Majority of the products get discarded at end-of-life (EoL), causing environmental pollution, and resulting in a complete loss of all materials and embodied energy. Adopting a closed-loop material flow approach can aid preventing such losses and enable EoL value recovery from these products. Design and engineering decisions made and how products are used impact the capability to implement EOL strategies such as disassembly and remanufacturing. Some underlying factors affecting the capability to implement these EOL strategies have been discussed in previous studies. However, relevant metrics and attributes are not well defined and comprehensive methods to quantitatively evaluate them are lacking. This study will first identify key lifecycle oriented metrics affecting disassemblability and remanufacturability. Then a methodology is proposed for the quantitative evaluation of these strategies considering the quality of returns, product-design characteristics and process technology requirements. Finally, an industrial case-study is presented to demonstrate the application of the proposed method.

Linear covariance analysis has been utilized in a wide variety of applications. Historically, the theory has made significant contributions to navigation system design and analysis. More recently, the theory has been extended to capture the combined effect of navigation errors and closed-loop control on the performance of the system. These advancements have made possible rapid analysis and comprehensive trade studies of complicated systems ranging from autonomous rendezvous to vehicle ascent trajectory analysis. Comprehensive trade studies are also needed in the area of gimbaled pointing systems where the information needs are different from previous applications. It is therefore the objective of this research to extend the capabilities of linear covariance theory to analyze the closed-loop navigation and control of a gimbaled pointing system. The extensions developed in this research include modifying the linear covariance equations to accommodate a wider variety of controllers. This enables the analysis of controllers common to gimbaled pointing systems, with internal states and associated dynamics as well as actuator command filtering and auxiliary controller measurements. The second extension is the extraction of power spectral density estimates from information available in linear covariance analysis. This information is especially important to gimbaled pointing systems where not just the variance but also the spectrum of the pointing error impacts the performance. The extended theory is applied to a model of a gimbaled pointing system which includes both flexible and rigid body elements as well as input disturbances, sensor errors, and actuator errors. The results of the analysis are validated by direct comparison to a Monte Carlo-based analysis approach. Once the developed linear covariance theory is validated, analysis techniques that are often prohibitory with Monte Carlo analysis are used to gain further insight into the system. These include the creation of conventional error budgets through sensitivity analysis and a new analysis approach that combines sensitivity analysis with power spectral density estimation. This new approach resolves not only the contribution of a particular error source, but also the spectrum of its contribution to the total error. In summary, the objective of this dissertation is to increase the utility of linear covariance analysis for systems with a wide variety of controllers and for whom the spectrum of the errors is critical to performance.

<p>Brain-machine interfaces (BMIs) use neuronal activity to control external actuators. As such, they show great promise for restoring motor and communication abilities in persons with paralysis or debilitating neurological disorders.</p><p>While BMIs aim to enact normal sensorimotor functions, so far they have lacked afferent feedback in the form of somatic sensation. This deficiency limits the utility of current BMI designs and may hinder the translation of future clinical BMIs, which will need a means of delivering sensory signals from prosthetic devices back to the user. </p><p>This dissertation describes the development of brain-machine-brain interfaces (BMBIs) capable of bidirectional communication with the brain. The interfaces consisted of efferent and afferent modules. The efferent modules decoded motor intentions from the activity of populations of cortical neurons recorded with chronic multielectrode recording arrays. The activity of these ensembles was used to drive the movements of a computer cursor and a realistic upper-limb avatar. The afferent modules encoded tactile feedback about the interactions of the avatar with virtual objects through patterns of intracortical microstimulation (ICMS).</p><p>I first show that a direct intracortical signal can be used to instruct rhesus monkeys about the direction of a reach to make with a BMI. Rhesus monkeys placed an actuator over an instruction target and obtained, from the target's artificial texture, information about the correct reach path. Initially these somatosensory instructions took the form of vibrotactile stimulation of the hands. Next, ICMS of primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PPC) in another was substituted for this peripheral somatosensory signal. Finally, the monkeys made direct brain-controlled reaches using the activity of ensembles of primary motor cortex (M1) cells, conditional on the ICMS cues. The monkey receiving ICMS of S1 was able to achieve the same level of proficiency with ICMS as with the stimulus delivered to the skin of the hand. The monkey receiving ICMS of PPC was unable to perform the task above chance. This experiment indicates that ICMS of S1 can form the basis of an afferent prosthetic input to the brain for guiding brain-controlled prostheses.</p><p>I next show that ICMS of S1 can provide feedback about the interactions of a virtual-reality upper-limb avatar and virtual objects, enabling active touch. Rhesus monkeys initially controlled the avatar with the movements of their arms and used it to search through sets of up to three objects. Feedback in the form of temporal patterns of ICMS occurred whenever the avatar touched a virtual object. Monkeys learned to use this feedback to find the objects with particular artificial textures, as encoded by the ICMS patterns, and select those associated with reward while avoiding selecting the non-rewarded objects. Next, the control of the avatar was switched to direct brain-control and the monkeys continued to move the avatar with motor commands derived from the extracellular neuronal activity of M1 cells. The afferent and efferent modules of this BMBI were temporally interleaved, and as such did not interfere with each other, yet allowed effectively concurrent operation. Cortical motor neurons were measured while the monkey passively observed the movements of the avatar and were found to be modulated, a result that suggests that concurrent visual and artificial somatosensory feedback lead to the incorporation of the avatar into the monkey's internal brain representation.</p><p>Finally, I probed the sensitivity of S1 to precise temporal patterns of ICMS. Monkeys were trained to discriminate between periodic and aperiodic ICMS pulse trains. The periodic pulse-trains consisted of 200 Hz bursts at a 10 Hz secondary frequency. The aperiodic pulse trains had a distorted periodicity and consisted of 200 Hz bursts at a variable instantaneous secondary frequency. The statistics of the aperiodic pulse trains were drawn from a gamma distribution with equal mean inter-burst intervals to the periodic pulse trains. The monkeys were able to distinguish periodic pulse trains from aperiodic pulse trains with coefficients of variation of 0.25 or greater. This places an upper-bounds on the communication bandwidth that can be achieved with a single channel of temporal ICMS in S1.</p><p>In summary, rhesus monkeys were augmented with a bidirectional neural interface that allowed them to make reaches to objects and discriminate them by their textures--all without making actual movements and without relying on somatic sensation from their real bodies. Both action and perception were mediated by the brain-machine-brain interface. I probed the sensitivity of the afferent leg of the interface to precise temporal patterns of ICMS. Moreover, I describe evidence that the BMBI controlled avatar was incorporated into the monkey's internal brain representation. These results suggest that future clinical neuroprostheses could implement realistic feedback about object-actuator interactions through patterns of ICMS, and that these artificial somatic sensations could lead to the incorporation of the prostheses into the user's body schema.</p> / Dissertation

In this paper, we identify sufficient conditions for Lyapunov Mean Square Stability (LMSS) of a contention-based network of first-order systems, with state-based schedulers. The stability analysis helps us to choose policies for adapting the scheduler threshold to the delay from the network and scheduler. We show that three scheduling laws can result in LMSS: constant-probability laws and additively increasing or decreasing probability laws. Our results counter the notions that increasing probability scheduling laws alone can guarantee stability of the closed-loop system, or that decreasing probability scheduling laws are required to mitigate congestion in the network. / <p>QC 20130116</p>

This research is to carry out an experimental study to examine and verify the effectiveness of the control algorithms and strategies developed at the Advanced Engineering Design Laboratory (AEDL). For this purpose, two objectives are set to be achieved in this research. The first objective is to develop a generic experiment environment (test bed) such that different control approaches and algorithms can be implemented on it. The second objective is to conduct an experimental study on the examined control algorithms, as applied to the above test bed. <p>To achieve the first objective, two main test beds, namely, the real-time controllable (RTC) mechanism and the hybrid machine, have been developed based on a two degree of freedom (DOF) closed-loop five-bar linkage. The 2-DOF closed-loop mechanism is employed in this study as it is the simplest of multi-DOF closed-loop mechanisms, and control approaches and conclusions based on a 2-DOF mechanism are generic and can be applied to a closed-loop mechanism with a higher number of degrees of freedom. The RTC mechanism test bed is driven by two servomotors and the hybrid machine is driven by one servomotor and a traditional CV motor. To achieve the second objective, an experimental study on different control algorithms has been conducted. The Proportional Derivative (PD) based control laws, i.e., traditional iii PD control, Nonlinear-PD (NPD) control, Evolutionary PD (EPD) control, non-linear PD learning control (NPD-LC) and Adaptive Evolutionary Switching-PD (AES-PD) are applied to the RTC mechanism; and as applied to the Hybrid Actuation System (HAS), the traditional PD control and the SMC control techniques are examined and compared. <p> In the case of the RTC mechanism, the experiments on the five PD-based control algorithms, i.e., PD control, NPD control, EPD, NPD-LC, and AES-PD, show that the NPD controller has better performance than the PD controller in terms of the reduction in position tracking errors. It is also illustrated by the experiments that iteration learning control (ILC) techniques can be used to improve the trajectory tracking performance. <p>However, AES-PD showed to have a faster convergence rate than the other ILC control laws. Experimental results also show that feedback ILC is more effective than the feedforward ILC and has a faster convergence rate. In addition, the results of the comparative study of the traditional PD and the Computed Torque Control (CTC) technique at both low and high speeds show that at lower speeds, both of these controllers provide similar results. However, with an increase in speed, the position tracking errors using the CTC control approach become larger than that of the traditional PD control. In the case of the hybrid machine, PD control and SMC control are applied to the mechanism. The results show that for the control of the hybrid machine and the range of speed used in this experimental study, PD control can result in satisfactory performance. However, SMC proved to be more effective than PD control.

This research is to carry out an experimental study to examine and verify the effectiveness of the control algorithms and strategies developed at the Advanced Engineering Design Laboratory (AEDL). For this purpose, two objectives are set to be achieved in this research. The first objective is to develop a generic experiment environment (test bed) such that different control approaches and algorithms can be implemented on it. The second objective is to conduct an experimental study on the examined control algorithms, as applied to the above test bed. <p>To achieve the first objective, two main test beds, namely, the real-time controllable (RTC) mechanism and the hybrid machine, have been developed based on a two degree of freedom (DOF) closed-loop five-bar linkage. The 2-DOF closed-loop mechanism is employed in this study as it is the simplest of multi-DOF closed-loop mechanisms, and control approaches and conclusions based on a 2-DOF mechanism are generic and can be applied to a closed-loop mechanism with a higher number of degrees of freedom. The RTC mechanism test bed is driven by two servomotors and the hybrid machine is driven by one servomotor and a traditional CV motor. To achieve the second objective, an experimental study on different control algorithms has been conducted. The Proportional Derivative (PD) based control laws, i.e., traditional iii PD control, Nonlinear-PD (NPD) control, Evolutionary PD (EPD) control, non-linear PD learning control (NPD-LC) and Adaptive Evolutionary Switching-PD (AES-PD) are applied to the RTC mechanism; and as applied to the Hybrid Actuation System (HAS), the traditional PD control and the SMC control techniques are examined and compared. <p> In the case of the RTC mechanism, the experiments on the five PD-based control algorithms, i.e., PD control, NPD control, EPD, NPD-LC, and AES-PD, show that the NPD controller has better performance than the PD controller in terms of the reduction in position tracking errors. It is also illustrated by the experiments that iteration learning control (ILC) techniques can be used to improve the trajectory tracking performance. <p>However, AES-PD showed to have a faster convergence rate than the other ILC control laws. Experimental results also show that feedback ILC is more effective than the feedforward ILC and has a faster convergence rate. In addition, the results of the comparative study of the traditional PD and the Computed Torque Control (CTC) technique at both low and high speeds show that at lower speeds, both of these controllers provide similar results. However, with an increase in speed, the position tracking errors using the CTC control approach become larger than that of the traditional PD control. In the case of the hybrid machine, PD control and SMC control are applied to the mechanism. The results show that for the control of the hybrid machine and the range of speed used in this experimental study, PD control can result in satisfactory performance. However, SMC proved to be more effective than PD control.

Closed-loop supply chain (CLSC) management provides opportunity for cost savings through the integration of product recovery activities into traditional supply chains. Product recovery activities, such as remanufacturing, reclaim a portion of the previously added value in addition to the physical material. Our problem setting is motivated by the practice of an Original Equipment Manufacturer (OEM) in the automotive service parts industry, who operates a well established forward network. The OEM faces customer demand due to warranty and beyond warranty vehicle repairs. The warranty based demand induces part returns. We consider a case where the OEM has not yet established a product recovery network, but has a strategic commitment to implement remanufacturing strategy. In accomplishing this commitment, complications arise in the network design due to activities and material movement in both the forward and reverse networks, which are attributed to remanufacturing. Consequently, in implementing the remanufacturing strategy, the OEM should simultaneously consider both the forward and reverse flows for an optimal network design, instead of an independent and sequential modeling approach. In keeping with these motivations, and with the goal of implementing the remanufacturing strategy and transforming independent forward and reverse supply chains to CLSCs, we propose to investigate the following research questions: 1. How do the following transformation strategies leverage the CLSC?s overall cost performance? ? Extending the already existing forward channel to incorporate reverse channel activities. ? Designing an entire CLSC network. 2. How do the following network flow integration strategies influence the CLSC?s overall cost performance? ? Using distinct forward and reverse channel facilities to manage the corresponding flows. ? Using hybrid facilities to coordinate the flows. In researching the above questions, we address significant practical concerns in CLSC network design and provide cost measures for the above mentioned strategies. We also contribute to the current literature by investigating the optimal CLSC network design. More specifically, we propose three models and develop mathematical formulations and novel solution approaches that are based on decomposition techniques, heuristics, and meta-heuristic approaches to seek a solution that characterizes the configuration of the CLSC network, along with the coordinated forward and reverse flows.

With the widespread use of doubly-fed induction generators (DFIG) in wind energy conversion systems, condition monitoring is being given importance. Non-intrusive techniques like motor current signature analysis (MCSA), which involves looking for specific frequency components in the current spectrum, are preferred over analysis of magnetic field, temperature, vibrations or acoustic noise which require additional sensors. The major difficulty in MCSA is isolation of the fault, as multiple faults produce similar signatures. Moreover, closed-loop control makes diagnostics more complicated due to inherent compensation by the controller. This thesis presents a method to diagnose static and dynamic air gap eccentricities in doubly-fed induction generators operated for closed-loop stator power control by using a modified control technique to enable detection and isolation of this fault from electrical unbalances in the stator and rotor and load torque oscillations that produce similar effects. The effectiveness of the proposed control is verified using simulations and preliminary experiments performed on a healthy machine.

Global urbanization trends and climate changes result in a significant pressure for a future development of urban areas. The construction industry can play a primary role in addressing some of the challenges, but in order to make it happen, the phases of design, construction, use and deconstructio of a building should meet the criteria of sustainability. The closed loop approach can help the construction industry to move in the right direction.  This thesis analysis the closed loop approach to identify its potential contribution in solving the sustainability challenges in urban areas, as well as explores the key aspects helping or hindering the implementation of the approach. The methods used for the research include the framework for strategic sustainable development, case study analysis and interviews with experts in the field. The research exposed several gaps in the use of the approach moving towards sustainability, mainly due to the fact that the unique and shared definition of it is missing and neither actions nor tools are suggested for a successful implementation of the approach. Main barriers are related to the material choice, flexibility of the design, communication and legislation aspects, work with supply chain and interaction with stakeholders. Nonetheless most of them are also seen as potential enablers. Recommendations are provided to help overcoming the existing barriers and valorizing the key enablers, but the clear definition of the approach is necessary to exploit potentialitie of the closed loop approach.

Low-thrust spacecraft trajectory optimization is often a difficult and time-consuming process. One alternative is to instead use a closed-loop, feedback-driven control law, which calculates the control using knowledge of only the current state and target state, and does not require the solution of a nonlinear optimization problem or system of nonlinear equations. Though generally suboptimal, such control laws are attractive because of the ease and speed with which they may be implemented and used to calculate feasible low-thrust maneuvers. This thesis presents the theoretical foundations for seven modern low-thrust control laws based on control law "blending" and Lyapunov control theory for a particle spacecraft operating in an inverse-square gravitational field. The control laws are evaluated critically to determine those that present the best combinations of thoroughness of method and minimization of user input required. The three control laws judged to exhibit the most favorable characteristics are then compared quantitatively through three numerical simulations. The simulations demonstrate the effectiveness of feedback-driven control laws, but also reveal several situations in which the control laws may perform poorly or break down altogether due to either theoretical shortcomings or numerical difficulties. The causes and effects of these issues are explained, and methods of handling them are proposed, implemented, and evaluated. Various opportunities for further work in the area are also described. / text