Robust Feedback Linearization Approach for Fuel-Optimal Oriented Control of Turbocharged Spark-Ignition Engines

This chapter proposes a new control approach for the turbocharged air system of a gasoline engine. To simplify the control implementation task, static lookup tables (LUTs) of engine data are used to estimate the engine variables in place of complex dynamical observer and/or estimators. The nonlinear control design is based on the concept of robust feedback linearization which can account for the modeling uncertainty and the estimation errors induced by the use of engine lookup tables. The control feedback gain can be effectively computed from a convex optimization problem. Two control strategies have been investigated for this complex system: drivability optimization and fuel reduction. The effectiveness of the proposed control approach is clearly demonstrated with an advanced engine simulator

Explicit-Ready Nonlinear Model Predictive Control for Turbocharged Spark-Ignited Engine

International audienceThe trend to reduce the engine size in automotive industry is motivated by more restrictive pollutant emissions standards. That is why engine technical definitions have become more and more complicated. The control challenge has also grown since engines are now considered as highly nonlinear multi-input multi-output systems with saturated actuators. In this context, the need for model-based control laws is bigger than ever. In this study we propose a nonlinear model predictive control strategy based on a physical engine model. Moreover, we also underline the benefit of using a thermodynamic engine term in the objective function. Finally, the design and calibration choices consciously fulfill the criterions of the use of an explicit approach for the real time implementation

Nonlinear Model Predictive Control of the Air Path of a Turbocharged Gasoline Engine Using Laguerre Functions

International audienceObjectives in terms of pollutant emissions and fuel consumption reduction have led car manufacturers to enhance the technical definitions of combustion engines. The latter should now be considered as multiple-input multiple-output nonlinear systems with saturated actuators. This considerably increases the challenge regarding the development of optimal control laws under the constraints of constant cost reductions in the automotive industry. In the present paper, the use of a nonlinear model predictive control (NMPC) scheme is studied for the air path control of a turbocharged gasoline engine. Specifically, a zero dimension physics-based model is combined with parameterization of the future control trajectory. The use of Laguerre polynomials is shown to increase flexibility for the future control trajectory at no cost in computational requirements. This increase in flexibility leads to an improvement of the transient response of the closed-loop with respect to traditional approaches. This practical application shows that this approach makes it easier to fine-tune the NMPC scheme when dealing with engine air path control

Automotive Powertrain Control — A Survey

This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72023/1/j.1934-6093.2006.tb00275.x.pd

A STUDY OF MODEL-BASED CONTROL STRATEGY FOR A GASOLINE TURBOCHARGED DIRECT INJECTION SPARK IGNITED ENGINE

To meet increasingly stringent fuel economy and emissions legislation, more advanced technologies have been added to spark-ignition (SI) engines, thus exponentially increase the complexity and calibration work of traditional map-based engine control. To achieve better engine performance without introducing significant calibration efforts and make the developed control system easily adapt to future engines upgrades and designs, this research proposes a model-based optimal control system for cycle-by-cycle Gasoline Turbocharged Direct Injection (GTDI) SI engine control, which aims to deliver the requested torque output and operate the engine to achieve the best achievable fuel economy and minimum emission under wide range of engine operating conditions. This research develops a model-based ignition timing prediction strategy for combustion phasing (crank angle of fifty percent of the fuel burned, CA50) control. A control-oriented combustion model is developed to predict burn duration from ignition timing to CA50. Using the predicted burn duration, the ignition timing needed for the upcoming cycle to track optimal target CA50 is calculated by a dynamic ignition timing prediction algorithm. A Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is proposed to handle operating-point-dependent model errors caused by inherent errors resulting from modeling assumptions and limited calibration points, which helps to ensure the proper performance of model-based ignition timing prediction strategy throughout the entire engine lifetime. Using the adaptive combustion model, an Adaptive Extended Kalman Filter (AEKF) based CA50 observer is developed to provide filtered CA50 estimation from cyclic variations for the closed-loop combustion phasing control. An economic nonlinear model predictive controller (E-NMPC) based GTDI SI engine control system is developed to simultaneously achieve three objectives: tracking the requested net indicated mean effective pressure (IMEPn), minimizing the SFC, and reducing NOx emissions. The developed E-NMPC engine control system can achieve the above objectives by controlling throttle position, IVC timing, CA50, exhaust valve opening (EVO) timing, and wastegate position at the same time without violating engine operating constraints. A control-oriented engine model is developed and integrated into the E-NMPC to predict future engine behaviors. A high-fidelity 1-D GT-POWER engine model is developed and used as the plant model to tune and validate the developed control system. The performance of the entire model-based engine control system is examined through the software-in-the-loop (SIL) simulation using on-road vehicle test data

Composite Adaptive Internal Model Control: Theory and Applications to Engine Control

To meet customer demands for vehicle performance and to satisfy increasingly stringent emission standard, powertrain control strategies have become more complex and sophisticated. As a result, controller development and calibration have presented a time-consuming and costly challenge to the automotive industry. This thesis aims to develop new control methodologies with reduced calibration effort. Internal model control (IMC) lends itself to automotive applications for its intuitive control structure with simple tuning philosophy. A few applications of IMC to the boost-pressure control problem have been reported, however, none offered an implementable and easy-to-calibrate solution. Motivated by the need to develop robust and easily calibratable control technologies for boost-pressure control of turbocharged gasoline engines, this thesis developed new control design methodologies in the IMC framework. Two directions are pursued: adaptive IMC (AIMC) and nonlinear IMC. A plant model and a plant inverse are explicit components of IMC. In the presence of plant-model uncertainty, combining the IMC structure with parameter identification through the certainty equivalence principle leads to adaptive IMC (AIMC), where the plant model is identified and the plant inverse is derived by inverting the model. We propose the composite AIMC (CAIMC), which identifies the model and the inverse in parallel, and reduces the tracking error through the online identification. ``Composite" refers to the simultaneous identifications. The constraint imposed by the stability of an n-th order model is nonconvex, and it is re-parameterized as a linear matrix inequality. The parameter identification problem with the stability constraint is reformulated as a convex programming problem. Stability proof and asymptotic performance are established for CAIMC of a general n-th order plant. CAIMC is applied to the boost-pressure control problem of a turbocharged gasoline engine. It is first validated on a physics-based high-order and nonlinear proprietary turbocharged gasoline engine Simulink model, and then validated on a turbocharged 2L four-cylinder gasoline engine on a Ford Explorer EcoBoost. Both simulations and experiments show that CAIMC is not only effective, but also drastically reduces the calibration effort compared to the traditional PI controller with feedforward. Nonlinear IMC is presented in the context of the boost-pressure control of a turbocharged gasoline engine. To leverage the available tools for linear IMC design, the quasi-linear parameter varying (quasi-LPV) models are explored. A new approach for nonlinear inversion, referred to as the structured quasi-LPV model inverse, is developed and validated. A fourth-order nonlinear model which sufficiently describes the dynamic behavior of the turbocharged engine is used as the design model, and the IMC controller is derived based on the structured quasi-LPV model inverse. The nonlinear IMC is applicable when the nonlinear system has a special structural property and has not been generalized yet. Simulations on a high-fidelity turbocharged engine model are carried out to show the feasibility of the proposed nonlinear IMC.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136978/1/connieqz_1.pd

Explicit Nonlinear Model Predictive Control of the Air Path of a Turbocharged Spark-Ignited Engine

International audiencePollutant emissions and fuel economy objectives have led car manufacturers to develop innovative and more sophisticated engine layouts. In order to reduce time-to-market and development costs, recent research has investigated the idea of a quasi-systematic engine control development approach. Model based approaches might not be the only possibility but they are clearly predetermined to considerably reduce test bench tuning work requirements. In this paper, we present the synthesis of a physics-based nonlinear model predictive control law especially designed for powertrain control. A binary search tree is used to ensure real-time implementation of the explicit form of the control law, computed by solving the associated multi-parametric nonlinear problem

Air Charge Control for Turbocharged Spark Ignition Engines with Internal Exhaust Gas Recirculation

Abstract-This paper presents the design of transient cylinder charge control, based on a cycle-averaged mean-value model for a turbocharged spark ignition direct injection engine equipped with dual-independent intake and exhaust variable camshafts which in this paper has been constrained to variable valve overlap with symmetric cam motions. The controloriented model, parameterized using dynamometer measurements, is shown to capture engine static and dynamic behavior of throttled conditions. The transient effects of throttling and variable valve timing on the cylinder charge over part-load and lightly boosted conditions are first analyzed to investigate the dynamic interactions between the electronic throttle and the valve overlap through variable camshafts. Given the fast dynamics of the electronic throttle actuator, a nonlinear feedforward and feedback throttle compensator, in reference to its static set-points, is employed here to improve the transient response of cylinder charge. It has been shown in simulation results that the combined use of both compensators can considerably improve transient engine performance

The Incorruptible Integrator: A Streamlined Approach to IMC-PID Controller Tuning

In automakers\u27 never-ending quest to reduce emissions and improve performance, the turbocharger represents a major step in advancing these goals. By repurposing waste exhaust and compressing the air intake, they are able to increase overall power. One critical control loop in the turbocharger is control of boost pressure via the wastegate. This is a highly nonlinear process and experimental data has shown that a gain-scheduled PID (proportional integral derivative) controller developed with IMC (internal model control) tuning methodology is an effective means to control boost pressure. Motivated by this successful implementation of IMC-PID tuning in the automotive world, this work hopes to extend and analyze that framework. Traditionally, the success of an IMC controller depends on the accuracy of the plant model. This research challenges this view and investigates using IMC with a gain-integrator-delay (GID) model identified at a critical frequency, regardless of the actual plant. The GID model is useful because of its simplicity to characterize and its ability to be translated to the ubiquitous PID controller easily. Three design techniques are developed: (1) design for post-hoc tuning, (2) design for closed loop bandwidth, and (3) design for phase margin. In addition, these techniques are investigated via a Monte Carlo simulation to determine efficacy for when there exists plant/model mismatch. Finally, the three techniques are applied to control the speed of an inertia disk on the Quanser Servo 2 device