Adaptive Discrete Second Order Sliding Mode Control with Application to Nonlinear Automotive Systems

Sliding mode control (SMC) is a robust and computationally efficient model-based controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. However, the implementation of the conventional continuous-time SMC on digital computers is limited, due to the imprecisions caused by data sampling and quantization, and the chattering phenomena, which results in high frequency oscillations. One effective solution to minimize the effects of data sampling and quantization imprecisions is the use of higher order sliding modes. To this end, in this paper, a new formulation of an adaptive second order discrete sliding mode control (DSMC) is presented for a general class of multi-input multi-output (MIMO) uncertain nonlinear systems. Based on a Lyapunov stability argument and by invoking the new Invariance Principle, not only the asymptotic stability of the controller is guaranteed, but also the adaptation law is derived to remove the uncertainties within the nonlinear plant dynamics. The proposed adaptive tracking controller is designed and tested in real-time for a highly nonlinear control problem in spark ignition combustion engine during transient operating conditions. The simulation and real-time processor-in-the-loop (PIL) test results show that the second order single-input single-output (SISO) DSMC can improve the tracking performances up to 90%, compared to a first order SISO DSMC under sampling and quantization imprecisions, in the presence of modeling uncertainties. Moreover, it is observed that by converting the engine SISO controllers to a MIMO structure, the overall controller performance can be enhanced by 25%, compared to the SISO second order DSMC, because of the dynamics coupling consideration within the MIMO DSMC formulation.Comment: 12 pages, 7 figures, 1 tabl

Discrete Adaptive Second Order Sliding Mode Controller Design with Application to Automotive Control Systems with Model Uncertainties

Sliding mode control (SMC) is a robust and computationally efficient solution for tracking control problems of highly nonlinear systems with a great deal of uncertainty. High frequency oscillations due to chattering phenomena and sensitivity to data sampling imprecisions limit the digital implementation of conventional first order continuous-time SMC. Higher order discrete SMC is an effective solution to reduce the chattering during the controller software implementation, and also overcome imprecisions due to data sampling. In this paper, a new adaptive second order discrete sliding mode control (DSMC) formulation is presented to mitigate data sampling imprecisions and uncertainties within the modeled plant's dynamics. The adaptation mechanism is derived based on a Lyapunov stability argument which guarantees asymptotic stability of the closed-loop system. The proposed controller is designed and tested on a highly nonlinear combustion engine tracking control problem. The simulation test results show that the second order DSMC can improve the tracking performance up to 80% compared to a first order DSMC under sampling and model uncertainties.Comment: 6 pages, 6 figures, 2017 American 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

Adaptive comfort control implemented model (ACCIM) for energy consumption predictions in dwellings under current and future climate conditions: A case study located in Spain

Currently, the knowledge of energy consumption in buildings of new and existing dwellings is essential to control and propose energy conservation measures. Most of the predictions of energy consumption in buildings are based on fixed values related to the internal thermal ambient and pre-established operation hypotheses, which do not reflect the dynamic use of buildings and users’ requirements. Spain is a clear example of such a situation. This study suggests the use of an adaptive thermal comfort model as a predictive method of energy consumption in the internal thermal ambient, as well as several operation hypotheses, and both conditions are combined in a simulation model: the Adaptive Comfort Control Implemented Model (ACCIM). The behavior of ACCIM is studied in a representative case of the residential building stock, which is located in three climate zones with different characteristics (warm, cold, and mild climates). The analyses were conducted both in current and future scenarios with the aim of knowing the advantages and limitations in each climate zone. The results show that the average consumption of the current, 2050, and 2080 scenarios decreased between 23% and 46% in warm climates, between 19% and 25% in mild climates, and between 10% and 29% in cold climates by using such a predictive method. It is also shown that this method is more resilient to climate change than the current standard. This research can be a starting point to understand users’ climate adaptation to predict energy consumption

Multiphysics Diesel Aftertreatment System Modeling for Reduced Emissions from Hybrid Electric Heavy-Duty Powertrains

Hybridization of heavy-duty on-road vehicles presents an opportunity to significantly reduce internal combustion engine emissions in real-world operation. These gains can be realized through the coordination of the electric drive, engine, and aftertreatment systems. Accurate Multiphysics models of all powertrains sub-systems are required to achieve the goal of reduced emissions. This research aims to develop a model of a highly complex diesel engine aftertreatment system. This study focuses on utilizing transient data for calibration and validation of the aftertreatment system and reducing the run time when compared to real-time experiments. The calibration focuses on two physical phenomena, thermal behavior and chemical kinetics. Once a base model is set up, the calibration parameters are optimized using an accelerated genetic algorithm for factors that contribute to the reaction rates and the exhaust gas temperature. The research only utilizes data from transient engine experiments to better automate and speed-up the calibration process over traditional methodologies. The model setup ensures that it is fast-running, with ten times speed-up as compared to real-time. The model is capable of predicting and matching combined error for and concentration on a cumulative basis under 9.8% and 1% for the experimental data for cold FTP and hot FTP, respectively. The results of the model also predict close trends with the temperature profiles and have a close match with the tailpipe emission species concentration over a cumulative basis but fails to capture some transient behavior. The model results are also evaluated to identify the leading cause for the error so the model can be improved for further development. The model has the capability to generate results for the aftertreatment for further research

Supervisory control of complex propulsion subsystems

Modern gasoline and diesel combustion engines are equipped with several subsystems with the goal to reduce fuel consumption and pollutant exhaust emissions. Subsystem synergies could be harnessed using the supervisory control approach. Look-ahead information can be used to potentially optimise power-train control for real time implementation. This thesis delves upon modelling the exhaust emissions from a combustion engine and developing a combined equivalent objective metric to propose a supervisory controller that uses look-ahead information with the objective to reduce fuel consumed and exhaust emissions. In the first part of the thesis, the focus is on diesel engine application control for emissions and fuel consumption reduction.\ua0Model of exhaust emissions in a diesel engine obtained from a combination of nominal engine operation and deviations are evaluated for transient drive cycles.\ua0The look ahead information as a trajectory of vehicle speed and load over time is considered.\ua0The supervisory controller considers a discrete control action set over the first segment of the trip ahead.\ua0The cost to optimise is defined and pre-computed off-line for a discrete set of operating conditions.\ua0A full factorial optimisation carried out off-line is stored on board the vehicle and applied in real-time.\ua0In a first proposal, the subsystem control of the after-treatment system comprising the lean NOx trap and the selective reduction catalyst is considered.\ua0As a next iteration, the combustion engine is added to the control problem.\ua0Simulation comparison of the controllers with the baseline controller offers a 1 % total fuel equivalent cost improvement while offering the flexibility to tailor the controller for different cost objective. In the second part of the thesis, the focus is on cold-start emissions control for modern gasoline engines.\ua0Emissions occurring when the engine is started until the catalyst is sufficiently warm, contribute to a significant proportion of tailpipe pollutant emissions.\ua0Electrically heated catalyst (EHC) in the three way catalyst (TWC) is a promising technology to reduce cold-start emissions where the catalyst can be warmed up prior to engine start and continued after start.\ua0A simulation framework for the engine, TWC with EHC with focus on modeling the thermal and chemical interactions during cold-start was developed.\ua0An evaluation framework with a proposed equivalent emissions approach was developed considering the challenges associated with cold-start emission control.\ua0An equivalent emission optimal post-heating time for the EHC is proposed that adapts to information which is available in a real-time on-line implementation.\ua0The proposed controller falls short of just 1 % equivalent emissions compared to the optimal case