Robust control of parameter‐dependent input delay linear parameter‐varying (LPV) systems via gain‐scheduled dynamic output‐feedback control is considered in this paper. The controller is designed to provide disturbance rejection in the context of the induced
Physics-based linear model predictive control strategy for three-way catalyst air/fuel ratio control
The Current practice of air-fuel ratio control relies on empirical models and traditional PID controllers, which require extensive calibration to maintain the post-catalyst air-fuel ratio close to stoichiometry. In contrast, this work utilizes a physics-based Three-Way Catalyst (TWC) model to develop a model predictive control (MPC) strategy for air-fuel ratio control based on internal TWC oxygen storage dynamics. In this paper, parameters of the physics-based temperature and oxygen storage models of the TWC are identified using vehicle test data for a catalyst aged to 150,000 miles. A linearized oxygen storage model is then developed from the identified nonlinear model, which is shown via simulation to follow the nonlinear model with minimal error during nominal operation. This motivates the development of a Linear MPC (LMPC) framework using the linearized TWC oxygen storage model, reducing the requisite computational effort relative to a nonlinear MPC strategy. In this work, the LMPC utilizing a linearized physics-based TWC model is proven suitable for tracking a desired oxygen storage level by controlling the commanded engine air-fuel ratio, which is also a novel contribution. The offline simulation results show successful tracking performance of the developed LMPC framework.
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- Award ID(s):
- 1839050
- NSF-PAR ID:
- 10268673
- Date Published:
- Journal Name:
- Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
- ISSN:
- 0954-4070
- Page Range / eLocation ID:
- 095440702110212
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary ‐norm or the norm of the closed‐loop system in the presence of uncertainty and disturbances. A reciprocally convex approach is employed to bound the Lyapunov‐Krasovskii functional derivative and extract sufficient conditions for the controller characterization in terms of linear matrix inequalities (LMIs). The approach does not require the rate of the delay to be bounded, hence encompasses a broader family of input‐delay LPV systems with fast‐varying delays. The method is then applied to the air‐fuel ratio (AFR) control in spark ignition (SI) engines where the delay and the plant parameters are functions of the engine speed and mass air flow. The objectives are to track the commanded AFR signal and to optimize the performance of the three‐way catalytic converter (TWC) through the precise AFR control and oxygen level regulation, resulting in improved fuel efficiency and reduced emissions. The designed AFR controller seeks to provide canister purge disturbance rejection over the full operating envelope of the SI engine in the presence of uncertainties. Closed‐loop simulation results are presented to validate the controller performance and robustness while meeting AFR tracking and disturbance rejection requirements. -
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Abstract Rapid pressure swing adsorption (RPSA) is a gas separation technology used in the small‐scale oxygen concentrator devices. These devices are commonly used to produce high purity (~90%) oxygen from air for oxygen rehabilitation therapy, but can also produce a much wider range of oxygen purities for other applications. RPSA is a complex, cyclic, nonlinear switched logic process resulting from the coupling of gas adsorption, heat transfer, flow reversal effects, and process logic switches. For RPSA devices to operate satisfactorily, feedback control is critical but challenging due to their inherent complexity. In this article, we present a piecewise linear model predictive control framework for operation and control of a single‐bed RPSA system. A set of coupled, nonlinear partial differential equations with flow switching conditions is used as a plant model for the RPSA process. Subspace system identification with pseudo‐random binary sequence signals applied to this plant model at multiple operating points is used to generate a family of piecewise linear models for use in the model predictive controller algorithm. Detailed descriptions of the RPSA plant model, the multiple linear model identification procedure, the controller formulation and model switching logic are presented. The closed‐loop system is evaluated in simulation using several realistic set point tracking and disturbance rejection cases.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/09544070211021207
