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Multi-Parameter Quadratic Programming explicit model predictive Based Real Time Turboshaft Engine control

ENERGIES 2021年第17期14卷 5539页

作者： Gu, Nannan Wang, Xi Zhu, Meiyin Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China Beihang Univ Beihang Hangzhou Innovat Inst Yuhang Hangzhou 310023 Peoples R China

The traditional model predictive control (tMPC) algorithms have a large amount of online calculation, which makes it difficult to apply them directly to turboshaft engine-rotor systems because of real time requirements. Therefore, based on the theory of the perturbed piecewise affine system (PWA) and multi-parameter quadratic programming explicit model predictive control (mpQP-eMPC) algorithm, we develop a controller design method for turboshaft engine-rotor systems, which can be used for engine steady-state, transient state and limit protection control. This method consists of two steps: controller offline design and online implementation. Firstly, the parameter space of the PWA system is divided into several partitions offline based on the disturbance and performance constraints. Each partition has its own control law, which is in the form of piecewise affine linear function between the controller and the parameters. The control laws for those partitions are also obtained in this offline step. After which, for the online control implementation step, the corresponding control law can be obtained by a real-time query of a corresponding partition, which the current engine state falls into. This greatly reduces the amount of online calculation and thus improves the real-time performance of the MPC controller. The effectiveness of the proposed method is verified by simulating the steady-state and transient process of a turboshaft engine-rotor system with a limit protection requirement. Compared with tMPC, an mpQP-eMPC based controller can not only guarantee good steady-state, dynamic control performance and limit protection, but can also significantly improve the real-time performance of the control system.

关键词： real-time control turboshaft engine limit protection perturbed piecewise affine systems multi-parameter quadratic programming explicit model predictive control

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Multi-Parametric model predictive control for vehicle platoons

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年

作者： Mohammadi, Mahsa Maleki, Mohammadreza Taghavipour, Amir KN Toosi Univ Technol Dept Mech Engn 470 Mirdamad Ave West Tehran 19697 Iran

Traffic congestion has emerged as a pressing issue in today's transportation systems, resulting in significant energy consumption and time wastage. One potential solution to alleviate traffic jams is the utilization of connected automated vehicles (CAVs) in the form of vehicle platoons. In such platoons, the lead vehicle sends its essential information to others through the dedicated short-range communication system (DSRC), enabling the followers to maintain a safe distance and velocity. model predictive control (MPC) is widely recognized as an effective control strategy for regulating platoon behavior. However, its online optimization process involves complex calculations, resulting in sluggish performance. This study aims to design a more efficient control strategy for vehicle platoons and to enhance their overall performance through explicit model predictive control (eMPC). By solving the optimization problem offline in a multiparametric manner, the proposed approach eliminates the computational overhead associated with implicit MPC, making it well-suited for high-speed systems like automotive applications. Simulation results demonstrate that the proposed controller achieves high accuracy in tracking the reference input and assumed acceleration profile, ensuring the stable behavior of the vehicle platoon throughout the pre-defined scenario. Furthermore, the controller is implemented in Prescan software using actual acceleration data for the lead vehicle, confirming its effectiveness under more realistic conditions. Evaluation of the results reveals that the designed controller significantly improves the platoon's performance in terms of tracking the behavior of the lead vehicle, as measured by the mean squared error (MSE) metric.

关键词： Vehicle platoon explicit model predictive control autonomous vehicles V2V communications optimization problem

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BP neural network-based explicit MPC of nonlinear boiler-turbine systems

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JOURNAL OF PROCESS control 2025年 145卷

作者： Li, Jing He, Defeng Wang, Xiuli Kang, Yu Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China Univ Sci & Technol China Automat Dept Hefei 230026 Peoples R China

This paper proposes a new explicit model predictive control (EMPC) scheme of constrained nonlinear systems with unknown but bounded input disturbances. Firstly, support vector machine is used to learn internal and external approximations of the feasible state space of the EMPC. Then, the control surface on the feasibility of EMPC is constructed by a backpropagation neural network (BPNN). The finite horizon optimal control solution to the EMPC can be computed from real-time data by training the control surface. The proposed EMPC is also suitable for nonlinear systems with higher dimensions in terms of reducing online computational burdens and enhancing control accuracy. Next, the Hoeffding's Inequality is used to ensure that the EMPC law computed by the BPNN approximation complies with the specified range with a high level of confidence. Moreover, some conditions are obtained to guarantee the stability and recursive feasibility of the EMPC with probabilistic assurances. Finally, a 160 MW boiler-turbine system is employed to verify the effectiveness and applications of the proposed method.

关键词： explicit model predictive control Boiler-turbine systems BP neural network Nonlinear control

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An explicit predictive controller for fuel-cell electric vehicles incorporating the hierarchical architecture

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APPLIED ENERGY 2025年 383卷

作者： Jiang, Zewei Hou, Zhuoran Chu, Liang Zhao, Di Jiang, Jingjing Yang, Jun Zhang, Yuanjian Jilin Univ Coll Automot Engn Changchun 130022 Peoples R China Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU England

In light of the severity of global climate change and the energy crisis, fuel cell electric vehicles (FCEVs) have garnered significant attention as a potential solution. Integrating advanced energy management strategies (EMSs) into FCEVs can effectively optimize the energy consumption performance of the highly nonlinear powertrain across various driving conditions. In this paper, a hierarchical explicit model predictive control energy management strategy (H-EMPC EMS) is proposed to enhance the economic performance of FCEVs and ensure control robustness across diverse driving conditions. Firstly, a hierarchical control architecture is developed to improve the adaptability of the EMS to diverse driving conditions. The upper layer performs driving conditions recognition and operation modes switching by observing the vehicle state. The lower layer determines power distribution throughout the powertrain. Secondly, an explicit MPC (eMPC) controller is developed to make the powertrain promptly respond to the power demand for the vehicle. To bolster the real-time application capability of the controller, systematic optimization is carried out by combining eMPC offline control law generation and online control law invocation. Thirdly, a feedback compensator is integrated into the controller to mitigate the effects of parameter variations on control and strengthen the robustness of the EMS. Finally, simulation evaluations and hardware-in-the-loop (HIL) tests demonstrate that the proposed H-EMPC EMS can improve economic performance and guarantee real-time performance for the studied FCEV. Compared with other baselines, the energy-saving capability is remarkable, showcasing its promising performance.

关键词： Energy management strategy explicit model predictive control Fuel cell electric vehicle Hierarchical control architecture Adaptive optimization

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explicit model predictive controller under uncertainty: An adjustable robust optimization approach

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JOURNAL OF PROCESS control 2019年 84卷 115-132页

作者： Tejeda-Iglesias, Manuel Lappas, Nikolaos H. Gounaris, Chrysanthos E. Ricardez-Sandoval, Luis Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

Conventional model predictive control (MPC) involves solving an optimization problem online to determine the control actions that minimize a performance criterion function. The high computational expense associated with conventional MPC may make its application challenging for large-scale systems. explicit MPC has been developed to solve the optimization problem offline. In this work, adjustable robust optimization is used to obtain the explicit solution to the MPC optimization problem offline for discrete-time linear time invariant systems with constraints on inputs and states. In the robust model formulation, an uncertain additive time-varying error is introduced to account for model uncertainty resulting from plant-model mismatch caused by un-measurable disturbances or process nonlinearities. The explicit solution is an optimal time-varying sequence of feedback control laws for the control inputs parameterized by the systems states. The control laws are evaluated in a time-varying manner when the process is online using state measurements. This study shows that the resulting control laws ensure the implemented control actions maintain the system states within their feasible region for any realizations of the uncertain parameters that are within the user-defined uncertainty set. Three case studies are presented to demonstrate the proposed approach and to highlight the benefits and limitations of this method. The proposed framework advances the development of explicit MPC by further expanding its application to large as well as nonlinear systems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： explicit model predictive control Adjustable robust optimization Robust control

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Real-time process safety and systems decision-making toward safe and smart chemical manufacturing

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DIGITAL CHEMICAL ENGINEERING 2025年 15卷

作者： Braniff, Austin Akundi, Sahithi Srijana Liu, Yuanxing Dantas, Beatriz Niknezhad, Shayan S. Khan, Faisal Pistikopoulos, Efstratios N. Tian, Yuhe West Virginia Univ Dept Chem & Biomed Engn Morgantown WV 26506 USA Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77845 USA Texas A&M Univ Texas A&M Energy Inst College Stn TX 77845 USA Texas A&M Univ Mary Kay OConnor Proc Safety Ctr Artie McFerrin Dept Chem Engn College Stn TX USA

The ongoing digital transformation has created new opportunities for chemical manufacturing with increasing plant interconnectivity and data accessibility. This paper reviews state-of-the-art research developments which offer the potential for real-time process safety and systems decision-making in the digital era. An overview is first presented on online process safety management approaches, including dynamic risk analysis and fault diagnosis/prognosis. Advanced operability and control methods are then discussed to achieve safely optimal operations under uncertainty (e.g., flexibility analysis, safety-aware control, fault-tolerant control). We highlight the connections between systems-based operation and process safety management to achieve operational excellence while proactively reducing potential safety losses. We also review the developments and showcases of digital twins paving the way to actual cyber-physical integration. Outstanding challenges and opportunities are identified such as safe data-driven control, integrated operability, safety and control, cyber- physical demonstration, etc. Toward this direction, we present our ongoing developments of the REal-Time Risk-based Optimization (RETRO) framework for safe and smart process operations.

关键词： Real-time optimization explicit model predictive control Dynamic risk analysis Fault diagnosis and prognosis Digital twin Cyber-physical integration

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An explicit model predictive controller for constrained stochastic linear systems 21st

An explicit model predictive controller for constrained stoc...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Desimini, Riccardo Prandini, Maria Politecn Milan Piazza Leonardo Da Vinci 32 I-20133 Milan Italy

In this paper we introduce an explicit model predictive controller (eMPC) for a linear system subject to an additive stochastic disturbance with bounded support. The finite horizon control problem that is solved to determine the eMPC consists in minimizing an average quadratic cost subject to robust linear constraints involving state and input. By resorting to a control law parametrization that is affine in the disturbance, the finite horizon control problem is reformulated as a convex quadratic optimization program and solved via multiparametric quadratic programming. The resulting eMPC is piecewise affine as a function of the state. The proposed approach is compared with an alternative min-max approach from the literature on a numerical example. Copyright (C) 2020 The Authors.

关键词： Constrained control explicit model predictive control stochastic linear systems

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A nonliner explicit model predictive controller for an Electric vehicle driven by a four-wheel Hub 7

A nonliner explicit Model Predictive Controller for an Elect...

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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Chu, Liang Li, Jinwei Liu, Ming Wang, Yilin Jiang, Zewei Guo, Chong The State Key Laboratory of Automotive Simulation and Control Jilin University Changchun China School of Automotive Studies Tongji University Shanghai China Changsha Automobile Innovation Research Institute Jilin University Changchun China

ISBN: (纸本)9798350340488

Nowadays, braking energy recovery technology has been widely integrated into new energy-electric vehicle electromechanical systems, which can extend the mileage of vehicles. However, coordinating electric braking and hydraulic braking to ensure an optimum balance between braking safety and energy recovery is a relevant problem to be worked out. This study proposes a coordinated control strategy for the electro-hydraulic braking system. First, an optimization distribution strategy of braking force is elaborated. Then, ca coordinated control strategy based on the explicit nonlinear model predictive controller(eNMPC) is designed to correct the braking torque aiming at guaranteeing stability and the energy regenerating efficiency simultaneously. Lastly, the analysis results manifest that the proposed strategy can increase the braking energy recovery efficiency by nearly 4.02% compared with the traditional strategies, highlighting its anticipated preferable performance. © 2023 IEEE.

关键词： electric vehicle electro-hydraulic coordinated braking explicit model predictive control

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An explicit model predictive controller for constrained stochastic linear systems

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IFAC-PapersOnLine 2020年第2期53卷 11386-11391页

作者： Riccardo Desimini Maria Prandini Politecnico di Milano Piazza Leonardo Da Vinci 32 20133 Milano Italy

关键词： Constrained control explicit model predictive control stochastic linear systems

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Design of Approximate explicit model predictive controller Using Parametric Optimization

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JOURNAL OF MECHANICAL DESIGN 2022年第12期144卷 124501-124501页

作者： Tsai, Ying-Kuan Malak, Richard J., Jr. Texas A&M Univ Dept Mech Engn Design Syst Lab College Stn TX 77843 USA

This paper introduces a new technique, called state-parameterized nonlinear programming control (sp-NLPC), for designing a feedback controller that can stabilize intrinsically unstable nonlinear dynamical systems using parametric optimization. Stability-preserving constraints are included in the optimization problem solved offline by the predictive parameterized Pareto genetic algorithm (P3GA), a constrained nonlinear parametric optimization algorithm. The optimal control policy is approximated from P3GA output using radial basis function (RBF) metamodeling. The sp-NLPC technique requires fewer assumptions and is more data-efficient than alternative methods. Two nonlinear systems (single and double inverted pendulums on a cart) are used as benchmarking problems. Performance and computational efficiency are compared to several competing control design techniques. Results show that sp-NLPC outperforms and is more efficient than competing methods. A qualitative investigation on phase plane analysis for the controlled systems is presented to ensure stability. The approximating state-dependent solution for the control input lends itself to applications of control design for control co-design (CCD). Such extensions are discussed as part of future work.

关键词： design optimization explicit model predictive control parametric optimization

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