MIMO First and Second Order Discrete Sliding Mode Controls of Uncertain Linear Systems under Implementation Imprecisions

The performance of a conventional model-based controller significantly depends on the accuracy of the modeled dynamics. The model of a plant's dynamics is subjected to errors in estimating the numerical values of the physical parameters, and variations over operating environment conditions and time. These errors and variations in the parameters of a model are the major sources of uncertainty within the controller structure. Digital implementation of controller software on an actual electronic control unit (ECU) introduces another layer of uncertainty at the controller inputs/outputs. The implementation uncertainties are mostly due to data sampling and quantization via the analog-to-digital conversion (ADC) unit. The failure to address the model and ADC uncertainties during the early stages of a controller design cycle results in a costly and time consuming verification and validation (V&V) process. In this paper, new formulations of the first and second order discrete sliding mode controllers (DSMC) are presented for a general class of uncertain linear systems. The knowledge of the ADC imprecisions is incorporated into the proposed DSMCs via an online ADC uncertainty prediction mechanism to improve the controller robustness characteristics. Moreover, the DSMCs are equipped with adaptation laws to remove two different types of modeling uncertainties (multiplicative and additive) from the parameters of the linear system model. The proposed adaptive DSMCs are evaluated on a DC motor speed control problem in real-time using a processor-in-the-loop (PIL) setup with an actual ECU. The results show that the proposed SISO and MIMO second order DSMCs improve the conventional SISO first order DSMC tracking performance by 69% and 84%, respectively. Moreover, the proposed adaptation mechanism is able to remove the uncertainties in the model by up to 90%.Comment: 10 pages, 11 figures, ASME 2017 Dynamic Systems and Control Conferenc

EASILY VERIFIABLE CONTROLLER DESIGN WITH APPLICATION TO AUTOMOTIVE POWERTRAINS

Bridging the gap between designed and implemented model-based controllers is a major challenge in the design cycle of industrial controllers. This gap is mainly created due to (i) digital implementation of controller software that introduces sampling and quantization imprecisions via analog-to-digital conversion (ADC), and (ii) uncertainties in the modeled plant’s dynamics, which directly propagate through the controller structure. The failure to identify and handle these implementation and model uncertainties results in undesirable controller performance and costly iterative loops for completing the controller verification and validation (V&V) process. This PhD dissertation develops a novel theoretical framework to design controllers that are robust to implementation imprecision and uncertainties within the models. The proposed control framework is generic and applicable to a wide range of nonlinear control systems. The final outcome from this study is an uncertainty/imprecisions adaptive, easily verifiable, and robust control theory framework that minimizes V&V iterations in the design of complex nonlinear control systems. The concept of sliding mode controls (SMC) is used in this study as the baseline to construct an easily verifiable model-based controller design framework. SMC is a robust and computationally efficient controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. The SMC structure allows for further modification to improve the controller robustness against implementation imprecisions, and compensate for the uncertainties within the plant model. First, the conventional continuous-time SMC design is improved by: (i) developing a reduced-order controller based on a novel model order reduction technique. The reduced order SMC shows better performance, since it uses a balanced realization form of the plant model and reduces the destructive internal interaction among different states of the system. (ii) developing an uncertainty-adaptive SMC with improved robustness against implementation imprecisions. Second, the continuous-time SMC design is converted to a discrete-time SMC (DSMC). The baseline first order DSMC structure is improved by: (i) inclusion of the ADC imprecisions knowledge via a generic sampling and quantization uncertainty prediction mechanism which enables higher robustness against implementation imprecisions, (ii) deriving the adaptation laws via a Lyapunov stability analysis to overcome uncertainties within the plant model, and (iii) developing a second order adaptive DSMC with predicted ADC imprecisions, which provides faster and more robust performance under modeling and implementation imprecisions, in comparison with the first order DSMC. The developed control theories from this PhD dissertation have been evaluated in real-time for two automotive powertrain case studies, including highly nonlinear combustion engine, and linear DC motor control problems. Moreover, the DSMC with predicted ADC imprecisions is experimentally tested and verified on an electronic air throttle body testbed for model-based position tracking purpose

Development and characterisation of error functions in design

As simulation is increasingly used in product development, there is a need to better characterise the errors inherent in simulation techniques by comparing such techniques with evidence from experiment, test and inservice. This is necessary to allow judgement of the adequacy of simulations in place of physical tests and to identify situations where further data collection and experimentation need to be expended. This paper discusses a framework for uncertainty characterisation based on the management of design knowledge leading to the development and characterisation of error functions. A classification is devised in the framework to identify the most appropriate method for the representation of error, including probability theory, interval analysis and Fuzzy set theory. The development is demonstrated with two case studies to justify rationale of the framework. Such formal knowledge management of design simulation processes can facilitate utilisation of cumulated design knowledge as companies migrate from testing to simulation-based design

Quantifying the use of chloroform vapor exposure to improve the adhesion of Au Thin films onto PMMA

The metallization of Au onto plastics is an important processing step in applications such as the aerospace and automotive industries, the field of microelectronics, and the fabrication of microfluidic devices. While its corrosion resistance and excellent electrical and thermal conductivity make Au a useful choice, its inertness results in poor adhesion to polymer surfaces. Previous studies have indicated that exposing commercially available poly(methyl methacrylate) (PMMA) sheets to chloroform vapor following Au deposition significantly improves adhesion. In this study, we utilized electron-beam evaporation and magnetron sputtering to deposit Au thin films onto 1.50 mm thick PMMA and exposed the samples to vapor released from chloroform heated on a hot plate set at 70 °C. The force required to remove both treated and untreated Au thin films was determined by placing samples on a polisher spinning at 150 rpm and utilizing UV-VIS spectroscopy to measure the absorbance of light through the films to quantify their removal as a function of applied polishing force. The pressure required to polish Au from PMMA exposed to chloroform (CHCl3) after metal deposition was compared to the pressure required for pre-treated samples. Post-treated Au thin films were characterized during the polishing process using atomic force microscopy (AFM). AFM images demonstrated a progressive roughening of the surface corresponding to an increase in applied force. Additionally, these images support a model in which the chloroform treatment softens the PMMA surface, producing a softened layer that the polisher removes simultaneously with the Au thin film. The chloroform post-treatment procedure was then used to selectively pattern a series of PMMA samples

Furthering Service 4.0: Harnessing Intelligent Immersive Environments and Systems

With the increasing complexity of service operations in different industries and more advanced uses of specialized equipment and procedures, the great current challenge for companies is to increase employees' expertise and their ability to maintain and improve service quality. In this regard, Service 4.0 aims to support and promote innovation in service operations using emergent technology. Current technological innovations present a significant opportunity to provide on-site, real-time support for field service professionals in many areas

Accounting for Change: Assessing Top-line Implications of New Revenue Recognition Principles

The impending implementation of new FASB guidance regarding the practice of revenue recognition will presumably alter the periodic presentation of top-line business performance. In anticipation of these impacts, this study seeks to isolate contractual business relationships within the automotive supply chain industry in order to illuminate certain changes and make financial statement users aware that corresponding adjustments may have to be made to their perception of revenue results. By outlining the differences between new and historical U.S. GAAP, and applying the anticipated quantitative effects of such shifts within a propositional study, I seek to produce conclusions that investors and analysts can use to better interpret current and future revenue data. Using historical company figures as a basis, incremental influences are applied to disaggregated portions of contract revenue, and final revenue figures are reconstructed to reflect the implications of new accounting guidance. This study displays the potential relative movement of these periodic revenue results as businesses transition away from their established accounting practices and into a new recognition model

Variational Analysis for CNC Milling Process

Abstract Dimensional and geometrical defects severely affect the quality of chip removal processes in CNC machines. In order to improve the accuracy, a long and expensive calibration process is usually performed on the CNC machine. The tuning process could be easier and faster if the designer is able to evaluate the effect of error sources providing a more robust and reliable CNC machine. The joined use of variational analysis and finite element analysis of geometrical features forecasts the position error of the tool tip

Small Unmanned Aircraft Systems for Project-Based Engineering Education

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143092/1/6.2017-1377.pd