Constrained Surrogate-Based Engine Calibration Using Lower Confidence Bound

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

doi:10.1109/tmech.2021.3053246

Cited by 19 publications

(13 citation statements)

References 32 publications

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“…The high potential efficiency of SMAO process has been validated in terms of low cost and fast convergence by using classical test problems 12 and SMAO has been used for different applications such as groundwater reactive transport 13 and aerodynamic problems 14 with limited applications to automotive industry. Recently, Pal et al 15,16 successfully optimized the diesel engine calibration process using the SMAO method. To the authors’ best knowledge, this paper is the first time to utilize the SMAO method for predicting borderline knock limit.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Bayesian optimization for predicting borderline knock

Tang

Pal

Wen

et al. 2021

International Journal of Engine Research

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Engine knock is an undesirable combustion that could damage the engine mechanically. On the other hand, it is often desired to operate the engine close to its borderline knock limit to optimize combustion efficiency. Traditionally, borderline knock limit is detected by sweeping tests of related control parameters for the worst knock, which is expensive and time consuming, and also, the detected borderline knock limit is often used as a feedforward control without considering its stochastic characteristics without compensating current engine operational condition and type of fuel used. In this paper, stochastic Bayesian optimization method is used to obtain a tradeoff between stochastic knock intensity and fuel economy. The log-nominal distribution of knock intensity signal is converted to Gaussian one using a proposed map to satisfy the assumption for Kriging model development. Both deterministic and stochastic Kriging surrogate models are developed based on test data using the Bayesian iterative optimization process. This study focuses on optimizing two competing objectives, knock intensity and indicated specific fuel consumption using two control parameters: spark and intake valve timings. Test results at two different operation conditions show that the proposed learning algorithm not only reduces required time and cost for predicting knock borderline but also provides control parameters, based on trained surrogate models and the corresponding Pareto front, with the best fuel economy possible.

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Section: Introductionmentioning

confidence: 99%

Stochastic Bayesian optimization for predicting borderline knock

Tang

Pal

Wen

et al. 2021

International Journal of Engine Research

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“…However, standard acquisition functions such as the expected improvement, lower confidence bound, and entropy search, to name a few, have to be constrained by some form of a feasibility measure. Thus, different constrained acquisition functions such as constrained expected improvement [10], constrained lower confidence bound [6], and constrained predictive entropy search [11] have been proposed to accommodate the feasibility of the constraints functions and limit the search space design when acquiring data.…”

Section: A Constrained Bayesian Optimizationmentioning

confidence: 99%

“…Constrained Bayesian optimization has been widely used in optimizing black-box constrained objective functions [4], robotics [5], automotive [6], and chemical design [7]. However, little research has been done on the application of constrained Bayesian optimization in aerospace engineering design.…”

Section: Introductionmentioning

confidence: 99%

Physics-Constrained Bayesian Optimization for Optimal Actuators Placement in Composite Structures Assembly

AlBahar

Kim

Wang

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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Complex constrained global optimization problems such as optimal actuators placement are extremely challenging. Such challenges, including nonlinearity and nonstationarity of engineering response surfaces, hinder the use of ordinary constrained Bayesian optimization (CBO) techniques with standard Gaussian processes as surrogate models. To overcome those challenges, we propose a physics-constrained Bayesian optimization with multi-layer deep structured Gaussian processes, MGP-CBO. Specifically, we introduce a surrogate model with a multi-layer deep Gaussian process (MGP) mean function. The hierarchical structure of our model enables the applicability of constrained Bayesian optimization to complex nonlinear and nonstationary processes. The deep Gaussian process regression model, MGP, can efficiently and effectively represent the response surface function between actuators and dimensional deformations, thus yielding a better estimated global optimum in a shorter computational time. The proposed MGP-CBO model can realize faster convergence to the global optimum with lower constraint violations. Through extensive evaluations carried out on synthetic problems and a real-world engineering design problem, we show that MGP-CBO outperforms existing benchmarks. Although we use the optimal actuators placement as a demonstration example, the proposed MGP-CBO model can be applied to other complex nonstationary engineering optimization problems.Note to Practitioners-Bayesian optimization is a widely used sequential design strategy for engineering optimization because it does not rely on functional forms of response surfaces. This paper helps address two questions in practice: (i) how to incorporate physics constraints into Bayesian optimization. (ii) How to do Bayesian optimization when the systems have hierarchical structures. In practice, the hierarchical system structure is ubiquitous, and the engineering optimization is constrained by physical laws or special requirements. Therefore, the proposed physics-constrained Bayesian optimization with a multi-layer Gaussian process could provide a new tool for engineering design optimization problems. The computational convergence and complexity have been investigated. The proposed method Dr. Yue's research is partially funded by the US National Science Foundation (NSF) under CMMI-2035038, the Department of Defense (DoD) MEEP program under award N00014-19-1-2728, and the Grainger Frontiers of Engineering Grant Award from the National Academy of Engineering. Dr.

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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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Constrained Surrogate-Based Engine Calibration Using Lower Confidence Bound

Cited by 19 publications

References 32 publications

Stochastic Bayesian optimization for predicting borderline knock

Stochastic Bayesian optimization for predicting borderline knock

Physics-Constrained Bayesian Optimization for Optimal Actuators Placement in Composite Structures Assembly

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

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