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stochastic model predictive control based on multi-step control strategy for discrete nonlinear systems

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第7期35卷 2478-2498页

作者： Liu, Xiangjie Feng, Le Kong, Xiaobing Lee, Kwang Y. North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX USA

stochastic model predictive control (SMPC) is a popular approach to control uncertain systems by incorporating the probabilistic distribution of the uncertainty into the controller design. However, the performance of the SMPC deteriorates rapidly when the system becomes nonlinear. In this article, an efficient SMPC is derived after decomposing the discrete nonlinear systems into a linear stochastic model and a bounded model mismatch. The proposed controller includes a linearized-model-based SMPC and an RMPC for the model mismatch. The multi-step control strategy is incorporated to reduce the conservativeness by introducing more degrees of freedom. The feasibility of the recursiveness of the proposed controller is thoroughly discussed. The control performance is illustrated by examples involving both a numerical nonlinear stochastic system and a wind-blade pitch system.

关键词： stochastic model predictive control discrete nonlinear stochastic system multi-step control strategy probabilistic constraint

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stochastic model predictive control for the optimal operation of office buildings

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BUILDING AND ENVIRONMENT 2025年 267卷

作者： He, Ning Guo, Jiawen Li, Yanxin Quan, Yubo Li, Ruoxia Yang, Liu Xian Univ Architecture & Technol Sch Mech & Elect Engn Xian 710311 Peoples R China Xian Univ Architecture & Technol Coll Informat & Control Engn Xian 710311 Peoples R China Xian Univ Architecture & Technol State Key Lab Green Bldg Western China Xian 710311 Peoples R China

This paper developed a novel stochastic model predictive control (SMPC) strategy to enhance the operational efficiency of office buildings. Firstly, an improved state space model encompassing temperature and relative humidity simultaneously is developed to accurately characterize the thermal comfort condition within the office building. Then, given the obtained comprehensive model, a new SMPC approach is proposed based on chance constraints to minimize energy consumption while guaranteeing thermal comfort for occupants. Besides, the feasibility and stability properties of the SMPC are demonstrated theoretically. Finally, the proposed SMPC method is verified through a real office building located in Xi'an, China, and the result shows that compared to the conventional ON-OFF and MPC control strategies, the SMPC can achieve 39.1 % and 33.3 % energy-saving and less temperature and relative humidity requirement violations.

关键词： State space model stochastic model predictive control Chance constraint Office buildings

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stochastic model predictive control Based Fast-Slow Coordination Automatic Generation control

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第3期39卷 5259-5271页

作者： Shen, Yukang Wu, Wenchuan Sun, Shumin Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China State Grid Shandong Elect Power Co Ltd Elect Power Res Inst Jinan 250003 Peoples R China

The increasing integration of renewable energy sources to power systems brings challenges to the real-time frequency control. This article proposes a stochastic model predictive control (MPC) based two-timescale automatic generation control (AGC) scheme to coordinate the fast converter interfaced generators (CIG) and slow synchronous generators (SG). In the proposed model, the transmission time delay and dynamic features of heterogeneous generators are formulated, so the control performance can be significantly improved. In each AGC period, the AGC commands of fast CIGs are set as multi-step signals while those of SGs remain as single step, and multiple predicted system states of intra-period are incorporated in the control objective. The prediction error is characterized using Gaussian distribution. Based on the decomposition of predicted states and the exact reformulation of the original chance constraints, the stochastic MPC model can be equivalent transformed into a tractable quadratic program model. Case studies conducted on modified IEEE 118-bus system shows the effectiveness of the proposed method.

关键词： Automatic generation control stochastic processes Frequency control Power system stability Power system dynamics Generators Delays stochastic model predictive control two-timescale fast-slow coordination converter interfaced generator

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stochastic model predictive control With a Safety Guarantee for Automated Driving

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 22-36页

作者： Brudigam, Tim Olbrich, Michael Wollherr, Dirk Leibold, Marion Tech Univ Munich Chair Automat Control Engn D-80333 Munich Germany Univ Augsburg Dept Comp Sci D-86135 Augsburg Germany

Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding vehicles. Future motion of surrounding vehicles is often difficult to predict. Whereas robust control approaches achieve safe, yet conservative motion planning for automated vehicles, stochastic model predictive control (SMPC) provides efficient planning in the presence of uncertainty. Probabilistic constraints are applied to ensure that the maximal risk remains below a predefined level. However, safety cannot be ensured as probabilistic constraints may be violated, which is not acceptable for automated vehicles. Here, we propose an efficient trajectory planning framework with safety guarantees for automated vehicles. SMPC is applied to obtain efficient vehicle trajectories for a finite horizon. Based on the first optimized SMPC input, a guaranteed safe backup trajectory is planned using reachable sets. This backup is used to overwrite the SMPC input if necessary for safety. Recursive feasibility of the safe SMPC algorithm is proved. Highway simulations show the effectiveness of the proposed method regarding performance and safety.

关键词： Trajectory Trajectory planning Uncertainty Safety TV Probabilistic logic predictive control model predictive control stochastic model predictive control failsafe trajectory planning automated vehicles

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stochastic model predictive control-based countermeasure methodology for satellites against indirect kinetic cyber-attacks

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INTERNATIONAL JOURNAL OF control 2023年第7期96卷 1895-1908页

作者： Amin Alandihallaj, M. Assadian, Nima Khorasani, Khashayar Univ Toronto Inst Aerosp Studies Toronto ON Canada Sharif Univ Technol Dept Aerosp Engn Tehran Iran Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

The objective of this paper is to provide a stochastic framework to optimally avoid collision between a maneuverable spacecraft and a space object or debris. The satellite collision can be caused through a cyber-attack on a satellite by colliding it with a considered strategic satellite. Consequently, it is highly imperative that critical operational space assets be provided with autonomous collision avoidance systems. The collision avoidance methodology proposed in this paper will reduce the collision probability to an acceptable level and protect the satellite against indirect kinetic cyber-attacks initiated by designing optimal collision avoidance maneuvers using a stochastic model predictive control strategy. The collision probability is estimated using the available historical Two-Line Elements of determined objects, and the model predictive control scheme guarantees the safety of the space close approaches. The proposed and developed collision-avoidance countermeasure methodology is numerically simulated for the collision case study between the Iridium-33 and the Cosmos-2251 satellites. The results demonstrate and illustrate the effectiveness, capabilities, and advantages of our proposed methodology in avoiding probable collisions due to indirect kinetic cyber-attacks.

关键词： Collision avoidance maneuver indirect kinetic cyber-attack cyber-attack countermeasure model predictive control stochastic model predictive control

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stochastic model predictive control for Water Level Regulation in a Lake 3

Stochastic Model Predictive Control for Water Level Regulati...

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3rd IFAC Workshop on Integrated Assessment modeling for Environmental Systems (IAMES)

作者： Jibran, Muhammad Weyer, Erik Wu, Wenyan Univ Melbourne Dept Elect & Elect Engn Melbourne Vic Australia Univ Melbourne Dept Infrastruct Engn Melbourne Vic Australia

In this paper, we propose a water level regulation framework for systems with uncertain model parameters and subject to additive disturbances. Bayesian system identification is used to estimate the distributions of the uncertain parameters. To regulate water levels, stochastic model predictive control (SMPC) is applied, using a combination of an average approach and a chance-constrained approach. We adopt a scenario approach to obtain a tractable approximation of the optimisation problem. Simulations based on real data from the upper part of the Murray River in Australia show the efficacy of the proposed framework. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Bayesian identification stochastic model predictive control chance-constrained optimisation scenario approach

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stochastic model predictive control for Robust Grid Frequency Regulation 12

Stochastic Model Predictive Control for Robust Grid Frequenc...

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12th IFAC Symposium on control of Power and Energy Systems (CPES)

作者： Och, Alexander Ulbig, Andreas Rhein Westfal TH Aachen IAEW Aachen Germany

This paper delves into the application of stochastic model predictive controls (SMPC) for power grids driven by inverter-interfaced generators, focusing on enhancing grid stability amidst decreasing inertia. By employing SMPC, uncertainties in energy systems are anticipated and plant-model mismatch is mitigated. Improvement in grid robustness concerning frequency limits is demonstrated via a Monte Carlo approach. The integration of data-driven model augmentation and stochastic constraint tightening significantly enhance the precision and robustness of frequency control. This study highlights the potential of SMPC in navigating uncertainties in energy systems and offering a robust framework for maintaining grid stability. Copyright (c) 2024 The Authors.

关键词： stochastic model predictive control model predictive control Frequency Regulation Virtual Inertia Droop control Data-Driven modelling Monte Carlo Approach

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stochastic model predictive control with Optimal Linear Feedback for Mobile Robots in Dynamic Environments 8

Stochastic Model Predictive Control with Optimal Linear Feed...

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8th IFAC Conference on Nonlinear model predictive control (NMPC)

作者： Gao, Yunfan Messerer, Florian van Duijkeren, Niels Diehl, Moritz Robert Bosch GmbH Corp Res Stuttgart Germany Univ Freiburg Dept Microsyst Engn IMTEK Freiburg Germany

Robot navigation around humans can be a challenging problem since human movements are hard to predict. stochastic model predictive control (MPC) can account for such uncertainties and approximately bound the probability of a collision to take place. In this paper, to counteract the rapidly growing human motion uncertainty over time, we incorporate state feedback in the stochastic MPC. This allows the robot to more closely track reference trajectories. To this end the feedback policy is left as a degree of freedom in the optimal control problem. The stochastic MPC with feedback is validated in simulation experiments and is compared against nominal MPC and stochastic MPC without feedback. The added computation time can be limited by reducing the number of additional variables for the feedback law with a small compromise in control performance. Copyright (C) 2024 The Authors.

关键词： robotics stochastic model predictive control motion planning

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Enhanced Tube-Based Event-Triggered stochastic model predictive control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Learning-Based stochastic model predictive control for Autonomous Driving at Uncontrolled Intersections

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第2期26卷 1538-1546页

作者： Soman, Surya Zanon, Mario Bemporad, Alberto IMT Sch Adv Studies Lucca I-55100 Lucca Italy

Autonomous driving in urban environments requires safe control policies that account for the non-determinism of moving obstacles, such as the position other vehicles will take while crossing an uncontrolled intersection. We address this problem by proposing a stochastic model predictive control (MPC) approach with robust collision avoidance constraints to guarantee safety. By adopting a stochastic formulation, the quality of closed-loop tracking is increased by avoiding giving excessive importance to future obstacle configurations that are unlikely to occur. We compute the probabilities associated with different obstacle trajectories by learning a classifier on a realistic dataset generated by the microscopic traffic simulator SUMO and show the benefits of the proposed stochastic MPC formulation on a simulated realistic intersection.

关键词： Autonomous vehicles model predictive control model predictive control scenario trees scenario trees stochastic model predictive control stochastic model predictive control supervised learning supervised learning classification methods classification methods decision trees decision trees

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