Quantification of Linear Approximation Error for Model Predictive Control of Spark-Ignited Turbocharged Engines

Koli, Rohit; Egan, Daniel; Zhu, Qilun; Prucka, Robert

doi:10.4271/2019-24-0014

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…The three main MPC methods that have been implemented on ICEs are Linear, Economic, and Nonlinear MPC as shown in Table 1. Due to the limited computational capability of engine ECU, linear methods, such as Explicit MPC [30,[38][39][40]43,45,47,50], single MPC [24,28,36,37,40,49,108,142,145,151], switching MPC [27,29,33,46,53,147,149], and linear parameter varying (LPV) MPC [21,22,29,42,48,129,143,150], have been widely implemented in ICEs.…”

Section: Methods Of Mpc In Icesmentioning

confidence: 99%

“…MPC applications are not limited to a specific engine type, and, due to the capability of MPC, they have been implemented in various types of engines. MPC has been implemented for different ICE combustion modes, including Compression Ignition Combustion (CI) [26,[28][29][30][31]34,35,40,45,[47][48][49][50]53,135,137,139,[141][142][143]145,147,148,150], Spark Ignition (SI) [25,33,36,[41][42][43]51,52,54,136,138,140,144], and Low Temperature Combustion (LTC) [21,22,27,[37][38][39]108,129,130].…”

Section: Mpc Applications In Icesmentioning

confidence: 99%

“…Most studies focused on the topology I-level 2A, i.e., optimal tracking controllers, and these studies assumed that the optimal reference values were given. Implementation of L2A of topology I has been done both in simulation [22,25,108,129,130,[136][137][138][139][140][141][142][143][144][145][147][148][149][150][151] and in real-time experimental platforms [21,.…”

Section: Topology/structurementioning

confidence: 99%

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Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

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Section: Methods Of Mpc In Icesmentioning

confidence: 99%

Section: Mpc Applications In Icesmentioning

confidence: 99%

Section: Topology/structurementioning

confidence: 99%

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Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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“…This is undesirable because using a complex model to capture all relevant dynamics can restrict the length of the receding horizon to a point where MPC may not be able to optimize all of the desired dynamics directly. While methods to increase the computational tractability of COMs used by MPC have been proposed [11][12][13], they do not address the need of MPC to model system performance at every time step in the receding horizon. Thus, MPC can be formulated to address control of complex systems over short receding horizons or simple systems over long horizons, but not both at once.…”

mentioning

confidence: 99%

A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation

2023

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One major cost of improving the automotive fuel economy while simultaneously reducing tailpipe emissions is increased powertrain complexity. This complexity has consequently increased the resources (both time and money) needed to develop such powertrains. Powertrain performance is heavily influenced by the quality of the controller/calibration. Since traditional control development processes are becoming resource-intensive, better alternate methods are worth pursuing. Recently, reinforcement learning (RL), a machine learning technique, has proven capable of creating optimal controllers for complex systems. The model-free nature of RL has the potential to streamline the control development process, possibly reducing the time and money required. This article reviews the impact of choices in two areas on the performance of RL-based powertrain controllers to provide a better awareness of their benefits and consequences. First, we examine how RL algorithm action continuities and control–actuator continuities are matched, via native operation or conversion. Secondly, we discuss the formulation of the reward function. RL is able to optimize control policies defined by a wide spectrum of reward functions, including some functions that are difficult to implement with other techniques. RL action and control–actuator continuity matching affects the ability of the RL-based controller to understand and operate the powertrain while the reward function defines optimal behavior. Finally, opportunities for future RL-based powertrain control development are identified and discussed.

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Development of an integrated energy and thermal planner for a series hybrid off-road autonomous tracked vehicle

Ghate,

Sundar,

Zhu

et al. 2024

Energy Conversion and Management

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Quantification of Linear Approximation Error for Model Predictive Control of Spark-Ignited Turbocharged Engines

Cited by 4 publications

References 19 publications

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation

Development of an integrated energy and thermal planner for a series hybrid off-road autonomous tracked vehicle

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