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robust model predictive control for Uncertain Impulsive Positive System

INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2025年第3期23卷 757-766页

作者： Shi, Donglin Ai, Jun Feng, Yuchang Northeast Elect Power Univ Sch Automat Engn Jilin 132012 Peoples R China

This study delves into a robust model predictive control method for impulsive positive system, incorporating control input/state constraints and interval uncertain. Initially, a state feedback controller is devised, and sufficient conditions for ensuring positivity and stability of the closed-loop impulsive positive system with interval uncertain are obtained utilizing the linear copositive Lyapunov function and the linear cost function. Subsequently, linear programming techniques are applied to solve the optimization problem, yielding optimal control inputs. This designed control strategy ensures the system's positivity and stability while meeting control input/state constraints. Lastly, the validity of the approach is proved using numerical simulation examples.

关键词： Impulsive positive system interval uncertain linear programming robust model predictive control

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robust model predictive control for fractional-order descriptor systems with uncertainty

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FRACTIONAL CALCULUS AND APPLIED ANALYSIS 2024年第1期27卷 173-189页

作者： Arbi, Adnene Univ Carthage EPT LIM LR01ES13 Carthage Tunisia Univ Carthage ENSTAB Carthage Tunisia Univ Carthage Natl Sch Adv Sci & Technol Borj Cedria Dept Adv Sci & Technol Hammam Chott 1164 Tunisia

In this study, a new robust predictive control technique is investigated for uncertain fractional-order descriptor systems. Using the properties of fractional calculus and the construction of an appropriate Lyapunov function, the sufficient conditions to guarantee the existence of a robust predictive controller are given by minimizing the worst-case optimization problem. The new robust predictive controller is presented by using Shur complement, linear matrix inequality toolbox and cone-complement linear algorithm, and it is proved that the feasible solution of the optimization problem at the initial time can guarantee the admissibility of fractional-order descriptor closed-loop system.

关键词： robust model predictive control Fractional-order descriptor systems uncertainty Lyapunov function

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robust model predictive control for Trajectory Tracking of Robotic Manipulators Based on Fully Actuated System Approach 3

Robust Model Predictive Control for Trajectory Tracking of R...

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3rd Conference on Fully Actuated System Theory and Applications (FASTA)

作者： Yang, Yi Heng Zhang, Kai Chen, Zhi Hua Li, Bin Sichuan Univ Dept Aeronaut & Astronaut Chengdu 610065 Peoples R China Sichuan Univ Robot Satellite Key Lab Sichuan Prov Chengdu 610065 Peoples R China Beijing Inst Control Engn Beijing 100190 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

Multi-degree-of-freedom (DOF) manipulators demonstrate vast application potential in aerospace, medical, and agricultural domains. In this paper, the robust model predictive control (MPC) scheme based on fully actuated system approach (FASA) is proposed for trajectory tracking problem of robotic manipulators with state constraints on joint angle, angular velocity and control torque. First, to handle with the coupled dynamics nonlinearity, a FASA-based controller is adopted. Fortunately, the obtained closed-loop system is linear and concise, brings all the design degrees of freedom, which can be further optimized to satisfy the trajectory tracking performance and state constraints. Furthermore, in addressing the system uncertainties and state constraints, the robust model control scheme is proposed to optimize the FASA-based controller, and the optimization problem is transformed into a quadratic programming (QP) problem. Finally, numercial simulation is carried out to verify the effectiveness of proposed method.

关键词： Robotic Manipulators State Constraints Fully Actuated System Approach robust model predictive control

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robust model predictive control for Markovian jump systems under Round-Robin protocol

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INTERNATIONAL JOURNAL OF control 2022年第2期95卷 406-418页

作者： Zhang, Bin Song, Yan Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China

In this paper, a robust model predictive control problem is investigated for a class of Markovian jump systems with polytopic uncertainties. The Round-Robin protocol is utilised to schedule data transmission from the controllers to the actuator in order to reduce the burden of communication and the occupancy of network effects. Moreover, two kinds of transition probability matrices for the Markovian process are considered due to the practical application process, namely the one with polytopic uncertainties and the other with partially unknown entries. Sufficient conditions are established by means of the mixed-mode and token-dependent Lyapunov-like technique such that the closed-loop system is stable in the mean-square sense. In addition, a set of mixed-mode and token-dependent state feedback controllers are designed in terms of minimising a certain upper bound of the worst-case infinite horizon cost function. Finally, some simulation examples are implemented to verify the effectiveness of the proposed design scheme.

关键词： robust model predictive control Markovian jump systems networked control systems Round-Robin protocol partially uncertain transition probabilities

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robust model predictive control strategy for virtually coupled train sets using time-varying tubes 26

Robust model predictive control strategy for virtually coupl...

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IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

作者： Luo, Zhengwei Lv, Jidong Lv, Jiahui Liu, Hongjie Chai, Ming Li, Kaicheng Beijing Jiaotong Univ Natl Engn Res Ctr Rail Transportat Operat & Contr Beijing Peoples R China

ISBN: (纸本)9798350399462

Advanced Virtual Coupling (VC) technology can improve rail transit capacity by reducing train spacing. Due to the inevitable time-varying uncertainties of tunnel resistance and other disturbances, it is a great challenge to design an optimal controller for Virtually Coupled Train Set (VCTS), especially considering safety constraints and less conservative control performance. To overcome these problems, this paper proposes a novel robust model predictive control (MPC) strategy for VCTS based on time-varying tubes, which is different from the existing methods based on fixed size tubes. First, the VCTS system is modeled considering complex constraints and bounded disturbances. Secondly, on the basis of analyzing the influence of disturbances, the optimal control problem is constructed by using time-varying tubes for constraints tightening. Furthermore, a distributed robust MPC algorithm is proposed to realize cooperative operation control, and its recursive feasibility and input-to-state stability are proved. Finally, numerical simulations are provided to show that the time-varying tubes can obtain a larger feasible domain of the optimal control problem than the fixed size tubes, which is conducive to reducing the conservativeness of controller, and the proposed algorithm can deal with disturbances effectively.

关键词： Virtual coupling robust model predictive control Time-varying tubes Input-to-state stability

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Learning robust model predictive control for Voltage control of Islanded Microgrid

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 3021-3032页

作者： Kiani, Sahand Salmanpour, Ali Hamzeh, Mohsen Kebriaei, Hamed Penn State Univ Sch Elect & Comp Engn State Coll PA 16802 USA Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran *** Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran *** Iran

This paper proposes a novel control design for voltage tracking of an islanded AC microgrid in the presence of nonlinear loads and parametric uncertainties at the primary level of control. The proposed method is based on the Tube-Based robust model predictive control (RMPC), an online optimization-based method which can handle the constraints and uncertainties as well. The challenge with this method is the conservativeness imposed by designing the tube based on the worst-case scenario of the uncertainties. This weakness is amended in this paper by employing a combination of a learning-based Gaussian Process (GP) regression and Tube-Based RMPC. The advantage of using GP is that both the mean and variance of the loads are predicted at each iteration based on the real data, and the resulted values of mean and the bound of confidence are utilized to design the tube in Tube-Based RMPC. The theoretical results are also provided to prove the recursive feasibility and stability of the proposed learning based Tube-Based RMPC. Finally, the simulation results are carried out on both single and multiple DG (Distributed Generation) units.

关键词： Microgrids Uncertainty Voltage control Vectors Power system reliability Mathematical models Gaussian processes Gaussian process regression islanded microgrid robust model predictive control voltage control

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Distributed coordinated control for voltage regulation in active distribution networks based on robust model predictive control

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 166卷

作者： Song, Guocheng Wu, Qiuwei Jiao, Wenshu Lu, Lina Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Hangzhou Dianzi Univ Informat Engn Coll Hangzhou 311305 Zhejiang Peoples R China

As the integration of renewable energy sources increases, the uncertainty of wind power and photovoltaics bring new challenges to the voltage control problem of active distribution networks (ADNs). To address these challenges, this paper proposes a double-time-scale distributed voltage control strategy for ADNs based on robust model predictive control (RMPC), which considers the coordination between multiple voltage regulation devices. In the slow-time-scale control (STC), on-load Tap changers (OLTC), step voltage regulators (SVR), and capacitor banks (CBs) are optimized to minimize long-term voltage deviations and reduce tap operations of these traditional regulation devices. On this basis, in the fast-time-scale control (FTC), the active and reactive power outputs of distributed generators (DGs) are further optimized based on RMPC to regulate the fast voltage fluctuations while considering the uncertainty of DG outputs. The RMPC model is formulated as a minimum-maximum convex optimization problem, which is transformed into a quadratic programming problem. Moreover, by equivalently processing of adjacent control areas in ADNs, the distribution network model established based on voltage sensitivity method is decomposed to accelerate the solving process. The effectiveness of the proposed double-time-scale distributed RMPC voltage control scheme has been verified in a modified Italia 54-bus system. Results demonstrate that, compared to conventional deterministic centralized control, the proposed scheme achieves 63% reduction for the maximum voltage deviation.

关键词： Distributed generator Active distribution network robust model predictive control Distributed voltage control

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Automatic tuning of robust model predictive control in iterative tasks using efficient Bayesian optimization

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2024年第7期46卷 1362-1373页

作者： Tong, Junbo Du, Shuhan Fan, Wenhui Chai, Yueting Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ Dept Automat Main Bldg 601A Beijing 100084 Peoples R China

robust model predictive control (RMPC) is an effective technology for controlling uncertain systems while robustly handling constraints, and its closed-loop performance heavily relies on the selection of objective functions. However, the objective functions are typically chosen to be close to the real control objectives, despite an objective function that leads to less conservative constraints often provides better closed-loop performance. In this paper, we propose an automatic tuning framework for RMPC in iterative tasks. In particular, we parameterize RMPC and develop a Bayesian optimization (BO) method to tune it by solving a black-box optimization problem. We then introduce an efficient transfer learning framework within BO, which speeds up the searching process and enhances the controller performance. The effectiveness of the proposed tuning framework is illustrated on numerical examples.

关键词： robust model predictive control Bayesian optimization iterative learning control

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Safe and Optimal Motion Planning for Autonomous Underwater Vehicles: A robust model predictive control Framework Integrating Fast Marching Time Objectives and Adaptive control Barrier Functions

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DRONES 2025年第4期9卷 273-273页

作者： Tian, Zhonghe Chen, Mingzhi Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China

Autonomous Underwater Vehicles (AUVs) have shown significant promise across various underwater applications, yet face challenges in dynamic environments due to the limitations of traditional motion planning methods while Artificial Potential Field (APF)-based control barrier functions focus solely on obstacle proximity and distance-based methods oversimplify obstacle geometries, and both fail to ensure safety and satisfy turning radius constraints for under-actuated AUVs in intricate environments. This paper proposes a robust model predictive control (MPC) framework integrating an enhanced fast marching control barrier function, specifically designed for AUVs equipped with fully directional sonar systems. The framework introduces a novel improvement for moving obstacles by extending the control barrier function field propagation along the obstacle's movement direction. This enhancement generates precise motion plans that ensure safety, satisfy kinematic constraints, and effectively handle static and dynamic obstacles. Simulation results demonstrate superior obstacle avoidance and motion planning performance in complex scenarios, with key outcomes including a minimum safety margin of 1.86 m in cluttered environments (vs. 0 m for A* and FMM) and 1.76 m in dynamic obstacle scenarios (vs. 0.13 m for MPC-APFCBF), highlighting the framework's ability to enhance navigation safety and efficiency for real-world AUV deployments in unpredictable marine environments.

关键词： autonomous underwater vehicle motion planning fast marching method control barrier function robust model predictive control

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robust model predictive control with polytopic model uncertainty through System Level Synthesis

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AUTOMATICA 2024年 162卷

作者： Chen, Shaoru Preciado, Victor M. Morari, Manfred Matni, Nikolai Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA

We propose a robust model predictive control (MPC) method for discrete -time linear systems with polytopic model uncertainty and additive disturbances. Optimizing over linear time -varying (LTV) state feedback controllers has been successfully used for robust MPC when only additive disturbances are present. However, it is challenging to design LTV state feedback controllers in the face of model uncertainty whose effects are difficult to bound. To address this issue, we propose a novel approach to over -approximate the effects of both model uncertainty and additive disturbances by a filtered additive disturbance signal. Using the System Level Synthesis framework, we jointly search for robust LTV state feedback controllers and the bounds on the effects of uncertainty online, which allows us to reduce the conservatism and minimize an upper bound on the worst -case cost in robust MPC. We provide a comprehensive numerical comparison of our method and representative robust MPC methods from the literature. Numerical examples demonstrate that our proposed method can significantly reduce the conservatism over a wide range of uncertainty parameters with comparable computational effort as the baseline methods. (c) 2023 Published by Elsevier Ltd.

关键词： robust model predictive control System Level Synthesis robust control

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