A Modeling and Analysis Framework To Support Monitoring, Assessment, and Control of Manufacturing Systems Using Hybrid Models

The manufacturing industry has constantly been challenged to improve productivity, adapt to continuous changes in demand, and reduce cost. The need for a competitive advantage has motivated research for new modeling and control strategies able to support reconfiguration considering the coupling between different aspects of plant floor operations. However, models of manufacturing systems usually capture the process flow and machine capabilities while neglecting the machine dynamics. The disjoint analysis of system-level interactions and machine-level dynamics limits the effectiveness of performance assessment and control strategies. This dissertation addresses the enhancement of productivity and adaptability of manufacturing systems by monitoring and controlling both the behavior of independent machines and their interactions. A novel control framework is introduced to support performance monitoring and decision making using real-time simulation, anomaly detection, and multi-objective optimization. The intellectual merit of this dissertation lies in (1) the development a mathematical framework to create hybrid models of both machines and systems capable of running in real-time, (2) the algorithms to improve anomaly detection and diagnosis using context-sensitive adaptive threshold limits combined with context-specific classification models, and (3) the construction of a simulation-based optimization strategy to support decision making considering the inherent trade-offs between productivity, quality, reliability, and energy usage. The result is a framework that transforms the state-of-the-art of manufacturing by enabling real-time performance monitoring, assessment, and control of plant floor operations. The control strategy aims to improve the productivity and sustainability of manufacturing systems using multi-objective optimization. The outcomes of this dissertation were implemented in an experimental testbed. Results demonstrate the potential to support maintenance actions, productivity analysis, and decision making in manufacturing systems. Furthermore, the proposed framework lays the foundation for a seamless integration of real systems and virtual models. The broader impact of this dissertation is the advancement of manufacturing science that is crucial to support economic growth. The implementation of the framework proposed in this dissertation can result in higher productivity, lower downtime, and energy savings. Although the project focuses on discrete manufacturing with a flow shop configuration, the control framework, modeling strategy, and optimization approach can be translated to job shop configurations or batch processes. Moreover, the algorithms and infrastructure implemented in the testbed at the University of Michigan can be integrated into automation and control products for wide availability.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147657/1/migsae_1.pd

Fault-Tolerant Control Strategy for Steering Failures in Wheeled Planetary Rovers

Fault-tolerant control design of wheeled planetary rovers is described. This paper covers all steps of the design process, from modeling/simulation to experimentation. A simplified contact model is used with a multibody simulation model and tuned to fit the experimental data. The nominal mode controller is designed to be stable and has its parameters optimized to improve tracking performance and cope with physical boundaries and actuator saturations. This controller was implemented in the real rover and validated experimentally. An impact analysis defines the repertory of faults to be handled. Failures in steering joints are chosen as fault modes; they combined six fault modes and a total of 63 possible configurations of these faults. The fault-tolerant controller is designed as a two-step procedure to provide alternative steering and reuse the nominal controller in a way that resembles a crab-like driving mode. Three fault modes are injected (one, two, and three failed steering joints) in the real rover to evaluate the response of the nonreconfigured and reconfigured control systems in face of these faults. The experimental results justify our proposed fault-tolerant controller very satisfactorily. Additional concluding comments and an outlook summarize the lessons learned during the whole design process and foresee the next steps of the research

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

Described here is the progress made by Levels 1, 2, and 3 of the Space Station Freedom in developing and applying advanced automation and robotics technology. Emphasis was placed on the Space Station Freedom program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) Progress Report 13, and issues of A&R implementation into the payload operations integration Center at Marshall Space Flight Center. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for Space Station Freedom

Robotic Assembly Control Reconfiguration Based on Transfer Reinforcement Learning for Objects with Different Geometric Features

Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically. Since force-pose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to answering how to reconfigure existing controllers for new assembly objects with different geometric features. In this paper, model-based parameters are first reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL). Then the RL agent is transferred based on the proposed Weighted Dimensional Policy Distillation (WDPD) method. The experiment results demonstrate that the control reconfiguration method costs less time and achieves better control performance, which confirms the validity of proposed methods

Advanced development for space robotics with emphasis on fault tolerance

This paper describes the ongoing work in fault tolerance at the University of Texas at Austin. The paper describes the technical goals the group is striving to achieve and includes a brief description of the individual projects focusing on fault tolerance. The ultimate goal is to develop and test technology applicable to all future missions of NASA (lunar base, Mars exploration, planetary surveillance, space station, etc.)