaces (maps) are critical to the effectiveness of this process. This paper introduces a methodology that allows one to create a visual decision making interface for any multi-input multi-output (MIMO) system. In this case, the MIMO is thought of in the broadest sense to include battlefield operations, complex system design, and human support systems (rehabilitation). Our methodology starts with a Bayesian causal network approach to modeling the MIMO system. Various decision making scenarios in a typical MIMO system are presented. This is then followed by a description of the framework that allows for the presentation of the relevant scenario dependent data to the human decision maker (HDM). This presentation is in the form of 3-D surface plots called decision surfaces. Additional decision making tools (norms) are then presented. These norms allow for single value numbers to be presented along with the decision surfaces to better aid the HDM. We then present some applications of the framework to representative MIMO systems. This methodology easily adapts to systems that grow bigger and also when two or more systems are combined to form a larger system.

Guidelines for Managing Sensors in Cyber Physical Systems with Multiple Sensors

Cyber physical systems (CPSs) typically have numerous sensors monitoring the various physical processes involved. Some sensor failures are inevitable and may have catastrophic effects. The relational nature of the diverse measurands can be very useful in detecting faulty sensors, monitoring the health of the system, and reducing false alarms. This paper provides procedures on how one may integrate data from the various sensors, by careful design of a sensor relationship network. Once such a network has been adopted, choices become available in real time for enhancing the reliability, safety, and performance of the overall system.

The Need for a Performance Map Based Decision Process

This paper presents a mathematical framework to create a unique decision process based on multiple performance maps of varying functional value and volatility to aid in complex decisions for highly nonlinear and coupled systems by involving human visualization and direct algebraic computations. This map-based process is intended to extend other decision tools, such as fuzzy logic and classical optimization (through cost functions) by embedding in-depth measurement certification, lessons learned, and data uncertainty in geometric map representations. As systems become ever more complex with more human intervention, reliable decisions must be made in less and less time (i.e., a few milliseconds). This decision framework is best carried out with selected maps combined into envelopes as functional/proven decision surfaces. Its potential for data-driven decisions of broad applicability is illustrated in several unique domains ranging from an intelligent actuator, a sensored soldier, health care provider management, a venture capital firm, and in human guided design/synthesis processes for parametrically dense systems. We also show that there is a geometrical structure (serial, parallel, or hybrid) to these decision processes which guides the efficient formulation of the most useful forward or inverse computation.

Simultaneous Sensor and Process Fault Detection and Isolation in Multiple-Input–Multiple-Output Systems

Dependable sensor data are vital in complex systems, which rely on a suite of sensors for control as well as condition monitoring. With any unanticipated deviations in sensor values, the challenge is to determine if the anomalies are the result of one or more flawed sensors or if it is indicative of a potentially more serious system-level fault. This paper describes a methodology using Bayesian networks to distinguish between sensor and process faults as well as faults involving multiple sensors or processes. A review of existing methodologies is presented first, followed by a description of the sensor/process fault detection and isolation (SPFDI) algorithm, its limitations and corresponding mitigating strategies. Discussions are also provided on the potential for false alarms and real-time updates of the system model based on validated sensor data. Factors that affect the algorithm such as the effect of network structure, sensor characteristics, effect of discretization, etc., are discussed. This is followed by details of implementation of the algorithm on an electromechanical actuator (EMA) test bed.

Visual Performance Maps for Human Choice in Hybrid Electric Vehicle’s In-Wheel Motors: Part I — Purchase Criteria

Satisfying human needs means to respond directly to human choice / human commands at the time of purchase, in real time operation, for maintenance / tech mods over the life history of the vehicle, and for refreshment in the future hybrid electric vehicles (HEV) equipped with four-independent in-wheel motors (IWM). This leads to maximizing human choice. To meet human choice means not only to keep the human fully informed on a series of choices, but also to maximize their self-awareness.Meeting human choice requires visual performance maps. Based on the future HEV with an open (modular) architecture, visual performance maps help customers make right choices what they want, so that a vehicle can be tailored to a particular customer priority such as cost and drivability for an aggressive driver. This paper demonstrates how different types of an IWM are matched to different types of customers. The decision framework developed in this paper is based on detailed human needs structured by performance maps to visually guide the customer in terms of purchase / operation / maintenance / refreshment decisions. Part I is focused on purchase criteria, while Part II discusses operation / maintenance / refreshment criteria.Copyright

Visual Performance Maps for Human Choice in Hybrid Electric Vehicle’s In-Wheel Motors: Part II — Operation, Maintenance, and Refreshment Criteria

Part I of this paper demonstrated how different human choices affect the selection of all basic components of a Hybrid Electric Vehicles (HEV) equipped with four-independent In-Wheel Motors (IWM) based on detailed human needs structured by visual performance maps to guide the customer in terms of purchase criteria: cost, weight, power, acceleration, gradeability, braking, handling, ride comfort, efficiency, and durability.This Part II discusses ten operation criteria: cornering force margin, roll angle, sideslip angle, lateral acceleration, slip angle, yaw rate, acceleration force margin, braking force margin, pitch angle, and travel range. These visual performance maps show the effects of HEV weight on acceleration, braking, and cornering maneuvers under various road conditions (i.e., dry asphalt, wet asphalt, snowy or icy road) which are evaluated and compared based on the implementation of a nonlinear 14 DOF full-vehicle model based on ride (7 DOF), handling (3 DOF), tire (4 DOF), slip ratio, slip angle, and the tire magic formula. In addition, this paper demonstrates how different human choices affect the HEV’s expected performance. Lastly, maintenance and refreshment criteria are presented and explained.Copyright

Autonomous Robotic System for Pin Pulling Operation in Rail Yard

The railroad industry expends significant effort to increase operational safety and efficiency by using a variety of sensors for machine health monitoring and inspection purposes. This paper proposes to advance rail technology even further to use similar sensor data for the control of a robotic system designed to automate the uncoupling of freight cars, a hazardous operation that currently requires human operators to interact with moving trains. To automate this process, an intelligent robotic system was developed to detect, track, approach, grasp, and manipulate semi-constrained objects on equipment in motion. This work presents a system prototype that utilizes machine vision, force feedback, and complex end-effector technology capable of autonomously uncoupling full-scale freight cars using visual and tactile feedback. Laboratory tests have proven that modern robotic and sensing hardware can be used to reliably separate pairs of rolling stock at 3.25 kilometers per hour. The results to date suggest that speeds of up to 7 km/h are feasible for a system deployed in a rail yard.© 2015 ASME

Modeling and Control of Automated Pipe Hoisting in Oil and Gas Well Construction

This paper presents a robust control algorithm for automatic hoisting of a drill string in oil and gas drilling operations. We demonstrate an iterative scheme for trajectory design and present a lumped dynamic model of the hoisting system. The trajectory is used along with the dynamic model to design a hybrid sliding mode and gain scheduled PI controller to deal with the frictional nonlinearities of the system. The simulation results demonstrate the feasibility of this approach in optimally performing the pipe hoisting task.Copyright

Computer Aided Design of Switched Reluctance Motors for Use in Robotic Actuators

The switched reluctance motor (SRM) is emerging as a strong contender to the permanent magnet synchronous motor as a prime mover of choice in robotic actuators. This paper describes a design synthesis tool for switched reluctance motors. First, a review of the currently existing design tools is presented. The paper then details a parametric design synthesis procedure that requires the formulation of analytical relationships that involve SRM performance and design parameters. The analytical relationships (rules of thumb for design) thus developed can be used along with parametric reduction techniques to work towards an optimal design.