A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization

Sorourifar, Farshud; Makrygirgos, Georgios; Mesbah, Ali; Paulson, Joel A.

doi:10.1016/j.ifacol.2021.08.249

Cited by 42 publications

(10 citation statements)

References 15 publications

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“…Constrained BO (CBO) has been demonstrated to efficiently tune also predictive controllers. The approach relies on efficiently finding parameters in model predictive control (MPC), such as optimal weights in the MPC cost, prediction horizon length, and even dynamic model parameters through BO, and tuning is done with respect to optimizing the performance of the system [33]- [35]. Applying BO to tune the MPC parameters according to specified performance indicators can also compensate for model mismatch, as demonstrated in [36].…”

Section: Introductionmentioning

confidence: 99%

“…Applying BO to tune the MPC parameters according to specified performance indicators can also compensate for model mismatch, as demonstrated in [36]. While parameters relevant to the safety have been included as constraints in a CBO tuning approach [33], [34], in the current work we extend it by including a penalization term in the cost for maintaining safety during normal operation, including changes of the system such as variable load or wear.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization

König¹,

Khosravi²,

Maier³

et al. 2023

IEEE Trans. Ind. Electron.

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This paper presents an automated, model-free, datadriven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is composed of data-driven performance metrics and modelled using Gaussian processes. The safety requirement is imposed via a barrier-like term in the objective, which is introduced to account for operational changes in the system. We further introduce a data-driven constraint that captures the stability requirements from system data. Numerical evaluation shows that the proposed approach outperforms relay feedback autotuning and quickly converges to the global optimum, thanks to a tailored stopping criterion. We demonstrate the performance of the method in simulations and experiments. For experimental implementation, in addition to the introduced safety constraint, we integrate a method for automatic detection of the critical gains and extend the optimization objective with a penalty depending on the proximity of the current candidate points to the critical gains. The resulting automated tuning method optimizes system performance while ensuring stability and standardization.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization

König¹,

Khosravi²,

Maier³

et al. 2023

IEEE Trans. Ind. Electron.

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“…Compared with other derivative-free optimization algorithms, such as particle swarm optimization and genetic algorithms, BO is considerably faster and more data-efficient (Lu et al, 2020). BO has been adopted in a few MPC tuning problems (Guzman et al, 2022; Lu et al, 2020; Piga et al, 2019; Sorourifar et al, 2021; Stróżecki et al, 2021) to attain higher overall closed-loop performance. A review of BO can be found in Shahriari et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Automatic tuning of robust model predictive control in iterative tasks using efficient Bayesian optimization

Tong,

Du,

Fan

et al. 2023

Transactions of the Institute of Measurement and Control

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Robust model predictive control (RMPC) is an effective technology for controlling uncertain systems while robustly handling constraints, and its closed-loop performance heavily relies on the selection of objective functions. However, the objective functions are typically chosen to be close to the real control objectives, despite an objective function that leads to less conservative constraints often provides better closed-loop performance. In this paper, we propose an automatic tuning framework for RMPC in iterative tasks. In particular, we parameterize RMPC and develop a Bayesian optimization (BO) method to tune it by solving a black-box optimization problem. We then introduce an efficient transfer learning framework within BO, which speeds up the searching process and enhances the controller performance. The effectiveness of the proposed tuning framework is illustrated on numerical examples.

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“…Bayesian optimization (BO) 7,8 has emerged as a powerful approach for handling these types of black‐box problems, even when the measured objective value is corrupted by noise. Several recent works have successfully demonstrated BO for model learning and auto‐tuning of model predictive control (MPC) 5,9–11 and other complex control structures 12,13 …”

Section: Introductionmentioning

confidence: 99%

“…Bayesian optimization (BO) 7,8 has emerged as a powerful approach for handling these types of black-box problems, even when the measured objective value is corrupted by noise. Several recent works have successfully demonstrated BO for model learning and auto-tuning of model predictive control (MPC) 5,[9][10][11] and other complex control structures. 12,13 Standard BO approaches for auto-tuning rely on nonparametric Gaussian process (GP) models, 14 constructed from closed-loop simulation or experimental data, to describe the impact of controller tuning parameters on the closed-loop performance measures; these GP models can be interpreted as probabilistic "surrogate models" for the performance measures of interest.…”

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confidence: 99%

Adversarially robust Bayesian optimization for efficient auto‐tuning of generic control structures under uncertainty

2022

Self Cite

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The performance of optimization‐ and learning‐based controllers critically depends on the selection of several tuning parameters that can affect the closed‐loop control performance and constraint satisfaction in highly nonlinear and nonconvex ways. Due to the black‐box nature of the relationship between tuning parameters and general closed‐loop performance measures, there has been a significant interest in automatic calibration (i.e., auto‐tuning) of complex control structures using derivative‐free optimization methods, including Bayesian optimization (BO) that can handle expensive unknown cost functions. Nevertheless, an open challenge when applying BO to auto‐tuning is how to effectively deal with uncertainties in the closed‐loop system that cannot be attributed to a lumped, small‐scale noise term. This article addresses this challenge by developing an adversarially robust BO (ARBO) method that is particularly suited to auto‐tuning problems with significant time‐invariant uncertainties in an expensive system model used for closed‐loop simulations. ARBO relies on a Gaussian process model that jointly describes the effect of the tuning parameters and uncertainties on the closed‐loop performance. From this joint Gaussian process model, ARBO uses an alternating confidence‐bound procedure to simultaneously select the next candidate tuning and uncertainty realizations, implying only one expensive closed‐loop simulation is needed at each iteration. The advantages of ARBO are demonstrated on two case studies, including an illustrative problem and auto‐tuning of a nonlinear model predictive controller using a benchmark bioreactor problem.

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A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization

Cited by 42 publications

References 15 publications

Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization

Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization

Automatic tuning of robust model predictive control in iterative tasks using efficient Bayesian optimization

Adversarially robust Bayesian optimization for efficient auto‐tuning of generic control structures under uncertainty

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