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15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yang, Jian Fang, Lingqi Song, Dongran Li, Ya Liu, Beibei Lv, Quanxu Cent South Univ Sch Automat Hunan Prov Key Lab Power Elect Equipment & Grid Changsha Peoples R China

ISBN: (纸本)9781728151694

model predictive yaw control (MPYC) using the future wind direction information could improve energy conversion efficiency of wind turbines. However, the performance of MPYC system is closely related to the wind direction prediction of which the accuracy is actually difficult to improve. In this paper, we propose a stochastic model predictive yaw control (SMPYC) based on multi-scenario optimization to solve the uncertainty of future wind direction prediction. Meanwhile, in order to reduce computational burden during the model solving, the synchronous backward substitution method is used to cut down the scenarios with guaranteed precision. Then, the performance of the proposed SMPYC method is demonstrated by the simulation tests comparing with baseline control method (MPYC). Finally, our results show that the overall performance including power production and yaw actuator usage of SMPVC is enhanced.

关键词： wind turbine yaw system stochastic model predictive control multi-scenario optimization synchronous backward substitution method

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A CHANCE-CONSTRAINED stochastic model predictive control PROBLEM WITH DISTURBANCE FEEDBACK

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2021年第1期17卷 67-79页

作者： Tan, Yuan Cao, Qingyuan Li, Lan Hu, Tianshi Su, Min Sichuan Univ Coll Elect & Informat Technol Chengdu Peoples R China Univ Edinburgh Business Sch Edinburgh Midlothian Scotland Chengdu Univ Informat Technol Sch Elect Engn Chengdu Peoples R China Xihua Univ Inovat & Entrepreneurship Coll Chengdu Peoples R China

In this paper, we develop two algorithms for stochastic model predictive control (SMPC) problems with discrete linear systems. Participially, chance constraints on the state and control are considered. Different from the state-of-the-art robust model predictive control (RMPC) algorithm, the proposed is less conservative. Meanwhile, the proposed algorithms do not assume the full knowledge of the disturbance distribution. It only requires the mean and variance of the disturbance. Rigorous computational analysis is carried out for the proposed algorithms. Numerical results are provided to demonstrate the effectiveness and the superior of the proposed SMPC algorithms.

关键词： stochastic model predictive control chance constraint robust optimization

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Energy Management of the Power-Split Hybrid Electric City Bus Based on the stochastic model predictive control

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IEEE ACCESS 2021年 9卷 2055-2071页

作者： Yang, Rong Yang, Xiaohu Huang, Wei Zhang, Song Guangxi Univ Coll Mech Engn Nanning 530004 Peoples R China Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Peoples R China Guangxi Yuchai Machinery Co Ltd Yulin 537005 Peoples R China

The energy management strategy of hybrid electric vehicles is of significant importance to improve the fuel economy. In this regard, two energy management strategies are designed for power-split hybrid electric city bus (HECB), which are based on the linear time-varying stochastic model predictive control (LTV-SMPC) and stochastic model predictive control based on Pontriagin's minimum principle (PMP-SMPC). In the present study, the Markov chain and long short-term memory (LSTM) forecast demand torque and velocity respectively are applied to establish a combination forecast model. Then several processes, including linear approximation, processing simplified control model, the proposed nonlinear vehicle model is converted into a linear time-varying model. Meanwhile, the energy management problem in a linear quadratic programming problem is solved. Considering linearization error and limitations of the quadratic optimization, Pontriagin's minimum principle (PMP) is applied to optimize the nonlinear model predictive control. Based on the reference theory, the range of coordinated variables is derived, and the optimal coordination variable is searched by dichotomy to realize the rolling optimization of the model predictive control (MPC). Finally, the effectiveness of the proposed energy management strategy is verified by simulating several case studies. Obtained results show that compared with the rule-based (RB) control strategy, the fuel economy of LTV-SMPC and PMP-SMPC increases by 8.79% and 14.42%, respectively. Meanwhile, it is concluded that the two strategies have real-time computing potential.

关键词： Hybrid electric city bus stochastic model predictive control combination forecast Markov chain long short-term memory Pontryagin's minimum principle

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Machine learning-based surrogate model assisting stochastic model predictive control of urban drainage systems

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年 346卷 118974页

作者： Luo, Xinran Liu, Pan Xia, Qian Cheng, Qian Liu, Weibo Mai, Yiyi Zhou, Chutian Zheng, Yalian Wang, Dianchang Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China Wuhan Univ Hubei Prov Key Lab Water Syst Sci Sponge City Cons Wuhan 430072 Peoples R China Wuhan Univ Res Inst Water Secur RIWS Wuhan 430072 Peoples R China Hubei Water Resources Res Inst Hubei Water Resources & Hydropower Sci & Technol P Wuhan 430070 Peoples R China Yangtze Ecol & Environm Co Ltd Wuhan 430072 Peoples R China

Quantifying the uncertainty of stormwater inflow is critical for improving the resilience of urban drainage systems (UDSs). However, the high computational complexity and time consumption obstruct the implementation of uncertainty-addressing methods for real-time control of UDSs. To address this issue, this study developed a machine learning-based surrogate model (MLSM) that maintains high-fidelity descriptions of drainage dynamics and meanwhile diminishes the computational complexity. With stormwater inflow and controls as inputs and system overflow as the output, MLSM is able to fast evaluate system performance, and therefore stochastic optimization becomes feasible. Thus, a real-time control strategy was built by combining MLSM with the stochastic model predictive control. This strategy used stochastic stormwater inflow scenarios as input and aimed to minimize the expected overflow under all scenarios. An ensemble of stormwater inflow scenarios was generated by assuming the forecast errors follow normal distributions. To downsize the ensemble, representative scenarios with their probabilities were selected using the simultaneous backward reduction method. The proposed control strategy was applied to a combined UDS of China. Results are as follows. (1) MLSM fit well with the original high-fidelity urban drainage model, while the computational time was reduced by 99.1%. (2) The proposed strategy consistently outperformed the classical deterministic model predictive control in both magnitude and duration dimensions of system resilience, when the consumed time compatible is with the real-time operation. It is indicated that the proposed control strategy could be used to inform the realtime operation of complex UDSs and thus enhance system resilience to uncertainty.

关键词： Urban drainage system Combined sewer overflow stochastic model predictive control Machine learning -based surrogate model Resilience

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Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control

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ANNUAL REVIEWS IN control 2023年 56卷

作者： Landgraf, Daniel Voelz, Andreas Berkel, Felix Schmidt, Kevin Specker, Thomas Graichen, Knut Friedrich Alexander Univ Erlangen Nurnberg FAU Chair Automat Control Cauerstr 7 D-91058 Erlangen Germany Corp Res Robert Bosch GmbH Robert Bosch Campus 1 D-71272 Renningen Germany

The performance of modern control methods, such as model predictive control, depends significantly on the accuracy of the system model. In practice, however, stochastic uncertainties are commonly present, resulting from inaccuracies in the modeling or external disturbances, which can have a negative impact on the control performance. This article reviews the literature on methods for predicting probabilistic uncertainties for nonlinear systems. Since a precise prediction of probability density functions comes along with a high computational effort in the nonlinear case, the focus of this article is on approximating methods, which are of particular relevance in control engineering practice. The methods are classified with respect to their approximation type and with respect to the assumptions about the input and output distribution. Furthermore, the application of these prediction methods to stochastic model predictive control is discussed including a literature review for nonlinear systems. Finally, the most important probabilistic prediction methods are evaluated numerically. For this purpose, the estimation accuracies of the methods are investigated first and the performance of a stochastic model predictive controller with different prediction methods is examined subsequently using multiple nonlinear systems, including the dynamics of an autonomous vehicle.

关键词： Probabilistic prediction stochastic control Nonlinear predictive control stochastic system model Uncertainty approximation Chance constraints stochastic model predictive control

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Two-stage data-driven dispatch for integrated power and natural gas systems by using stochastic model predictive control

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APPLIED ENERGY 2023年第1期343卷

作者： Zhao, Yuehao Li, Zhiyi Ju, Ping Zhou, Yue Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China Cardiff Univ Sch Engn Cardiff CF24 3AA Wales

The optimal dispatch of the integrated power and natural gas systems can increase the utilization rate of renewable energy and energy efficiency while decreasing operation costs. The common prediction errors of wind power and electric load have the potential to negatively impact the normal operation of the integrated power and natural gas systems. A two-stage data-driven dispatch strategy is proposed to reduce this effect, consisting of the day-ahead dispatch stage and the intraday rolling dispatch stage using stochastic model predictive control (MPC). In the day-ahead dispatch stage, the data-driven chance constraints of tie-line power and reserve of gas -fired generators are built, and the day-ahead tie-line power is obtained and regarded as input parameters to the intraday dispatch stage. In the intraday dispatch stage, the data-driven chance constraints of tie-line power and reserve of gas-fired generators with the latest rolling prediction data are built, and the remaining control vari-ables are obtained. The distribution characteristics of the stochastic prediction errors of wind power and electric load are captured and described by the variational Bayesian Gaussian mixture model with massive historical data. Then the original stochastic mixed-integer nonlinear programming problem is converted to a tractable deterministic one by the quantile-based analytical reformulation and convex relaxation technique. Finally, the proposed strategy is verified by the numerical experiments based on a modified IEEE 33-bus system integrated with a 10-node natural gas system and a micro hydrogen system. The numerical results demonstrate that the proposed strategy reduces the actual costs and decreases the violation rate caused by the stochastic prediction errors of wind power and electric load.

关键词： Two-stage dispatch Chance-constrained programming Data-driven stochastic model predictive control Integrated power and natural gas systems

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Friction-adaptive stochastic nonlinear model predictive control for autonomous vehicles

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VEHICLE SYSTEM DYNAMICS 2024年第2期62卷 347-371页

作者： Vaskov, Sean Quirynen, Rien Menner, Marcel Berntorp, Karl Mitsubishi Elect Res Labs Control Auton Cambridge MA 02139 USA Univ Michigan Ann Arbor MI USA

This paper addresses the trajectory-tracking problem under uncertain road-surface conditions for autonomous vehicles. We propose a stochastic nonlinear model predictive controller (SNMPC) that learns a tyre-road friction model online using standard automotive-grade sensors. Learning the entire tyre-road friction model in real time requires driving in the nonlinear, potentially unstable regime of the vehicle dynamics, using a prediction model that may not have fully converged. To handle this, we formulate the tyre-friction model learning in a Bayesian framework and propose two estimators that learn different aspects of the tyre-road friction. The estimators output the estimate of the tyre-friction model as well as the uncertainty of the estimate, which expresses the confidence in the model for different driving regimes. The SNMPC exploits the uncertainty estimate in its prediction model to take proper action when the uncertainty is large. We validate the approach in an extensive Monte Carlo study using real vehicle parameters and in CarSim. The results when comparing to various MPC approaches indicate a substantial reduction in constraint violations, as well as a reduction in closed-loop cost. We also demonstrate the real-time feasibility in automotive-grade processors using a dSPACE MicroAutoBox-II rapid prototyping unit, showing a worst-case computation time of roughly 40 ms.

关键词： Vehicle dynamics stochastic model predictive control particle filtering machine learning

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stochastic model predictive control without terminal constraints

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2019年第15期29卷 4987-5001页

作者： Lorenzen, Matthias Mueller, Matthias A. Allgoewer, Frank Univ Stuttgart Inst Syst Theory & Automat Control D-70569 Stuttgart Germany

Sufficient conditions for the stability of stochastic model predictive control without terminal cost and terminal constraints are derived. Analogous to stability proofs in the nominal setup, we first provide results for the case of optimization over general feedback laws and exact propagation of the probability density functions of the predicted states. We highlight why these results, being based on the principle of optimality, do not directly extend to currently used computationally tractable approximations such as optimization over parameterized feedback laws and relaxation of the chance constraints. Based thereon, for both cases, stability results are derived under stronger assumptions. A third approach is presented for linear systems where propagation of the mean value and the covariance matrix of the states instead of the complete distribution is sufficient, and hence, the principle of optimality can be used again. The main results are presented for nonlinear systems along with examples and computational simplifications for linear systems.

关键词： constrained control MPC without terminal constraints nonlinear systems stochastic model predictive control

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stochastic model predictive control for probabilistically constrained Markovian jump linear systems with additive disturbance

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2019年第15期29卷 5002-5016页

作者： Lu, Jianbo Xi, Yugeng Li, Dewei Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China

model predictive control (MPC) for Markovian jump linear systems with probabilistic constraints has received much attention in recent years. However, in existing results, the disturbance is usually assumed with infinite support, which is not considered reasonable in real applications. Thus, by considering random additive disturbance with finite support, this paper is devoted to a systematic approach to stochastic MPC for Markovian jump linear systems with probabilistic constraints. The adopted MPC law is parameterized by a mode-dependent feedback control law superimposed with a perturbation generated by a dynamic controller. Probabilistic constraints can be guaranteed by confining the augmented system state to a maximal admissible set. Then, the MPC algorithm is given in the form of linearly constrained quadratic programming problems by optimizing the infinite sum of derivation of the stage cost from its steady-state value. The proposed algorithm is proved to be recursively feasible and to guarantee constraints satisfaction, and the closed-loop long-run average cost is not more than that of the unconstrained closed-loop system with static feedback. Finally, when adopting the optimal feedback gains in the predictive control law, the resulting MPC algorithm has been proved to converge in the mean square sense to the optimal control. A numerical example is given to verify the efficiency of the proposed results.

关键词： long-run average cost Markovian jump linear systems maximal admissible sets optimal control probabilistic constraints stochastic model predictive control

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Energy Optimal Dispatch of the Data Center Microgrid Based on stochastic model predictive control

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Zhu, Yixin Wang, Jingyun Bi, Kaitao Sun, Qingzhu Zong, Yu Zong, Chenxi Jiangnan Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China

Renewable energy outputs such as wind turbines and photovoltaics, as well as data center workloads are both random and uncertain. In order to enhance the stability and economy of the data center in actual operation effectively, a multi-time scale optimal dispatch method for the data center microgrid based on stochastic model predictive control is proposed in this paper. In the day-ahead scheduling stage, the characteristic of the data center that batch workloads are allowed to be served delayed is considered. And the scenario analysis method is applied to describe the uncertainty of loads and renewable energy outputs, based on which an economic optimization scheduling model is established to minimize the system operating cost. The intra-day scheduling utilizes the rolling optimization and feedback correction of model predictive control to correct the deviation of loads and renewable energy outputs and adjust the day-ahead dispatch plan in real time, which ensures the effectiveness of day-ahead plan and the stability of system operating. Through the simulation results of a typical data center microgrid, the effectiveness of the proposed method is verified.

关键词： data center stochastic model predictive control scenarios analysis method batch workload energy optimal dispatch

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