Engine Calibration Using Global Optimization Methods with Customization

Zhu, Ling; Wang, Yan; Pal, Anuj; Zhu, Guoming

doi:10.4271/2020-01-0270

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…The approach has been validated in easing the computational burden for various problems ranging from parameter calibration and control design. References [16], [17], [18], [19], [20], [21], [22] implemented the Bayesian optimization framework in automotive domain for performing the engine calibration. Apart from automotive applications, the Bayesian optimization approach has also been successfully implemented in other applications such as analog/rf circuit design [23], groundwater reactive transport model [24], actuator modeling [25], and designing natural-gas liquefaction plant [26].…”

Section: Introductionmentioning

confidence: 99%

Sample-efficient Model Predictive Control Design of Soft Robotics by Bayesian Optimization

Pal¹,

He²,

Wei³

2022

Preprint

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This paper presents a sample-efficient data-driven method to design model predictive control (MPC) for cableactuated soft robotics using Bayesian optimization. Instead of modeling the complex dynamics of the soft robots, the proposed approach uses Bayesian optimization to search the best-guessed low-dimensional prediction model and its associated controller to minimize the objective function of closed-loop responses. The prediction model is updated by Bayesian optimization from the closed-loop input-output data in each iteration. A linear MPC is then designed based on the updated prediction model, and evaluated based on the closed-loop responses. Different from directly searching controller parameters, the closed-loop system stability, and inputs/outputs constraints can be easily handled in the MPC design. After a few iterations, a convergent solution of a (sub-)optimal controller can be obtained, which minimizes the user-defined closed-loop performance index. The proposed method is simulated and validated by a high-fidelity simulation of a cable-actuated soft robot. The simulation results demonstrate that the proposed approach can achieve desired tracking controller for the soft robot without a prior-known model.

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Section: Introductionmentioning

confidence: 99%

Sample-efficient Model Predictive Control Design of Soft Robotics by Bayesian Optimization

Pal¹,

He²,

Wei³

2022

Preprint

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show abstract

“…For the current work, the surrogate-assisted optimization method, also known as Bayesian optimization, is used as it has shown its ability to significantly reduce the computational burden in identifying the global optimal regions. [9][10][11][12] The surrogate-assisted optimization method works by creating a data-driven model using the experimental data and using it intelligently to lower the required expensive function evaluations. It is an iterative approach to push the search direction toward the global optimal region.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-driven model-based calibration for optimizing electrically boosted diesel engine performance

Pal

Zhu

Wang

et al. 2022

International Journal of Engine Research

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Engine calibration is a process of optimizing its control variables, which is a crucial step to achieve the desired performance from the engine system. However, its complexity has increased dramatically due to the constant evolution of the engine architecture with multiple control variables interacting with each other, making it difficult to calibrate its control variables with significantly increased experimental costs. System complexity also affects the physics-based modeling process as it makes the system model highly non-linear and difficult to capture system dynamics under all engine operating conditions. To overcome these challenges, the current work proposes a machine learning-based framework to reduce the required calibration effort. The work implements a stochastic surrogate assisted optimization approach on an actual engine experimental test setup, which efficiently looks for the global optimal solution based on only the input-output data set. This work performs a steady-state calibration of a turbocharged diesel engine with an additional external electric-assisted boosting system (eBoost). Three control parameters are considered for this study: Variable Geometry Turbocharger (VGT) vane position, Exhaust Gas Recirculation (EGR) valve position, and eBoost speed to identify the optimal trade-off between engine efficiency (Brake Specific Fuel Consumption) and emissions ( NO x). The addition of eBoost makes the calibration problem difficult because the two control parameters, VGT and EGR, also depend on eBoost speed. Using the proposed data-driven calibration approach, the experimental results showed enhanced engine performance in terms of efficiency and emissions using the eBoost compared with the case without eBoost. At the same time, the experimental cost was reduced significantly due to the reduced number of testing points required to achieve the optimum.

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Engine Calibration With Surrogate-Assisted Bilevel Evolutionary Algorithm

Wang

Zhu

et al. 2024

IEEE Trans. Cybern.

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Engine Calibration Using Global Optimization Methods with Customization

Cited by 7 publications

References 28 publications

Sample-efficient Model Predictive Control Design of Soft Robotics by Bayesian Optimization

Sample-efficient Model Predictive Control Design of Soft Robotics by Bayesian Optimization

Data-driven model-based calibration for optimizing electrically boosted diesel engine performance

Engine Calibration With Surrogate-Assisted Bilevel Evolutionary Algorithm

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