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Concept and Strategies: Equivalent predictive control and Handle Point control for Bipedal-Vehicle Transformable Robots Under Various Disturbances

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13470-13484页

作者： Dong, Chencheng Yu, Zhangguo Chen, Xuechao Lai, Junhang Liu, Jiayi Li, Chao Huang, Qiang Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Minist Educ Key Lab Biomimet Robots & Syst Beijing 100081 Peoples R China Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

Bipedal-vehicle transformable robots (BVTRs), equipped with driving wheels, combine the flexibility of bipedal locomotion with the speed of wheeled movement. However, maintaining balance across different formations under various external disturbances remains a significant challenge due to uncertain disturbance types and dynamic shifts between formations. To address these challenges, this paper introduces the concept of Equivalent predictive control (EPC), which models all disturbances as unified virtual wrenches and integrates them directly into the robot's predictive control model, treated as an inertia-varying single rigid body. By anticipating the future impact of disturbances, EPC enhances stability and enables simultaneous handling of various disturbances. To address the challenge of dynamic changes, contact variations, and shifting constraints during formation transitions, we propose Handle Point control (HPC). HPC simplifies multi-task tracking by reducing joint space control to a set of virtual target points, called 'handle points', such as knees, hips, and shoulders. This method facilitates real-time formation switching by tracking different handle points. Experiments on the BVTR platform BHR8-2 validate the effectiveness of the proposed control strategies. Note to Practitioners-This paper addresses two critical challenges for applying BVTRs in real-world industrial scenarios: 1) managing various external disturbances, and 2) overcoming the complexities associated with changing dynamics and contact situations during formation transitions. The proposed EPC strategy unifies all disturbance types and integrates them into the robot's dynamic model, enhancing stability and adaptability across formations. The HPC method simplifies control by focusing on tracking key handle points, allowing smooth, real-time formation transitions without needing multiple optimization schemes. These methods can also be applied to other robotic systems that face similar control chal

关键词： Robots Robot kinematics Adaptation models predictive control Load modeling Vehicle dynamics predictive models Automation Vectors Torque Transformable robots predictive control whole-body control optimizing

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Autonomous station keeping of satellites in areostationary Mars orbit: A predictive control approach

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ACTA ASTRONAUTICA 2025年 230卷 1-15页

作者： Halverson, Robert D. Weiss, Avishai Lundin, Gabriel Caverly, Ryan J. Univ Minnesota Dept Aerosp Engn & Mech Minneapolis MN 55455 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA

The continued exploration of Mars will require a greater number of in-space assets to aid interplanetary communications. Future missions to the surface of Mars maybe augmented with stationary satellites that remain overhead at all times as a means of sending data back to Earth from fixed antennae on the surface. These areostationary satellites will experience several important disturbances that push and pull the spacecraft off of its desired orbit. Thus, a station-keeping control strategy must be put into place to ensure the satellite remains overhead while minimizing the fuel required to elongate mission lifetime. This paper develops a model predictive control policy for areostationary station keeping that exploits knowledge of non-Keplerian perturbations in order to minimize the required annual station-keeping 4v. The station-keeping policy is applied to a satellite placed at various longitudes, and simulations are performed for an example mission at a longitude of a potential future crewed landing site. Through careful tuning of the controller constraints, and proper placement of the satellite at stable longitudes, the annual station-keeping 4v can be reduced relative to a na & iuml;ve mission design.

关键词： Areostationary Mars orbit Station keeping predictive control Stationary satellite mission design Space vehicles

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Intent Prediction-Driven Model predictive control for UAV Planning and Navigation in Dynamic Environments

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4946-4953页

作者： Xu, Zhefan Jin, Hanyu Han, Xinming Shen, Haoyu Shimada, Kenji Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

Aerial robots can enhance construction site productivity by autonomously handling inspection and mapping tasks. However, ensuring safe navigation near human workers remains challenging. While navigation in static environments has been well studied, navigating dynamic environments remains open due to challenges in perception and planning. Payload limitations restrict the robots to using cameras with limited fields of view, resulting in unreliable perception and tracking during collision avoidance. Moreover, the rapidly changing conditions of dynamic environments can quickly make the generated optimal trajectory outdated. To address these challenges, this letter presents a comprehensive navigation framework that integrates perception, intent prediction, and planning. Our perception module detects and tracks dynamic obstacles efficiently and handles tracking loss and occlusion during collision avoidance. The proposed intent prediction module employs a Markov Decision Process (MDP) to forecast potential actions of dynamic obstacles with the possible future trajectories. Finally, a novel intent-based planning algorithm, leveraging model predictive control (MPC), is applied to generate navigation trajectories. Simulation and physical experiments demonstrate that our method improves the safety of navigation by achieving the fewest collisions compared to benchmarks.

关键词： Trajectory Vehicle dynamics Navigation Planning Dynamics Collision avoidance Autonomous aerial vehicles predictive control Detectors Heuristic algorithms Aerial systems: Perception and autonomy integrated planning and control RGB-D perception

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Rule-Based predictive control for Battery Scheduling in Microgrids Under Power Generation and Load Uncertainties

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9782-9792页

作者： Kaheni, Mojtaba Fu, Jiali Papadopoulos, Alessandro V. Malardalen Univ Div Intelligent Future Technol S-72123 Vasteras Sweden Swedish Natl Rd & Transport Res Inst VTI S-11428 Stockholm Sweden

This paper addresses the control of the state of charge (SoC) of a Battery Energy Storage System (BESS) in a microgrid, considering uncertainties in load and Renewable Energy Sources (RES) generated power estimations. To achieve this objective, we propose RubPC, a novel rule-based Model predictive control (MPC). We partition the feasible operation space of the microgrid into two subzones, referred to as the white and yellow zones. The yellow zone represents the boundary space between the feasible and unfeasible operation spaces. In RubPC, we initially implement MPC on a predefined optimization window to determine the optimal SoC of the BESS, aiming to keep the microgrid within the white zone. Noting that mismatches between estimated and actual load and generated power may lead to constraint violations, we introduce a rule-based controller as a supervisory control. This controller monitors the microgrid's state, and if the microgrid enters the yellow zone, it adjusts the control to maintain the microgrid within the white zone. We validate our proposed method by simulating it using data from an electrified quarry site in Sweden.

关键词： Microgrids Uncertainty Discharges (electric) Optimal scheduling Robustness predictive control Job shop scheduling Industries Estimation Costs Battery energy storage system (BESS) demand-side management (DSM) model predictive control (MPC) optimization rule-based control

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Asynchronous Distributed Model predictive control for Multi-Agent Systems

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IEEE ACCESS 2025年 13卷 9592-9605页

作者： Mi, Xiaoxiao Liao, Yuanjiang Zeng, Hongzheng Ningbo Univ Technol Robot Inst Ningbo 315211 Peoples R China Civil Aviat Flight Univ China Key Lab Flight Tech & Flight Safety Civil Aviat Adm China Mianyang 621052 Peoples R China

This paper primarily focuses on the asynchronous cooperation control problem of multi-agent systems with external disturbances. Here, all agents take control actions, such as sampling measurements, computing control inputs, and exchanging information based on their individual clocks rather than a global synchronized clock. Owing to the asynchronous nature, the information of each agent received by all its neighbors at the same time instant may be different and deviate from what this agent predicts, potentially resulting in poor decision-making and even instability. To ensure stability, dynamic compatibility constraints related to all the uncertain deviations are established and incorporated into the optimization problem. Additionally, the constraint restriction method is employed to satisfy physical constraints and optimize performance in the presence of external perturbations. Then, an asynchronous distributed model predictive control with a dual-mode strategy is developed and sufficient conditions on the design parameters are constructed to ensure recursive feasibility and system stability. Finally, simulation experiments on a multiple-vehicle system with asynchronous control actions are carried out to validate the theoretical results.

关键词： Multi-agent systems Optimization Formation control Vectors predictive control Clocks Trajectory Synchronization Sufficient conditions Stability criteria Asynchronous sampling cooperative control distributed model predictive control formation control multi-agent system

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Discrete-Time Integral Fast Terminal Sliding Mode predictive control for Dynamic Positioning Ships With Lumped Uncertainties and Input Constraints

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第5期35卷 1763-1775页

作者： Deng, Fang Yang, Hualin Ding, Qiang Li, Boyang Qingdao Univ Sci & Technol Coll Mech & Elect Engn Qingdao Peoples R China

This paper proposes a robust discrete-time integral fast terminal sliding mode predictive control (DIFTSMPC) scheme for DP ship trajectory tracking under the consideration of unmodeled dynamics, environmental disturbance, and input constraints. An improved single closed-loop control strategy is designed by adopting the earth-fixed reference velocity as the virtual velocity. The improved nonlinear PID discrete-time integral fast terminal sliding mode is designed to achieve faster convergence. The unknown nonlinear dynamics and the environmental disturbance are combined as a lumped uncertainty term and estimated using the one-step delay observation method. Then the corrected sliding model state is optimized to track the reference sliding reaching law in the prediction horizon. The lumped uncertainty estimation is integrated into the sliding mode prediction to guarantee the robustness of the control scheme. The stability of the proposed scheme has been verified. Comparison results demonstrate the effectiveness and superiority in chattering suppressing and constraint handling of the proposed scheme.

关键词： dynamic positioning ship integral fast terminal sliding mode lumped uncertainty predictive control trajectory tracking

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An Advanced Model predictive control Method Based on Discrete Space Vector Modulation for Reduced Switch Counts Three-Level Converter

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第4期72卷 3884-3895页

作者： Jing, Mengmeng Chen, Zhiyuan Xing, Xiangyang Chen, Alian Zhang, Chenghui Blaabjerg, Frede Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China Aalborg Univ AAU Energy Aalborg Denmark

The existing model predictive control (MPC) method effectively suppresses common mode voltage (CMV) of the reduced switch counts three-level converter (RSC-TLC), but it cannot properly deal with the problem of neutral point (NP) voltage balance, fixed switching frequency (FSF) and low current ripples simultaneously. To overcome these obstacles, in this article, an advanced MPC method based on discrete space vector modulation (DSVM) for the RSC-TLC is first proposed. First, to reduce the CMV of the RSC-TLC, the CMV amplitudes of all acting voltage vectors are limited to be less than one sixth of dc-link voltage. Second, based on the switching characteristics of the RSC-TLC and the principle of DSVM, numerous virtual vectors are introduced and served as candidates to track the reference current. Third, considering NP voltage balance, the proper switching sequences with adjustable switching time are adopted to output the optimal virtual vector, which has the smallest current tracking error. Consequently, NP voltage balance and FSF with reduced current ripples are achieved to improve current quality. Further-more, only three candidate vectors are evaluated in each predictive step, which avoids evaluating extensive virtual vectors. Meanwhile, the coordinates of candidate vectors and the switching time for both coupled module and independent module are obtained by simple algebraic calculations under gh coordinate system, so that complex trigonometric functions are avoided. As a result, the proposed MPC method is computationally efficient. Finally, the simulation and experimental results verified feasibility and correctness of the proposed MPC method. Remarkably, for the proposed MPC method, the current harmonics are less than 4%, the CMV amplitude is reduced by 50% without significant increase of calculation burden compared with the conventional MPC method.

关键词： Vectors Switches Voltage control predictive control Harmonic analysis Aerospace electronics Pulse width modulation Common mode voltage (CMV) reduction discrete space vector modulation (DSVM) fixed switching frequency (FSF) low calculation burden low current ripples model predictive control (MPC) three-level converter (TLC)

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Calibration of parameters and predictive control strategy of a wind turbine for improvement of energy harvesting

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2025年

作者： Aljundi, Abdulghani Mohamed, Omar Abu Elhaija, Wejdan Princess Sumaya Univ Technol King Abdullah I Sch Grad Studies & Sci Res Dept Elect Engn Amman Jordan

Applied mathematical modeling of the various energy resources has contributed widely in their design and operation improvements. Among renewable energy sources, wind energy systems have gained a significant attention for their rapid and continuous growth, which resulted from their cost effectiveness and clean power generation. However, research effort on the industry and university levels is still needed to enhance the characteristics of existing wind turbines and allow larger amounts of their share. This thesis presents an accurate model of a wind turbine (TWT-1.65) system for time-based dynamic simulations. The parameters of the power coefficient have been identified, calibrated and verified using different meta-heuristic optimization techniques, which are the Wild Horse Optimizer (WHO), Whale Optimizer (WO), and Genetic Algorithm (GA) to enhance the model accuracy over the previously related studies. The new version of the parameters has resulted in a higher accuracy while being practically meaningful, with the best achieved MSE of 0.006. The system model has been then integrated with two predictive controllers which are the linear Model predictive control (MPC) and Neural Network predictive control (NNPC) to regulate the pitch angle trends for the target of maximum energy harvesting. With proper selection of the controllers' parameters, off-line simulation studies have shown improved production trends of the power output with constrained and safe trajectory of the pitch angle, which can be translated to be an improve in the total average harvested power of 34% and 44.5% over the complete time window of 5760 min using NNPC and Linear MPC respectively.

关键词： Modeling identification calibration wind turbine wild horse optimizer predictive control

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Jointed Observer-Based Sliding Mode predictive control for Interconnected Power Systems With Input Delays

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 480-488页

作者： Wang, Yingchun Liu, Yang Yu, Xiaoqi Zhang, Huaguang Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China State Grid Weifang Power Supply Co Weifang 261000 Peoples R China

This paper is concerned with the sliding mode predictive control problem for the load frequency control (LFC) of interconnected power systems with input delays and load disturbance. To deal with input delays, unknown disturbance, the interconnected influence existing simultaneously in power systems, the state-observer and disturbance-observer are respectively designed by employing the reduction transformation technique for state observer and introducing the sliding mode surface for the extended disturbance observer. Then the jointed observers based integral sliding mode controller with active disturbance compensation is designed. Furthermore, by constructing a novel Lyapunov function and matrix inequality technique, the sufficient conditions of exponential stability are provided for the closed-loop observer error systems in linear matrix inequality form, which can be solved by linear matrix inequality (LMI) toolbox in Matlab. Simulation is given to verify the effectiveness of the proposed approach.

关键词： Delays Power system stability Frequency control predictive control 1/f noise Linear matrix inequalities Disturbance observers Interconnected power systems load frequency control sliding mode predictive control extended disturbance observer input delay

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Self-Organizing Model predictive control for Constrained Nonlinear Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第1期55卷 501-512页

作者： Han, Hong-Gui Wang, Yan Sun, Hao-Yuan Liu, Zheng Qiao, Jun-Fei Beijing Univ Technol Sch Informat Sci & Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China

Model predictive control (MPC) is a practical method for addressing control issues in constrained systems. System identification and constrained optimization are two key problems that affect MPC performance. In this work, a self-organizing MPC (SOMPC) strategy is proposed for constrained nonlinear systems with unknown dynamics to achieve constraint satisfaction and improve control performance. First, the generalized multiplier method is introduced into the MPC framework to redesign the objective function. In this way, the constrained optimal control problem is reconstructed into an easily solvable unconstrained optimal problem. Second, a self-organizing fuzzy neural network (SOFNN) is adopted to identify unknown nonlinear system. Then, the performance of SOFNN is optimized by parameter updating and structure self-organization to provide accurate prediction output. Third, the gradient descent algorithm is utilized to solve nonlinear optimization problem of MPC to obtain control input. To ensure practical application, the convergence of SOFNN, the feasibility and stability of SOMPC strategy are proved. Finally, the proposed SOMPC strategy is demonstrated by a numerical experiment and an industrial process control simulation experiment, and the results show that it exhibits outstanding control performance and constraint satisfaction ability.

关键词： Fuzzy control Optimization Fuzzy neural networks Artificial neural networks Nonlinear dynamical systems control systems Accuracy predictive control Vectors Linear programming Fuzzy neural network (FNN) input and output constraints model predictive control (MPC) unknown nonlinear systems (UNSs)

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