Mario Anthony Santillo

Model Predictive Controller design for throttle and wastegate control of a turbocharged engine

In this paper, we consider the problem of turbocharged gasoline engine air-path control. Specifically, we apply linear Model Predictive Control (MPC) to coordinate throttle and turbocharger wastegate actuation for engine airflow and boost pressure control. Simplification of the prediction model used for the MPC reduces the memory requirement for implementation. We neglect the effects of variable cam timing in the prediction model, and instead, these effects are considered through a robustness analysis of the MPC to system variability. We compare two methods to achieve offset-free reference tracking, namely, the use of an integrator with actuator-saturation-based anti-windup logic, and the use of a Kalman filter to estimate plant-model mismatches. Evaluation of these methods for a vehicle acceleration scenario demonstrates advantages with using the Kalman-filter-based approach in the presence of system variability.

Composite Adaptive Internal Model Control and Its Application to Boost Pressure Control of a Turbocharged Gasoline Engine

Internal model control (IMC) explicitly incorporates the plant model and its approximate inverse and offers an intuitive controller structure and calibration procedure. In the presence of plant-model uncertainty, combining the IMC structure with parameter estimation through the certainty equivalence principle leads to adaptive IMC (AIMC), where either the plant model or its inverse is identified. This paper proposes a composite AIMC (CAIMC) that explores the IMC structure and simultaneous plant dynamics and inverse dynamics identification to achieve improved performance of AIMC. A toy plant is used to illustrate the feasibility and potential of CAIMC. The advantages of CAIMC are later demonstrated on the boost-pressure control problem of a turbocharged gasoline engine. The design of the CAIMC assumes that the plant model and its inverse are represented by the first-order linear dynamics. The unmodeled dynamics and uncertainties due to linearization and variations in operating conditions are compensated through adaptation. The resulting CAIMC is first applied to a physics-based high-order and nonlinear proprietary turbocharged gasoline engine model, and then validated on a turbocharged 2-L four-cylinder gasoline engine on a vehicle with vacuum-actuated wastegate. Both the simulation and experimental results show that the CAIMC cannot only effectively compensate for uncertainties but also auto-tune the IMC controller for the best performance.

Nonlinear internal model controller design for wastegate control of a turbocharged gasoline engine

This work investigates the design of nonlinear internal model control (IMC) for wastegate control of a turbocharged gasoline engine. We extend the inverse-based IMC design for linear time-invariant (LTI) systems to nonlinear systems. To leverage the available tools for LTI IMC deign, we have explored the quasi linear parameter varying (quasi-LPV) model. IMC design through transfer function inverse of the quasi-LPV model is ruled out due to parameter variability. A new approach for nonlinear inverse, 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 implemented to serve as the model in the IMC structure. The controller based on structured quasi-LPV model inverse is designed to achieve boost pressure tracking. Finally, simulations on a validated high fidelity model are carried out to show the feasibility of the IMC. Its closed-loop performance and robustness are compared with a well-tuned PI controller with extensive feedforward and anti-windup built in.

Towards ECU-Executable Control-Oriented Models of a Three-Way Catalytic Converter

The nonlinear dynamics of an automotive three-way catalyst (TWC) present a challenge to developing simple control-oriented models that are both useful for control and/or diagnostics and real-time executable within a vehicle engine-control unit (ECU). As such, we begin by developing a first-principles control-oriented TWC model and then proceed to apply simplifications. The TWC models are spatially discretized along the catalyst length to better understand and exploit the oxygen-storage dynamics. The TWC models also include the oxidation reaction of ceria by H2O, which is considered important since it represents the production of H2 within the catalyst. We present automated optimization routines to calibrate the TWC model along with a heated exhaust-gas oxygen (HEGO) sensor model using measured vehicle and emissions data. Finally, we demonstrate the combined models’ ability to accurately reproduce the measured HEGO voltage using engine feedgas constituent inputs, which is necessary for designing a robust model-based feedback controller.Copyright

Enhanced composite adaptive IMC for boost pressure control of a turbocharged gasoline engine

Internal model control (IMC) offers an intuitive control structure and simple tuning philosophy, which makes it appealing to industrial applications. In our recent work [1], we proposed composite adaptive IMC (CAIMC) which simultaneously identified the model and the inverse in the IMC structure. It demonstrates good performance and auto-tuning capability in simulations and experiments. In this paper, an innovative approach is taken to express the IMC tracking error in terms of the modeling error and right-inverse modeling error, which analytically justifies the CAIMC design and its auto-tuning property. This new expression of the tracking error also suggests reformulation of the inverse model estimation problem, leading to enhanced CAIMC performance. The CAIMC from [1] and the enhanced CAIMC with the reformulated inverse are presented and compared, and applied to the boost-pressure control problem of a turbocharged gasoline engine. Results from simulations on a proprietary model and experiments on a vehicle with a turbocharged 2:0L engine are presented to demonstrate the advantages of the enhanced CAIMC.

Mid-ranging control for an automotive three-way catalyst outer loop

As government regulations force stringent automobile emission standards with graceful degradation under various hardware faults and operating environments, accurate feedback control of the engine air-to-fuel ratio has become a key enabler. We present an overview of the automotive three-way catalyst (TWC) control system and discuss challenges associated with development and implementation of a successful feedback controller, including plant and sensor nonlinearities - in use, as well as regulatory On-Board-Diagnostics induced faults, bandwidth restrictions, and constraints such as drivability and noise, vibration, and harshness (NVH) considerations. To meet these goals, we modify the conventional mid-ranging control structure for accurate control of the TWC subsystem while simultaneously adhering to drivability and NVH constraints. Vehicle test results are shown exhibiting the controllers capabilities with and without induced faults.

Generalized composite adaptive IMC: Design and analysis

Internal model control (IMC), which explicitly incorporates a plant model and a plant inverse as its components, has an intuitive control structure and simple tuning philosophy, making it appealing to industrial applications. Combining the IMC structure with adaptation 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 estimated model. In [1], [2], we proposed the composite adaptive IMC (CAIMC) for a first-order plant and successfully applied it to the boost-pressure control problem of a turbocharged gasoline engine system. Within the IMC control structure, the plant model and the plant inverse are simultaneously identified to minimize modeling errors and further reduce the tracking error. Through theoretical analysis, simulations, and experimental validation, CAIMC was shown to demonstrate better performance compared to AIMC. In this paper the design procedure of CAIMC is generalized to a n-th order plant, and stability and asymptotic performance are established and analyzed under proper conditions.

Adaptation for Air-Intake System Throttle Control in a Gasoline Engine With Low-Pressure Exhaust-Gas Recirculation

We investigate control strategies for traditional throttle-in-bore as well as low-cost cartridge-style throttle bodies for the air-intake system (AIS) throttle used in low-pressure exhaust-gas recirculation (LPEGR) on a turbocharged gasoline engine. Pressure sensors placed upstream and downstream of the AIS throttle are available as signals from the vehicle’s engine control unit, however, we do not use high-bandwidth feedback control of the AIS throttle in order to maintain frequency separation from the higher-rate EGR loop, which uses the downstream pressure sensor for feedback control. A design-of-experiments conducted using a feed-forward lookup table-based AIS throttle control strategy exposes controller sensitivity to part-to-part variations. For accurate tracking in the presence of these variations, we explore the use of adaptive feedback control. In particular, we use an algebraic model representing the throttle plate effective opening area to develop a recursive least-squares (RLS)-based estimation routine. A low-pass filtered version of the estimated model parameters is subsequently used in the forward-path AIS throttle controller. Results are presented comparing the RLS-based feedback algorithm with the feed-forward lookup table-based control strategy. RLS is able to adapt for part-to-part and change-over-time variabilities and exhibits an improved steady-state tracking response compared to the feed-forward control strategy.Copyright