Multi-Objective Stochastic Bayesian Optimization for Iterative Engine Calibration

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

doi:10.23919/acc45564.2020.9147983

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…BO has also been proposed to select closed loop kernel based model [4]. BO was used in many other various real-world control applications, such as wind energy systems parameter tuning [1,2], engine calibration [26], and space cooling system optimization [8].…”

Section: Introductionmentioning

confidence: 99%

Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints

Xu¹,

Jones²,

Svetozarevic³

et al. 2023

Preprint

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We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed-loop performance by automatically tuning controller gains or reference setpoints in a model-free manner. However, BO methods have rarely been tested on dynamical systems with unmodeled constraints and time-varying ambient conditions. In this paper, we propose a violation-aware contextual BO algorithm (VACBO) that optimizes closed-loop performance while simultaneously learning constraint-feasible solutions under time-varying ambient conditions. Unlike classical constrained BO methods which allow unlimited constraint violations, or 'safe' BO algorithms that are conservative and try to operate with near-zero violations, we allow budgeted constraint violations to improve constraint learning and accelerate optimization. We demonstrate the effectiveness of our proposed VACBO method for energy minimization of industrial vapor compression systems under time-varying ambient temperature and humidity.

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Section: Introductionmentioning

confidence: 99%

Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints

Xu¹,

Jones²,

Svetozarevic³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Due to the above reasons, efficient global optimization 1 Jones et al (1998) has been widely applied to the closed-loop controller tuning problem, thanks to its black- Paulson and Mesbah (2020) and in various other real-world control applications, such as wind energy systems Vermillion (2017, 2020), engines Pal et al (2020) and space cooling Chakrabarty et al (2021).…”

Section: Introductionmentioning

confidence: 99%

VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints

Jones

Svetozarevic

et al. 2022

2022 American Control Conference (ACC)

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In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints. Existing Gaussian process based closedloop optimization methods, either can only guarantee local convergence (e.g., SafeOPT), or have no known optimality guarantee (e.g., constrained expected improvement) at all, whereas the recently introduced CONFIG algorithm has been proven to enjoy a theoretical global optimality guarantee. In this study, we demonstrate the effectiveness of CONFIG algorithm in the applications. The algorithm is first applied to an artificial numerical benchmark problem to corroborate its effectiveness. It is then applied to a classical constrained steady-state optimization problem of a continuous stirred-tank reactor. Simulation results show that our CONFIG algorithm can achieve performance competitive with the popular CEI (Constrained Expected Improvement) algorithm, which has no known optimality guarantee. As such, the CONFIG algorithm offers a new tool, with both a provable global optimality guarantee and competitive empirical performance, to optimize the closed-loop control performance for a system with soft unmodeled constraints. Last, but not least, the open-source code is available as a python package to facilitate future applications.

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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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Multi-Objective Stochastic Bayesian Optimization for Iterative Engine Calibration

Cited by 14 publications

References 19 publications

Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints

Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints

VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints

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

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