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学者姓名：陈宇韬
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Abstract ：
Wave Energy Converters (WECs) deployed in an array can reduce investment and operational costs, such as infrastructures, operations, and maintenance. Control for WEC arrays becomes more complex due to the dynamic behaviour of individual WECs and the interactions between neighbouring devices, including their defective and radiating effects, which impact the overall system performance. This paper investigates nonlinear model predictive control (NMPC) approaches for controlling an array of wave energy converters (WECs) to extract the maximum amount of energy while respecting safety constraints. Three schemes are benchmarked, namely: (i) Centralised NMPC, where the array is considered as an augmented system; (ii) Independent NMPC, where each WEC is considered independently, neglecting all interactive effects; and (iii) Distributed NMPC (D-NMPC), where a WEC only considers the interactive effects of its nearest neighbour. The proposed approaches are demonstrated and benchmarked through comparative simulation studies based on an array of homogeneous point absorbers. Simulation results highlight the limitations of the independent and centralised approaches and reveal the potential of D-NMPC in achieving most of the performance of C-NMPC, with a computational burden similar in scale to D-NMPC.
Keyword ：
Arrays Arrays Distributed Control Distributed Control Model Predictive Control Model Predictive Control Wave Energy Converters Wave Energy Converters
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| GB/T 7714 | Zhan, Siyuan , Chen, Yutao , Lan, Jianglin et al. Energy Maximisation Control for an Array of Wave Energy Converters - A Distributed Approach [J]. | 2024 UKACC 14TH INTERNATIONAL CONFERENCE ON CONTROL, CONTROL , 2024 : 31-36 . |
| MLA | Zhan, Siyuan et al. "Energy Maximisation Control for an Array of Wave Energy Converters - A Distributed Approach" . | 2024 UKACC 14TH INTERNATIONAL CONFERENCE ON CONTROL, CONTROL (2024) : 31-36 . |
| APA | Zhan, Siyuan , Chen, Yutao , Lan, Jianglin , Zhang, Yao . Energy Maximisation Control for an Array of Wave Energy Converters - A Distributed Approach . | 2024 UKACC 14TH INTERNATIONAL CONFERENCE ON CONTROL, CONTROL , 2024 , 31-36 . |
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Cooperative trajectory planning of aerial-ground systems is a fundamental and challenging problem, which aims to leverage the aerial information to assist the ground tasks. Existing methods often suffer from suboptimal trajectories or computation burden. In this paper, we address cooperative trajectory planning of aerial-ground systems in which an unmanned ground vehicle (UGV) plans its local trajectory in real-time with the assistance of an unmanned aerial vehicle (UAV). Firstly, the UAV generates guidance trajectory using nonlinear model predictive control (NMPC), which considers the obstacle distribution density as a factor reflecting the coupling effect of multiple obstacles on the UGV, thereby avoiding local minima problem and improving the feasibility of the planned trajectory. Secondly, a null-space-based behavioral control (NSBC) framework is employed to merge the guidance trajectory into the UGV's own planned one as a task. Finally, an event triggering task supervisor is developed for the UGV to decide the priorities of all tasks, which reduces the switching frequency of task priorities brought by traditional rule-based task supervisors. Both simulation and experiment results show that the proposed approach has superior trajectory planning performance in terms of trajectory error, on-line computation time and the success rate of task execution.
Keyword ：
Aerial-ground systems Aerial-ground systems Behavioral control Behavioral control Model predictive control Model predictive control Trajectory planning Trajectory planning
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| GB/T 7714 | Huang, Jie , Chen, Jianfei , Zhang, Zhenyi et al. On Real-time Cooperative Trajectory Planning of Aerial-ground Systems [J]. | JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS , 2024 , 110 (1) . |
| MLA | Huang, Jie et al. "On Real-time Cooperative Trajectory Planning of Aerial-ground Systems" . | JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 110 . 1 (2024) . |
| APA | Huang, Jie , Chen, Jianfei , Zhang, Zhenyi , Chen, Yutao , Lin, Dingci . On Real-time Cooperative Trajectory Planning of Aerial-ground Systems . | JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS , 2024 , 110 (1) . |
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This paper studies control strategies of intelligent vehicles at unsignalized intersections. By considering the interaction among multiple vehicles and communication disturbances, the problem is formulated as a robust differential game in which the controlled vehicle computes actions using disturbed information of locally neighboring vehicles. Both cooperative and non-cooperative differential game models are considered. The controlled vehicle optimizes its own cost in the non-cooperative game while coordinates its strategy with other vehicles to optimize a joint cost in the cooperative game. We show that the local optimal strategy of each intelligent vehicle defined by a single optimization problem converges to global robust Nash equilibrium in the non-cooperative game, and converges to the global robust Pareto-Nash equilibrium in the cooperative game. The effectiveness of the robust differential game method is verified by simulations under two scenarios. Results show that under the proposed differential game approach, vehicles pass through unsignalized intersections more quickly than methods using traditional discrete game approaches. In addition, vehicles can avoid colliding with each other and obstacles at the presence of communication disturbances.
Keyword ：
Costs Costs differential game differential game Differential games Differential games Games Games game theory game theory Nash equilibrium Nash equilibrium Optimal control Optimal control robust Nash equilibrium robust Nash equilibrium Unsignalized intersection Unsignalized intersection Vehicle dynamics Vehicle dynamics Vehicular ad hoc networks Vehicular ad hoc networks
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| GB/T 7714 | Huang, Jie , Wu, Zhihong , Xue, Wenyan et al. Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 , 25 (8) : 9535-9549 . |
| MLA | Huang, Jie et al. "Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 25 . 8 (2024) : 9535-9549 . |
| APA | Huang, Jie , Wu, Zhihong , Xue, Wenyan , Lin, Dingci , Chen, Yutao . Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 , 25 (8) , 9535-9549 . |
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Energy maximising (EM) control of wave energy converters (WECs) is a noncausal problem, where wave prediction information can be used to increase the energy conversion rate significantly. However, current approaches do not consider the prediction error evolution in the control formulation process, leading to potential unpredictable performance degradation. Moreover, most existing real-time WEC control approaches assume linear dynamics, motivated by their simplicity and mild computational cost and, thus, are not effective for real-time control for WECs with nonlinear dynamics. Targeting imperfect wave prediction and nonlinear WEC dynamics, this paper proposes a computationally-efficient nonlinear MPC (NMPC) scheme for WECs with (typically) imperfect wave excitation preview. This is achieved by introducing an input move blocking scheme when formulating and solving the online optimisation problem, i.e., defining finer discretisation grids for the control input and wave prediction at the early stages of the prediction horizon, where the wave prediction is more accurate, and coarser grids at the latter stages of the horizon, to reflect less inaccurate wave prediction information. Numerical simulation results are presented, based on a conceptual nonlinear point-absorber WEC, to verify the efficacy of the proposed NMPC method, in terms of produced energy, computational complexity, and robustness against wave prediction inaccuracy.
Keyword ：
Nonlinear model predictive control Nonlinear model predictive control Reduced computational complexity Reduced computational complexity Wave energy converter Wave energy converter Wave prediction Wave prediction
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| GB/T 7714 | Zhan, Siyuan , Chen, Yutao , Ringwood, John, V . Computationally-efficient nonlinear model predictive control of wave energy converters with imperfect wave excitation previews [J]. | OCEAN ENGINEERING , 2024 , 319 . |
| MLA | Zhan, Siyuan et al. "Computationally-efficient nonlinear model predictive control of wave energy converters with imperfect wave excitation previews" . | OCEAN ENGINEERING 319 (2024) . |
| APA | Zhan, Siyuan , Chen, Yutao , Ringwood, John, V . Computationally-efficient nonlinear model predictive control of wave energy converters with imperfect wave excitation previews . | OCEAN ENGINEERING , 2024 , 319 . |
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This paper addresses the trajectory planning and control problem of multi-spacecraft formation reconstruction in the presence of obstacles. By expressing spacecraft dynamics relative to a leader spacecraft in the formation, an integrated planning and control approach named predictive null-space-based behavior control (PNSBC) is proposed. First, a planner using null-space-based behavior control (NSBC) is designed at an upper layer to resolve multi-task conflicts. Here, both global tasks, e.g. formation keeping, and local tasks, e.g. obstacle avoidance, are considered. Second, a tracking controller is designed at the bottom layer using decentralized model predictive control. Unlike traditional two-layer approaches that treat planning and control separately, the proposed PNSBC integrates the planning and control in two ways: 1) the planner provides reference trajectories for the controller to track; 2) the model predictive control (MPC) controller provides predicted trajectories that can be employed by the planner for future task priority predictions, which extends the capability of NSBC from one-step planning to multi-step predicting. In addition, the computational burden of the MPC controller is greatly reduced by putting the nonlinear obstacle avoidance constraints into the planner as a local task. Simulation results show that such integrated approach has better performance in terms of safety constraint guarantee, fuel consumption and travel distance when compared against traditional non-integrated approaches, or all-in-one MPC methods, while constraints and task objectives are fully satisfied.& COPY; 2023 COSPAR. Published by Elsevier B.V. All rights reserved.
Keyword ：
Collision avoidance Collision avoidance Nonlinear model predictive control Nonlinear model predictive control Null-space-based behavioral control Null-space-based behavioral control Spacecraft formation Spacecraft formation
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| GB/T 7714 | Huang, Jie , Zhang, Jiancheng , Tian, Guoqing et al. Integrated planning and control for formation reconfiguration of multiple spacecrafts: A predictive behavior control approach [J]. | ADVANCES IN SPACE RESEARCH , 2023 , 72 (6) : 2007-2019 . |
| MLA | Huang, Jie et al. "Integrated planning and control for formation reconfiguration of multiple spacecrafts: A predictive behavior control approach" . | ADVANCES IN SPACE RESEARCH 72 . 6 (2023) : 2007-2019 . |
| APA | Huang, Jie , Zhang, Jiancheng , Tian, Guoqing , Chen, Yutao . Integrated planning and control for formation reconfiguration of multiple spacecrafts: A predictive behavior control approach . | ADVANCES IN SPACE RESEARCH , 2023 , 72 (6) , 2007-2019 . |
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In this work, a behavior-based adaptive dynamic programming (BADP) method is proposed for the coordination control of unmanned ground vehicle-manipulator systems (UGVMs). Through a null-space-based behavioral control (NSBC) framework, the multi-objective coordination control is transformed into a single-objective tracking control at the mission layer. Since cost functions and control constraints are simplified at the control layer, the complexity of controller design is reduced. Then, an identifier-actor-critic reinforcement learning algorithm framework is introduced to learn the optimal control policy by balancing the control performance and consumption. Simulation results show that control costs are reduced by around 13.5% per sampling period compared to existing multiple objective control methods. Finally, the BADP method is experimentally validated using four real UGVMs.
Keyword ：
Adaptive dynamic programming Adaptive dynamic programming behavioral control behavioral control multi-objective mission multi-objective mission UGVMs UGVMs
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| GB/T 7714 | Zhang, Zhenyi , Chen, Jianfei , Mo, Zhibin et al. Behavior-based Adaptive Dynamic Programming Method for Multiple Mobile Manipulators Coordination Control [J]. | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS , 2023 , 21 (9) : 3022-3035 . |
| MLA | Zhang, Zhenyi et al. "Behavior-based Adaptive Dynamic Programming Method for Multiple Mobile Manipulators Coordination Control" . | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 21 . 9 (2023) : 3022-3035 . |
| APA | Zhang, Zhenyi , Chen, Jianfei , Mo, Zhibin , Chen, Yutao , Huang, Jie . Behavior-based Adaptive Dynamic Programming Method for Multiple Mobile Manipulators Coordination Control . | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS , 2023 , 21 (9) , 3022-3035 . |
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This study investigates the multi-objective path planning problem in logistics autonomous systems (LAS), where unmanned ground vehicles (UGVs) need to deliver multiple packages to various destinations while avoiding obstacles. Using the null-space-based behavioral control (NSBC) framework, we extend our previous reinforcement learning task supervisor (RLTS) to propose an enhanced RLTS (IRLTS) for optimal path planning and dynamic, simultaneous task priority adjustment. Notably, IRLTS can re-order delivery to minimize total path length when presented with unknown obstacles. Simulations affirm that IRLTS achieves shorter total path lengths than RLTS and outperforms offline optimization-based algorithms on path length and re-planning capability. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC- ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Keyword ：
Logistics autonomous system Logistics autonomous system null-space behavioral control null-space behavioral control path planning path planning reinforcement learning reinforcement learning
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| GB/T 7714 | Pan, Congjie , Zhang, Zhenyi , Chen, Yutao et al. Improved Reinforcement Learning Task Supervisor for Path Planning of Logistics Autonomous System [J]. | IFAC PAPERSONLINE , 2023 , 56 (2) : 10010-10015 . |
| MLA | Pan, Congjie et al. "Improved Reinforcement Learning Task Supervisor for Path Planning of Logistics Autonomous System" . | IFAC PAPERSONLINE 56 . 2 (2023) : 10010-10015 . |
| APA | Pan, Congjie , Zhang, Zhenyi , Chen, Yutao , Lin, Dingci , Huang, Jie . Improved Reinforcement Learning Task Supervisor for Path Planning of Logistics Autonomous System . | IFAC PAPERSONLINE , 2023 , 56 (2) , 10010-10015 . |
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针对现有基于振动信号的诊断模型泛化能力差,而深度学习网络对计算量和存储量要求高的问题,提出轻量级融合密集连接网络与残差神经网络的故障诊断模型.首先,利用格拉姆角场将原始时序信号映射为灰度图像,充分利用二维卷积神经网络的性能;然后,融合密集连接网络和残差神经网络的优点构建融合网络模型,并通过鬼影模块降低其性能消耗,形成轻量级和高识别率的深度网络.实验结果表明,该改进的融合深度学习模型在比传统模型具有更强的鲁棒性和适用性的同时,还拥有极低的浮点运算量与参数量资源占用,证明了该方法在滚动轴承故障诊断领域是有效的、可行的.
Keyword ：
密集连接网络 密集连接网络 故障诊断 故障诊断 格拉姆角场 格拉姆角场 残差网络 残差网络 滚动轴承 滚动轴承 轻量化 轻量化
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| GB/T 7714 | 张皓云 , 王武 , 柴琴琴 et al. 滚动轴承轻量级深度故障诊断模型 [J]. | 福州大学学报（自然科学版） , 2023 , 51 (3) : 395-401 . |
| MLA | 张皓云 et al. "滚动轴承轻量级深度故障诊断模型" . | 福州大学学报（自然科学版） 51 . 3 (2023) : 395-401 . |
| APA | 张皓云 , 王武 , 柴琴琴 , 陈宇韬 . 滚动轴承轻量级深度故障诊断模型 . | 福州大学学报（自然科学版） , 2023 , 51 (3) , 395-401 . |
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This paper proposes a novel robust differential game scheme to solve the collision avoidance problem for networked multi-agent systems (MASs), subject to linear dynamics, external disturbances and limited observation capabilities. Compared with the existing differential game approaches only con-sidering obstacle avoidance objectives, we explicitly incorporate the trajectory optimization target by penalizing the deviation from reference trajectories, based on the artificial potential field (APF) concept. It is proved that the strategies of each agent defined by individual optimization problems will converge to a local robust Nash equilibrium (R-NE), which further, with a fixed strong connection topology, will converge to the global R-NE. Additionally, to cope with the limited observation for MASs, local robust feedback control strategies are constructed based on the best approximate cost function and distributed robust Hamilton-Jacobi-Isaacs (DR-HJI) equations, which does not require global information of agents as in the traditional Riccati equation form. The feedback gains of the control strategies are found via the ant colony optimization (ACO) algorithm with a non-dominant sorting structure with convergence guarantees. Finally, simulation results are provided to verify the efficacy and robustness of the novel scheme. The agents arrived at the targeted position collision-free with a reduced arrival time, and reached the targeted positions under disturbance.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.
Keyword ：
Collision avoidance Collision avoidance Limited observation Limited observation Multi-agent systems Multi-agent systems Robust differential game Robust differential game Trajectory optimization Trajectory optimization
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| GB/T 7714 | Xue, Wenyan , Zhan, Siyuan , Wu, Zhihong et al. Distributed multi-agent collision avoidance using robust differential game [J]. | ISA TRANSACTIONS , 2023 , 134 : 95-107 . |
| MLA | Xue, Wenyan et al. "Distributed multi-agent collision avoidance using robust differential game" . | ISA TRANSACTIONS 134 (2023) : 95-107 . |
| APA | Xue, Wenyan , Zhan, Siyuan , Wu, Zhihong , Chen, Yutao , Huang, Jie . Distributed multi-agent collision avoidance using robust differential game . | ISA TRANSACTIONS , 2023 , 134 , 95-107 . |
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Nonlinear model predictive control has been used in motion cueing algorithms recently to consider the nonlinear dynamics model of the system. The entire motion cueing algorithm indexes, including the physical and dynamical constraints of the actuators and physical constraints of passive joints, can be controlled with precision using nonlinear model predictive control. However, several weighting parameters in the nonlinear model predictive control-based motion cueing algorithm (including driving sensation, motion description of the actuators, and passive joints) require proper and laborious tuning to attain an optimal design structure. In this work, the optimal weighting parameters of a nonlinear predictive control-based motion cueing algorithm model are calculated using cascade optimisation and human interaction. A cascade optimisation method consisting of a particle swarm optimisation and genetic algorithm is designed to identify the best weighting parameters compared to those from one optimiser. In addition, the human decision-making units are added to the two-level cascade optimiser to determine the best solution from a Pareto front. The proposed cascade optimiser decreases the run-time with better extraction of the optimal weighting parameters to increase the motion fidelity compared to a single optimiser. It should be noted that the proposed methodology is applied along longitudinal channel. While the same methodology can be applied along lateral, heave and yaw channels for further evaluation of the proposed method. The proposed model is simulated utilising the MATLAB software and the results prove the efficiency of the newly proposed model compared to those from the previous single optimiser in reproducing more accurate motion signals with better usage of the driving motion platform workspace.
Keyword ：
Actuators Actuators Cascade optimization Cascade optimization human interaction human interaction Kinematics Kinematics Mathematical models Mathematical models motion cueing algorithm motion cueing algorithm nonlinear model predictive control nonlinear model predictive control Optimization Optimization Predictive models Predictive models Solid modeling Solid modeling Tuning Tuning weight tuning weight tuning
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| GB/T 7714 | Qazani, Mohammad Reza Chalak , Asadi, Houshyar , Chen, Yutao et al. An Optimal Nonlinear Model Predictive Control-Based Motion Cueing Algorithm Using Cascade Optimization and Human Interaction [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 , 24 (9) : 9191-9202 . |
| MLA | Qazani, Mohammad Reza Chalak et al. "An Optimal Nonlinear Model Predictive Control-Based Motion Cueing Algorithm Using Cascade Optimization and Human Interaction" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 24 . 9 (2023) : 9191-9202 . |
| APA | Qazani, Mohammad Reza Chalak , Asadi, Houshyar , Chen, Yutao , Abdar, Moloud , Karkoub, Mansour , Mohamed, Shady et al. An Optimal Nonlinear Model Predictive Control-Based Motion Cueing Algorithm Using Cascade Optimization and Human Interaction . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 , 24 (9) , 9191-9202 . |
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