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Asynchronous ADMM for nonlinear continuous-time systems

OPTIMAL control APPLICATIONS & METHODS 2024年第4期45卷 1375-1403页

作者： von Esch, Maximilian Pierer Voelz, Andreas Graichen, Knut Friedrich Alexander Univ Erlangen Nurnberg Chair Automat Control Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Chair Automat Control D-91058 Erlangen Germany

This paper presents synchronous as well as asynchronous formulations of the alternating direction method of multipliers (ADMM) for solving continuous-time nonlinear distributed model predictive control (DMPC) problems. It is shown that the optimal control problems of certain system classes can be transformed to fit the consensus-based ADMM variant problem formulation. The arising subproblems are solved locally on the agent level while the consensus step is solved centrally by a coordinator. Furthermore, the convergence of the synchronous and asynchronous ADMM algorithms to their respective first-order optimality conditions is presented in a continuous-time setting. The algorithm is applied to different example systems for which the convergence behavior and influence of the individual algorithmic parameters are investigated. The computation time of the agents remains unaffected by the system size and thus demonstrates the applicability to high-scaled systems. Moreover, results show that the asynchronous algorithm performs better in terms of execution time when compared to its synchronous counterpart.

关键词： ADMM asynchronous ADMM distributed model predictive control distributed optimal control multi-agent systems

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distributed MPC-Based Hierarchical Cooperative control for Mixed Vehicle Groups With T-CPS in the Vicinity of Traffic Signal Light

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第7期25卷 8003-8016页

作者： Huang, Shuai Sun, Dihua Zhao, Min Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China

To minimize the stop-and-go behavior caused by human factors and traffic signal light constraints, this paper proposes a distributed MPC-based hierarchical cooperative control protocol for mixed vehicle groups consisting of Connected and Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) in the vicinity of traffic signal lights (VTSL). Firstly, to characterize the formation pattern and mechanism of the mixed vehicle groups, the subgroup division and subgroup reorganization method is presented in the VTSL. Secondly, since human factors can affect the driving behavior of all vehicles within a subgroup, a HV model is established via considering human factors, such as the driver's insensitivity to distance and speed of the preceding vehicle. Thirdly, to guarantee the maximum number of vehicles passing the VTSL and the minimum travel time under the traffic signal light constraints, a distributed MPC-based hierarchical cooperative control method is proposed from the Transportation Cyber-Physical System (T-CPS) perspective. Finally, the simulator experiment results indicate that the proposed control protocol is more advantageous and effective as the penetration rate of CAVs continuously increase.

关键词： Fuels Sensors Protocols Human factors Vehicles Sun Velocity measurement Mixed traffic distributed model predictive control cooperative control transportation cyber-physical system human factors

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Integrated optimization of traffic signals and vehicle trajectories for mixed traffic at signalized intersections: A two-level hierarchical control framework

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 169卷

作者： Zou, Yangang Zheng, Fangfang Liu, Can Liu, Xiaobo Southwest Jiaotong Univ Sch Transportat & Logist Natl Engn Lab Integrated Transportat Big Data Appl Natl United Engn Lab Integrated & Intelligent Tran Western Hitech Zone Chengdu 611756 Sichuan Peoples R China

With the rapid advancement of connected and automated vehicle (CAV) technology, the integrated optimization of traffic signals and vehicle trajectories has emerged as a promising approach to enhance intersection performance. However, the complexity of this integrated optimization problem requires substantial computational resources, rendering existing methods impractical for real-time applications. To address this challenge, this paper presents a two- level hierarchical control framework for mixed traffic environments, consisting of both CAVs and human-driven vehicles (HVs), offering a computationally efficient solution without compromising performance. At the upper level, we introduce a platoon-based mixed integer linear programming (PMILP) model to jointly optimize signal timing and desired arrival times, with the main objective of minimizing traffic delay. Building upon the optimized desired arrival times, a Nash-based distributed model predictive control (DMPC) method is developed at the lower level to optimize CAV trajectories, enabling vehicles to pass through intersections at free- flow speeds without stopping and minimizing acceleration fluctuations. Numerical experiments are conducted to assess the performance of the proposed method against three alternatives. Method 1 uses actuated signal control (ASC) for traffic signals, and the Intelligent Driver model (IDM) for all vehicles. Method 2 combines the controlled optimization of phases (COP) for signal control with DMPC for CAVs and IDM for HVs. Method 3 applies the proposed PMILP method for traffic signals, predictive cruise control (PCC) for CAVs, and IDM for HVs. The results demonstrate that the proposed integrated optimization approach significantly reduces traffic delay, fuel consumption, and idling time, while simultaneously enhancing driving comfort across different CAV penetration rates and degrees of saturation. Notably, the proposed method achieves these improvements with a high level of computatio

关键词： Connected and automated vehicle Traffic signal control Vehicle trajectory optimization distributed model predictive control

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distributed MPC of Vehicle Platoons Considering Longitudinal and Lateral Coupling

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第3期25卷 2293-2310页

作者： Feng, Yangyang Yu, Shuyou Sheng, Encong Li, Yongfu Shi, Shuming Yu, Jianhua Chen, Hong Jilin Univ Dept Control Sci & Engn Changchun 130025 Peoples R China Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Peoples R China Chongqing Univ Posts & Telecommun Coll Automat Key Lab Intelligent Air Ground Cooperat Control Un Chongqing 400065 Peoples R China Jilin Univ Coll Transportat Changchun 130025 Peoples R China Dongfeng Commercial Vehicle Technol Ctr Wuhan 430058 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China Jilin Univ Dept Control Sci & Engn Changchun 130025 Peoples R China

In this paper, a hierarchical control strategy of vehicle platoons is presented, in which the longitudinal and lateral coupling property of vehicles is taken into account. A three-degree-of-freedom dynamic model of vehicles is approximated to a "global" linear model by the Koopman operator theory. A synchronous distributed predictive control scheme of vehicle platoons is proposed as an upper-level controller, where both the linear vehicle model and a linear parametric-varying lane-keeping model are adopted to predict the dynamic of vehicles, and keep vehicles in the designated lane. Thus, it can avoid the solution of nonlinear optimization problems and reduce the computational burden accordingly. A lower-level controller is designed, where the desired longitudinal control force determined by the upper-level controller is transformed into the desired throttle angle and brake pressure through an inverse longitudinal dynamics model of vehicles. The joint simulation results by PreScan, CarSim and MATLAB/Simulink show that when the leader vehicle accelerates or decelerates, the following vehicles in the platoon can keep the same velocity as the leader vehicle, and maintain the desired safety distance between the front and rear vehicles. In addition, joint simulation in the curved road scenario show that the performance of lane keeping can be guaranteed for vehicle platoons with the proposed control strategy.

关键词： Vehicle platoon longitudinal and lateral coupling Koopman operator lane-keeping distributed model predictive control

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model predictive control for resilient frequency management in power systems

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ELECTRICAL ENGINEERING 2024年第5期106卷 6131-6157页

作者： Das, Anurag Sengupta, Ananyo Natl Inst Technol Rourkela Dept Elect Engn Rourkela 769008 Orissa India

A disturbance in a power system causes the frequency to deviate from its nominal value. The load and generation of the system are strategically adjusted to restore the synchronous frequency. This paper introduces novel shrinking-horizon model predictive control (MPC) technique, which employs a centralized controller for managing the load-frequency of a single-area power system and distributed controllers for multi-area systems. The controller optimally changes generation settings and sheds non-critical loads to make the frequency and tie-line power deviation zero. In contrast to existing approaches that use an approximate first-order transfer function model, this paper presents a structure-preserving linear state-space model for power systems. This model takes into account frequency and voltage dependencies of both load and generation, allowing for more accurate representation of power system behavior. During rescheduling, the controller minimizes additional cost associated with changes while satisfying various operational and physical constraints. The paper conducts several case studies using IEEE test systems to devise corrective action plans for both under- and over-frequency scenarios. The proposed controller's robustness is tested against disturbances originating from renewable energy sources. Furthermore, a performance comparison is drawn between the proposed controller and existing control techniques. The comparative analysis indicates that the proposed approach consistently outperforms other controllers in terms of dynamic performance improvement, measured by parameters such as settling time, overshoot, undershoot, and error reduction. It is observed that the maximum undershoot and the settling time of the frequency is the least for centralized control. Comparative studies indicate that under different circumstances, the proposed approach outperforms other controllers in dynamic performance improvement, measured by settling time, overshoot, undershoot, and e

关键词： Centralized model predictive control distributed model predictive control Frequency-dependent load flow analysis Load-frequency control Optimal scheduling Sensitivity analysis Renewable energy sources

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Deep Learning-Based Energy-Efficient Spacing Policy and Platooning control Co-Design for Connected and Automated Vehicles on Inclined Roads

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第10期73卷 14486-14498页

作者： Zhang, Hanwen Chen, Jicheng Zhu, Chaojie Zhang, Hui Beihang Univ Sch Transportat Sci Engn Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong 999077 Peoples R China

In the work, we present a novel energy-efficient spacing policy and learning-based distributed model predictive control co-design framework for heterogeneous vehicle platoon considering road slope factor. A nonlinear longitudinal dynamics model with look-ahead slope consideration is established for each vehicle in platoon, whose total energy depletion is computed by a constructed energy consumption model. Benefiting from the co-design of platooning control and spacing policy design, energy efficiency is achieved by optimizing inter-vehicular distances and control inputs simultaneously in a designed energy-efficient distributed model predictive control (EDMPC) problem. To reduce online computational requirement of the proposed EDMPC and realize real-time implementation, deep learning technique is adopted to approximate the implicit controller obtained from EDMPC problem. Finally, simulations are conducted to demonstrate the effectiveness and strength of the proposed algorithm. Comparison studies illustrate over 2% percentage energy reduction and average $2 \,\mathrm{ms}$ execution time can be achieved by the EDMPC framework.

关键词： Roads Energy efficiency Energy consumption Computational modeling Vehicle dynamics Aerodynamics Topology Connected and autonomous vehicles heterogeneous vehicle platoon energy-efficient spacing policy design distributed model predictive control learning control

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Tube-Based distributed MPC for Load Frequency control of Power System With High Wind Power Penetration

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第2期39卷 3118-3129页

作者： Liu, Xiangjie Wang, Ce Kong, Xiaobing Zhang, Yi Wang, Weisheng Lee, Kwang Y. North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Univ Sci & Technol Dept Elect Engn Tangshan 063210 Peoples R China North China Elect Power Univ Renewable Energy Sources Beijing 102206 Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing 100192 Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

distributed model predictive control is an effective method to realize the power system load frequency control (LFC). However, the frequency stability of the multi-area power system becomes quite challenging with incorporating high wind power penetration, as it brings strong uncertainties. This article proposes a tube-based distributed model predictive control (DMPC) for the LFC of the interconnected power system. In the scheme, the high penetrated wind power is taken as an independent generation area, in which the virtual inertia control system is incorporated for enhancing the inertia response. The wind turbines are operated under the de-load mode for participating in the frequency regulation, just like the thermal power plants. The tube-based DMPC consists of a nominal MPC and an ancillary feedback law, which aims at restraining the wind speed uncertainty. The neighboring areas' information is incorporated into the ancillary controller as a feedback term, realizing effective coordination between generation areas. The input-to-state stability of the power system under the proposed tube-based DMPC can be guaranteed based on the Schur property and the bounded disturbance condition. The simulation on a four-area interconnected power system demonstrates the effectiveness of proposed algorithm on alleviating the frequency fluctuation caused by varying load and uncertain wind speed.

关键词： distributed model predictive control load frequency control tube-based control wind speed uncertainty

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A Multi-Timescale Coordinated Optimization Framework for Economic Dispatch of Micro-Energy Grid Considering Prediction Error

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第2期39卷 3211-3226页

作者： Yang, Yi Yang, Ping Zhao, Zhuoli Lai, Loi Lei South China Univ Technol Elect Power Coll Key Lab Clean Energy Technol Guangdong Prov Guangzhou 510640 Peoples R China Guangdong Univ Technol Sch Automat Dept Elect Engn Guangzhou 510006 Peoples R China

The integration of a large number of renewable energy sources (RES) into the micro-energy grid (MEG) brings certain challenges to its optimal dispatching, especially the impact of RES and load prediction error on optimal dispatching. Therefore, this article proposes a multi-timescale coordinated optimization framework for economic dispatch of MEG considering prediction error, including three stages: day-ahead economic dispatch, intra-day rolling optimization and real-time adjustment. In the day-ahead and intra-day stages, the device's output is solved with the objective of optimal system economy. The MEG is divided into three subsystems in the real-time adjustment stage using distributed model predictive control (DMPC). The subsystems cooperate to correct the intra-day device output, which reduces the complexity of solving system optimization problems. Meanwhile, considering that the prediction horizon in DMPC usually remains constant during the optimization, which affects the control performance of DMPC. This article analyzes the impact of the prediction horizon and control horizon on the control performance of DMPC. Then a DMPC method with adaptive adjustment of the prediction horizon according to the prediction errors of RES and loads power is proposed, which can balance the stability and prediction accuracy of the system. Finally, the case studies verify the feasibility of the proposed strategy.

关键词： Micro energy grid multi-timescale prediction error distributed model predictive control adaptive adjustment

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A third-party platoon coordination service: Pricing under government subsidies

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ASIAN JOURNAL OF control 2025年第1期27卷 13-26页

作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Martensson, Jonas KTH Royal Inst Technol Sch Elect Engn & Comp Sci Stockholm Sweden KTH Royal Inst Technol Integrated Transport Res Lab ITRL Stockholm Sweden Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Shanghai Peoples R China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease.

关键词： distributed model predictive control government subsidies platoon coordination pricing rules

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distributed model predictive Load Frequency control of multi-area Power Grid: A Decoupling Approach ⁎

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IFAC-PapersOnLine 2019年第20期52卷 205-210页

作者： Eleftherios E. Vlahakis Leonidas D. Dritsas George D. Halikias School of Mathematics Computer Science and Engineering City University of London UK Department of Electrical & Electronic Engineering Educators School of Pedagogical & Technological Education ASPETE Athens Greece

A model-predictive scheme for load frequency control of a multi-area power system is proposed. The method depends on a decoupling technique which allows for a control design with a distributed architecture. Treating the total power inflows of each area as input variables, a decoupled linearized model for each area is derived. This allows for the formulation and solution of a model predictive control problem with a quadratic performance index and input saturating constraints on the individual tie-line power flows, along with an overall equality constraint to address the energy balance of the network. It is assumed that the interconnection topology (tie-lines) coincides with the communication topology of the network. The only information which needs to be shared between interconnected areas is the local frequency variables. The effectiveness of the method is illustrated via a simulation study of a three-area network. Future work will attempt to establish formally the stability of the control scheme and to enhance the versatility of the method by including constraints on the state variables.

关键词： distributed model predictive control load frequency control automatic generation control interconnected power system

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