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Sparse Gaussian process-based strategies for two-layer model predictive control in autonomous vehicle drifting

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 174卷

作者： Hu, Cheng Xie, Yangyang Xie, Lei Magno, Michele Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Swiss Fed Inst Technol Ctr Project Based Learning D ITET CH-8092 Zurich Switzerland

Vehicle safety is paramount in autonomous driving, particularly when managing vehicles at extreme side-slip angles-a challenge often overlooked by conventional controllers. Recent studies have focused on vehicle drift control under such extreme conditions. However, tracking complex trajectories while drifting is challenging, especially in the presence of a model mismatch. This paper proposes a two-layer model predictive controller based on sparse variational Gaussian processes. The first layer is responsible for computing the optimal drift equilibrium points, while the second layer is tasked with tracking these points. A variational free energy-based Gaussian process is utilized to compensate for errors in the upper-layer drift equilibrium point calculations and mismatches in the lower-layer controller model. Moreover, the vehicle's state is determined to be either in transit drift or deep drift based on whether the slip angle and steering angle have reached critical values. Gaussian models are established for each state to enhance prediction accuracy. The effectiveness of the controller is demonstrated through joint simulations on MATLAB and CarSim platforms. First, the proposed two-layer model predictive controller was compared with three state-of-the-art drift controllers, demonstrating at least a 48.64% reduction in average lateral error when tracking trajectories with varying curvature. Second, when combined with sparse Gaussian processes, the controller's learning ability was validated in scenarios with a 5% to 20% friction coefficient mismatch. Specifically, in the scenario with a 20% friction coefficient mismatch, its average lateral error was reduced by 95.09% after model error learning. Additionally, the controller was compared with both Fully Independent Training Conditional (FITC) GP-based MPC and Full GP-based MPC controllers, demonstrating better trajectory tracking capability and model error learning ability.

关键词： Autonomous drift model predictive control Learning based controller Sparse Gaussian process

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A model predictive control to Improve Grid Resilience

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ENERGIES 2025年第7期18卷 1865-1865页

作者： Young, Joseph Wilson, David G. Weaver, Wayne Robinett, Rush D. OptimoJoe Houston TX 77254 USA Sandia Natl Labs Elect Sci Albuquerque NM 87185 USA Michigan Technol Univ Mech & Aerosp Engn Houghton MI 49931 USA

The following article details a model predictive control (MPC) to improve grid resilience when faced with variable generation resources. This topic is of significant interest to utility power systems where distributed intermittent energy sources will increase significantly and be relied on for electric grid ancillary services. Previous work on MPCs has focused on narrowly targeted control applications such as improving electric vehicle (EV) charging infrastructure or reducing the cost of integrating Energy Storage Systems (ESSs) into the grid. In contrast, this article develops a comprehensive treatment of the construction of an MPC tailored to electric grids and then applies it integration of intermittent energy resources. To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. First, the algorithm is compared to an existing MPC code and the results are verified by a numerically precise simulation. It is shown that this algorithm produces a control comparable to existing algorithms and the behavior of the control carefully respects the bounds specified. Second, the MPC is applied to a small nine bus system that contains a mix of turbine-spinning-machine-based and intermittent generation in order to demonstrate the algorithm's utility for resource planning and control of intermittent resources. This study demonstrates how the MPC can be tuned to change the behavior of the control, which can then assist with the integration of intermittent resources into the grid. The emphasis throughout the paper is to provide systematic treatment of the topic and produce a no

关键词： model predictive control optimal control power grid microgrid pseudospectral method orthogonal spline collocation

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Hierarchical single-objective variable double voltage vector model predictive control with low computational burden for cascaded H-bridge multilevel converter

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2025年第2期53卷 1118-1134页

作者： Han, Jingang Li, Xiangyu Tang, Tianhao Shanghai Maritime Univ Inst Elect Drives & Control Syst Shanghai Peoples R China

In this paper, a single objective variable double voltage vector model predictive control (SOV-DVVMPC) to lower the computational burden for single-phase cascaded H-bridge (CHB) converters is proposed. The proposed algorithm is based on a hierarchical structure to control the grid-connected current and capacitor voltage without a weighting factor. Firstly, layer I controls the grid-connected current to select the optimal region, the cost function related to the voltage region is designed and the optimal state is calculated directly by analyzing the linear relationship between the divided region and its adjacent voltage levels, which can reduce the computational burden. Correspondingly, the optimal vectors are symmetrically distributed over the entire control cycle by a modulation principle, fulfilling the fixed switching frequency (FSF) of the system. Then, layer II uses redundant switching states to maintain capacitor voltage balancing. Finally, a single-phase CHB converter experiment setup is constructed, the results show that compared with DVV-MPC, the execution time of SOV-DVVMPC is reduced by 26.4%, and compared with traditional MPC, the switching frequency is fixed and the output current quality is improved. The method aims to reduce the computational burden of model predictive control of cascaded H-bridge multilevel converters. It uses a double-vector voltage control approach to achieve a fixed switching frequency, and a hierarchical structure to control the grid current and dc-side output capacitor voltages. image

关键词： fixed switching frequency H-bridge converter low computational burden model predictive control single objective variable

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model-plant mismatch diagnosis using plant model ratios for a grinding mill circuit under model predictive control

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MINERALS ENGINEERING 2025年 227卷

作者： Mittermaier, Heinz K. le Roux, Johan D. Craig, Ian K. Mintek Measurement & Control Div Johannesburg South Africa Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa

model-based controllers often extend improved performance to mineral processing plants by leveraging predictive models to account for system dynamics, handling constraints, adapting to changing conditions, and optimizing control inputs. Inaccurate models will cause a deterioration of controller performance, which is often the case for grinding mill circuits. The plant model ratio was developed to diagnose parametric model plant mismatches for first-order plus time delay models. Using a simulation study, the plant model ratio is applied to test the feasibility of using the plant model ratio on a grinding mill circuit. By applying different scenarios of mismatch, some limitations of the plant model ratio are identified and discussed in light of a grinding mill circuit model that is used in model-based controllers. The plant model ratio is capable of identifying parametric model plant mismatches for the model of a grinding mill circuit, specifically changes in the direction of responses. This may occur in cases where disturbances push a grinding mill to operate to the right of the peak of a grind curve.

关键词： controller performance monitoring Grinding mill circuit model predictive control model-plant mismatch Process performance monitoring

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Auto-tuning strategy for deep Koopman robust model predictive control design based on advanced Metaheuristics

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NEUROCOMPUTING 2025年 638卷

作者： Meng, Zijia Xiao, Yu Xu, Xiaodong Luo, Biao Du, Yuncheng Gui, Weihua Cent South Univ Sch Automat Changsha 410006 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada Univ Houston Dept Biomed Engn Houston TX USA

Nonlinear systems are challenging to control due to their complexity and unpredictability, with existing methods often struggling to balance accuracy and computational efficiency. This paper addresses these challenges by proposing an auto-tuning strategy that integrates Koopman operator theory with model predictive control (MPC), optimized using metaheuristic algorithms. We use deep neural networks to learn the Koopman embedding function and operator, transforming the nonlinear system into an equivalent linear system in the embedding space. To enhance rapid setpoint tracking, metaheuristic algorithms automatically tune MPC parameters within the Koopman framework. Additionally, acknowledging deviations between the Koopman model and the actual nonlinear system, we implement robust constraint-tightening methods to ensure stable convergence of the system's state to the setpoint within a bounded error. Simulation results using a nonlinear iron removal process validate the effectiveness of the proposed approach. While this study focuses on offline optimization, the proposed framework can be extended to real-time applications in future work, addressing challenges like computational efficiency and model accuracy.

关键词： Nonlinear systems Deep Koopman operator Metaheuristic algorithms model predictive control Auto-tuning

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Single Solution Sphere-Decoding Algorithm model predictive control for High-Current Applications

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 406-415页

作者： Terlizzi, Cristina Bifaretti, Stefano Lampasi, Alessandro Univ Roma Tor Vergata Dept Ind Engn I-00133 Rome Italy ENEA I-00044 Frascati Italy DTT SCarl I-00044 Frascati Italy

This paper presents a Sphere-Decoding algorithm (SDA) model predictive control (MPC) for a parallel-connected H-Bridges Power Supply (PS). The proposed converter topology faces the very high current peaks (tens of kiloamperes) required by Central Solenoid coils of the Divertor Tokamak Test (DTT) facility for nuclear fusion, quite unusual in industry applications. The choice of the control strategy aims at exploiting the very fast transient response of MPC over linear control schemes and the computational burden reduction of SDA. As a result, this approach is able to guarantee a low load current tracking error and an effective current sharing among H-Bridges, thus proper operations for tens of years. In order to implement the SDA-MPC on a FPGA-based control board, fast but characterized by limited memory, the mathematical model of the PS is first introduced and the SDA-MPC procedure is then mathematically modified to find a single optimized solution. This simplification guarantees a remarkable reduction of the computational burden, avoiding the analysis of a set of possibilities, without losing in control effectiveness. Its performances are verified through simulations and experimentally validated with Hardware-In-the-Loop and prototype tests.

关键词： Mathematical models Coils Tokamak devices Legged locomotion Topology predictive control Prediction algorithms Current measurement Vectors Load modeling Divertor tokamak test facility equivalent model hardware-in-the-loop high-current power converters model predictive control parallel converters

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A fuzzy logic dispatching method based on model predictive control for adaptively addressing uncertain operation scenarios in multi-energy systems

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ENERGY 2025年 322卷

作者： Ma, Changwei Dong, Wei Shi, Yongtao Zhang, Fan Yang, Qiang Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

The integrated energy system (IES) effectively combines wind, solar, natural gas, and other resources to efficiently meet the diverse energy demands of cooling, heat, and electricity. Given the inherent randomness and intermittency of multi-energy sources and loads, achieving optimal conversion and allocation between different energy flows during IES operation remains a significant challenge. A sophisticated energy management approach that achieves long-term optimization under operating uncertainties is imperative. In this paper, a two-stage IES real-time dispatching method based on fuzzy logic and model predictive control (MPC) framework is proposed. In the day-ahead stage, utilizing the potential operating scenario constructed by the interval prediction boundary curve, the knowledge parameters of evolutionary fuzzy inference system (EFIS) are optimized using economic cost as the objective. This day-ahead stage can obtain the set of EFIS models tailored to different scenarios for IES operation dispatching. In the intra-day stage, based on the rolling prediction information in MPC framework, the real-time decision model is dynamically alternated from the pre-obtained EFIS models to enhance adaptability to varying operating patterns under multi-uncertainties. Compared with the three benchmark methods, the proposed method can save 14.1 %, 7.1 % and 5.3 % of the system operating costs, respectively. The simulation results demonstrate that the proposed method can effectively address the dispatching of FIS among multiple energy flows, thereby enhancing the operational efficiency of the system under multi-uncertainties.

关键词： Integrated energy system Energy management Real-time dispatching Evolutionary fuzzy inference system model predictive control

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Nonlinear model predictive control of an active dry friction damper for rotor vibration suppression

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NONLINEAR DYNAMICS 2025年第14期113卷 17821-17854页

作者： Jiang, Minghong Gao, Xianghong Zhang, Peng Zhu, Changsheng Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

Active dry friction dampers (ADFD) could effectively suppress rotor vibration via nonlinear dry friction effects. Previous studies demonstrated that an optimum normal force exists for the ADFD to minimize rotor vibrations. However, this optimum normal force is significantly influenced by rotational speeds or unbalanced forces, making one optimum design probably not effective in off-design conditions. To address this problem, a nonlinear model predictive control (MPC) algorithm is developed to execute online optimization of normal forces. The proposed MPC utilizes a specially designed augmented Kalman filter to estimate residual unbalances during operation. Based on the estimated residual unbalances, a rotation-speed-dependent prediction horizon is proposed to perform online normal force optimization with limited computational resources. Finally, an iterative algorithm is introduced to solve the nonlinear optimal control problem defined in the rotation-speed-dependent prediction horizon. Numerical and experimental investigations are performed to demonstrate the advantages of the proposed MPC. It is shown that the proposed MPC could adaptively increase the ADFD's normal forces if the rotor responses are too large while keeping the ADFD's normal force at a lower level when the rotor responses are relatively small. This makes the ADFD's vibration suppression performance relatively robust to changes in rotational speeds or unbalanced forces.

关键词： Active dry friction damper Rotor Vibration suppression model predictive control Augmented Kalman filter

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UTILIZING LINEAR QUADRATIC REGULATOR AND model predictive control FOR OPTIMIZING THE SUSPENSION OF A QUARTER CAR VEHICLE IN RESPONSE TO ROAD EXCITATION

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JOURNAL OF THEORETICAL AND APPLIED MECHANICS 2025年第1期63卷 75-89页

作者： Bajrami, Xhevahir Shala, Ahmet Likaj, Rame Krasniqi, Drin Shala, Erjon Univ Prishtina Fac Mech Engn Dept Mechatron Prishtina Kosovo

Vehicle suspension systems are fundamental components designed to mitigate the adverse effects of road surface irregularities. These systems are typically categorized as passive, semi-active, or active suspensions. This study focuses on a quarter car suspension model to explore the application of two control methods, the Linear Quadratic Regulator (LQR) and the model predictive control (MPC). Experimental data are collected using the Quanser active suspension experiment setup. Initially, the LQR controller is employed to optimize performance criteria related to the system state and input signals. Subsequently, the widely recognized MPC approach is used as an alternative control method. A comprehensive comparative analysis is conducted, taking into account various load conditions and parameter variations. Additionally, the study investigates system responses under varying road conditions, changes in plant characteristics, and the introduction of disturbances, to provide an exhaustive comparison of the two control methods. The results obtained with the MPC and the comparison with the findings of various authors to date allow us to emphasize that the presented results in this study significantly outperform the previous work. These outcomes have undergone rigorous validation on the physical model available in our mechatronics laboratory.

关键词： vehicle suspension quarter car model linear quadratic regulator model predictive control experimental analysis

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Practical considerations for implementing robust-to-early termination model predictive control

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SYSTEMS & control LETTERS 2025年 196卷

作者： Amiri, Mohsen Hosseinzadeh, Mehdi Washington State Univ Sch Mech & Mat Engn Pullman WA 99164 USA

model predictive control (MPC) is widely used to achieve performance objectives, while enforcing operational and safety constraints. Despite its high performance, MPC often demands significant computational resources, making it challenging to implement in systems with limited computing capacity. A recent approach to address this challenge is to use the Robust-to-Early Termination (REAP) strategy. At any time instant, REAP converts the MPC problem into the evolution of a virtual dynamical system whose trajectory converges to the optimal solution, and provides guaranteed sub-optimal and feasible solution whenever its evolution is terminated due to limited computational power. REAP has been introduced as a continuous-time scheme and its theoretical properties have been derived under the assumption that it performs all the computations in continuous time. However, REAP should be practically implemented in discrete-time. This paper focuses on the discrete-time implementation of REAP, exploring conditions under which anytime feasibility and convergence properties are maintained when the computations are performed indiscrete time. The proposed methodology is validated and evaluated through extensive simulation and experimental studies.

关键词： model predictive control Robust-to-early termination Discrete-time implementation Limited computing capacity

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