117 research outputs found
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
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
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
Integration and optimal control of microcsp with building hvac systems: Review and future directions
Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost
Reactivity controlled compression ignition engine: Pathways towards commercial viability
© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/).Reactivity-controlled compression ignition (RCCI) is a promising energy conversion strategy to increase fuel efficiency and reduce nitrogen oxide (NOx) and soot emissions through improved in-cylinder combustion process. Considering the significant amount of conducted research and development on RCCI concept, the majority of the work has been performed under steady-state conditions. However, most thermal propulsion systems in transportation applications require operation under transient conditions. In the RCCI concept, it is crucial to investigate transient behavior over entire load conditions in order to minimize the engine-out emissions and meet new real driving emissions (RDE) legislation. This would help further close the gap between steady-state and transient operation in order to implement the RCCI concept into mass production. This work provides a comprehensive review of the performance and emissions analyses of the RCCI engines with the consideration of transient effects and vehicular applications. For this purpose, various simulation and experimental studies have been reviewed implementing different control strategies like control-oriented models particularly in dual-mode operating conditions. In addition, the application of the RCCI strategy in hybrid electric vehicle platforms using renewable fuels is also discussed. The discussion of the present review paper provides important insights for future research on the RCCI concept as a commercially viable energy conversion strategy for automotive applications.Peer reviewe
Experimental Studies of Low-Load Limit in a Stoichiometric Micro-Pilot Diesel Natural Gas Engine
While operating at light loads, diesel pilot-ignited natural gas engines with lean pre-mixed natural gas suffer from poor combustion efficiency and high methane emissions. This work investigates the limits of low-load operation for a micro-pilot diesel natural gas engine that uses a stoichiometric mixture to enable methane and nitrogen oxide emission control. By optimizing engine hardware, operating conditions, and injection strategies, this study focused on defining the lowest achievable load while maintaining a stoichiometric equivalence ratio and with acceptable combustion stability. A multi-cylinder diesel 6.7 L engine was converted to run natural gas premix with a maximum diesel micro-pilot contribution of 10%. With a base diesel compression ratio of 17.3:1, the intake manifold pressure limit was 80 kPa (absolute). At a reduced compression ratio of 15:1, this limit increased to 85 kPa, raising the minimum stable load. Retarding the combustion phasing, typically used in spark-ignition engines to achieve lower loads, was also tested but found to be limited by degraded diesel ignition at later timings. Reducing the pilot injection pressure improved combustion stability, as did increasing pilot quantity at the cost of lower substitution ratios. The lean operation further reduced load but increased NOx and hydrocarbon emissions. At loads below the practical dual-fuel limit, a transition to lean diesel operation will likely be required with corresponding implications for the aftertreatment system
Machine Learning-based Classification of Combustion Events in an RCCI Engine Using Heat Release Rate Shapes
Reactivity controlled compression ignition (RCCI) mode offers high thermal efficiency and low nitrogen oxides (NOx) and soot emissions. However, high cyclic variability at low engine load and high pressure rise rates at high loads limit RCCI operation. Therefore, it is important to control the combustion event in an RCCI engines to prevent abnormal engine combustion. To this end, combustion in RCCI mode was studied by analyzing the heat release rates calculated from the in-cylinder pressure data at 798 different operating conditions. Five distinct heat release shapes are identified. These different heat release traces were characterized based on start of combustion, burn duration, combustion phasing, maximum pressure rise rate, maximum amount of heat release, maximum in-cylinder gas temperature and pressure. Both supervised and unsupervised machine learning approaches are used to classify different types of heat release rates. K-means clustering, an unsupervised algorithm, could not cluster the heat release traces distinctly. Convolution neural network (CNN) and decision trees, supervised classification algorithms, were designed to classify the heat release rates. The CNN algorithm showed 70% accuracy in predicting the shapes of heat release rates while decision tree resulted in 74.5% accuracy in predicting different heat release rate traces
Deep Learning based Model Predictive Control for Compression Ignition Engines
Machine learning (ML) and a nonlinear model predictive controller (NMPC) are
used in this paper to minimize the emissions and fuel consumption of a
compression ignition engine. In this work machine learning is applied in two
methods. In the first application, ML is used to identify a model for
implementation in model predictive control optimization problems. In the second
application, ML is used as a replacement of the NMPC where the ML controller
learns the optimal control action by imitating or mimicking the behavior of the
model predictive controller. In this study, a deep recurrent neural network
including long-short term memory (LSTM) layers are used to model the emissions
and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine.
This model is then used for model predictive controller implementation. Then, a
deep learning scheme is deployed to clone the behavior of the developed
controller. In the LSTM integration, a novel scheme is used by augmenting
hidden and cell states of the network in an NMPC optimization problem. The
developed LSTM-NMPC and the imitative NMPC are compared with the Cummins
calibrated Engine Control Unit (ECU) model in an experimentally validated
engine simulation platform. Results show a significant reduction in Nitrogen
Oxides (\nox) emissions and a slight decrease in the injected fuel quantity
while maintaining the same load. In addition, the imitative NMPC has a similar
performance as the NMPC but with a two orders of magnitude reduction of the
computation time.Comment: Submitted to Control engineering Practice (Submission date: March 9,
2022) Revised version (Submission date: June 18, 2022) Accepted on July 30,
202
Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines
The high thermal efficiency and reliability of the compression-ignition
engine makes it the first choice for many applications. For this to continue, a
reduction of the pollutant emissions is needed. One solution is the use of
machine learning (ML) and model predictive control (MPC) to minimize emissions
and fuel consumption, without adding substantial computational cost to the
engine controller. ML is developed in this paper for both modeling engine
performance and emissions and for imitating the behaviour of an Linear
Parameter Varying (LPV) MPC. Using a support vector machine-based linear
parameter varying model of the engine performance and emissions, a model
predictive controller is implemented for a 4.5 Cummins diesel engine. This
online optimized MPC solution offers advantages in minimizing the
\nox~emissions and fuel consumption compared to the baseline feedforward
production controller. To reduce the computational cost of this MPC, a deep
learning scheme is designed to mimic the behavior of the developed controller.
The performance in reducing NOx emissions at a constant load by the imitative
controller is similar to that of the online optimized MPC compared to the
Cummins production controller. In addition, the imitative controller requires
50 times less computation time compared to that of the online MPC optimization.Comment: Submitted to Advances in Automotive Control - 10th AAC 202
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