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Integrated Rudder/Fin control With Disturbance Compensation distributed model predictive control

IEEE ACCESS 2018年 6卷 72925-72938页

作者： Liang, Lihua Wen, Yu Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China

For the ship sailing in the seaway, usually the rudder is used to control the heading and the fin stabilizer is used to reduce rolling. However, there are interactions between their control effects, which usually deteriorate the control performance. The interactions can be used to enhance the control performance, if rudder and fin stabilizer are controlled to work in coordination, and this is usually called the integrated rudder/fin control. In this paper, a new control method based on distributed model predictive control (DMPC) and disturbance compensation mechanism is proposed to achieve the integrated rudder/fin control. MPC controllers are designed for rudder and fin stabilizer respectively to fulfill their different control requirements. Communication between the MPC controllers is established to make the rudder and fin stabilizer work in coordination. An extended state observer is adopted as the disturbance observer to estimate the lumped disturbance of roll motion which consists of model mismatch and external disturbance. The MPC controllers calculate control actions with the lumped disturbance to improve the control performance. They also avoid actuator saturations by limiting the control actions, which makes the control more robust in sever sea conditions. Simulation study shows that, with the proposed control method, the roll stabilization performance is enhanced and the heading control capability is preserved. The proposed control method also shows a more desirable control performance in roll stabilization compared with a well-tuned LQR controller.

关键词： distributed model predictive control disturbance compensation integrated rudder/fin control MIMO constrained control sliding mode differentiator

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distributed model predictive control with saturated inputs by a saturation-dependent Lyapunov function

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OPTIMAL control APPLICATIONS & METHODS 2017年第3期38卷 336-354页

作者： Zhang, L. Xie, W. Wang, J. South China Univ Technol Coll Automat Sci & Technol Guangzhou 510641 Guangdong Peoples R China Shanghai Jiao Tong Univ Minist Educ China Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

In this paper, distributed model predictive control (MPC) problems are considered for input-saturated polytopic uncertain systems by a saturation-dependent Lyapunov function approach. The actuator saturation is processed by the transformation into the linear convex combination form. By the decomposition of the control input, distributed MPC controllers are designed in parallel for each subsystems. The Lyapunov Function we select is saturation dependent, which is less conservative than the general Lyapunov Function approach. An invariant set condition is provided and min-max distributed MPC is proposed based on the invariant set. The robust distributed MPC controllers are determined by solving a linear matrix inequality (LMI) optimization problem. To reduce the conservatism, we present a robust distributed MPC algorithm, which is not only saturation dependent but also parameter dependent. A Jacobi iterative algorithm is developed to coordinate the distributed MPC controllers. A simulation example with multi-subsystem is carried out to demonstrate the effectiveness of the proposed distributed MPC algorithms. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： distributed model predictive control polytopic uncertain saturated inputs saturation dependent parameter dependent

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distributed model predictive control for cooperative synchromodal freight transport

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2017年第Sep.期105卷 240-260页

作者： Li, Le Negenborn, Rudy R. De Schutter, Bart Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands Delft Univ Technol Dept Maritime & Transport Technol NL-2628 CD Delft Netherlands

This paper investigates cooperative synchromodal freight transport planning among multiple intermodal freight transport operators in different and interconnected service networks. The cooperative planning is formulated as a cooperative model predictive container flow control problem, and solved with three distributed model predictive Flow control (DMPFC) approaches: the parallel and serial Augmented Lagrangian Relaxation (ALR) based DMPFC approaches, and the Alternating Direction Method of Multipliers (ADMM) based DMPFC approach. The simulation results show that the serial ALR-based DMPFC approach requires the least iterations and information exchanges while the ADMM-based DMPFC approach takes the least amount of actual computation time. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Cooperative synchromodal freight transport distributed model predictive control Alternating direction method of multipliers

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Scalable distributed model predictive control for constrained systems

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AUTOMATICA 2018年 93卷 407-414页

作者： Asadi, Elham (Fatemeh) Richards, Arthur Univ Cambridge Dept Engn Trumpington St Cambridge England Univ Bristol Dept Aerosp Engn Queens Bldg Bristol Avon England

A distributed model predictive control strategy is proposed for subsystems sharing a limited resource. Self-organized Time Division Multiple Access is used to coordinate subsystem controllers in a sequence such that no two re-optimize simultaneously. This new approach requires no central coordination or pre-organized optimizing sequence. The scheme guarantees satisfaction of coupled constraints despite dynamic entry and exit of subsystems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Constrained systems Scalable DMPC

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An LMI-based approach to distributed model predictive control design for spatially-interconnected systems

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AUTOMATICA 2018年 95卷 481-487页

作者： Liu, Qin Abbas, Hossam Seddik Velni, Javad Mohammadpour Univ Georgia Coll Engn Sch Elect & Comp Engn Athens GA 30602 USA Assiut Univ Fac Engn Elect Engn Dept Assiut 71515 Egypt

This paper proposes a new framework to distributed model predictive control (MPC) design for linear time- and space-invariant (LTSI) distributed systems subject to constraints. Given a two-dimensional, input-output model that describes the distributed dynamics among the subsystems, it is shown that a non-minimal state space realization leads to numerically tractable linear matrix inequality (LMI) based terminal state feedback controller design. The local online optimization problem is defined at the subsystem level with subsystems exchanging predictions through coupled states and can be solved in parallel at all subsystems non-iteratively. Stability and recursive feasibility are guaranteed in the presence of one-step delayed exchanging information among subsystems by imposing consistency constraints and terminal constraints. Attributed to the non-minimal state space realization, input-output properties are preserved in the MPC formulation, and hence no state estimator is needed for the online implementation. Simulation results using a heat equation demonstrate a satisfactory performance of the proposed distributed MPC design compared to centralized MPC schemes. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Spatially-interconnected systems Linear matrix inequalities Consistency constraints

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Robust output feedback distributed model predictive control of networked systems with communication delays in the presence of disturbance

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ISA TRANSACTIONS 2018年 80卷 12-21页

作者： Razavinasab, Zahra Farsangi, Malihe M. Barkhordari, Mojtaba Shahid Bahonar Univ Dept Elect Engn Afzalipour Sq Kerman Iran

In this work, an output feedback cooperative distributed model predictive control is developed for a class of networked systems composed of interacting subsystems interconnected through their states, in which it handles bounded disturbances and time varying communication delays. A distributed buffer based prediction strategy is used to compensate bounded delays and predict those states, which are coupled between subsystems that their actual values may not available due to delays. In the design of robust distributed model predictive control, distributed moving horizon estimation is employed so that convergence and boundedness of the estimation error are ensured. Furthermore, robust exponential stability of the closed loop system is established. The effectiveness of the proposed method is illustrated using two interconnected continuous stirred tank reactors.

关键词： Communication delays distributed model predictive control distributed moving horizon estimation Networked systems Robust control

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Adaptive event-triggered distributed model predictive control for multi-agent systems

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SYSTEMS & control LETTERS 2019年 134卷 104531-000页

作者： Zhan, Jingyuan Hu, Yanjie Li, Xiang Beijing Univ Technol Coll Metropolitan Transportat Beijing Key Lab Transportat Engn Beijing 100124 Peoples R China Fudan Univ Dept Elect Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Fudan Univ Res Ctr Smart Networks & Syst Sch Informat Sci & Engn Shanghai 200433 Peoples R China

Event-triggered control is an effective control approach with control update instants determined by pre-defined event-triggering conditions, and the triggering conditions are usually required to be tested continuously. In this paper, we present an adaptive event-triggered distributed model predictive control algorithm for nonlinear continuous-time multi-agent systems. All the agents are able to adaptively determine when to check the triggering conditions, which means the triggering conditions are tested intermittently with intervals determined adaptively. We prove the feasibility of the proposed algorithm, and also prove that the multi-agent system under the algorithm will converge to an invariant set in finite time by utilizing variable prediction horizon approach instead of the Lyapunov function method, such that the event-triggering conditions are less conservative. Finally, we provide numerical examples to verify the effectiveness and superiority of the proposed algorithm, revealing that the proposed algorithm can efficiently reduce the communication and computation cost as well as the sensing load. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Event-triggered control Multi-agent system Nonlinear system distributed model predictive control

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distributed model predictive control for consensus of nonlinear second-order multi-agent systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2017年第5期27卷 830-842页

作者： Gao, Yulong Dai, Li Xia, Yuanqing Liu, Yuwei Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

This paper proposes a distributed model predictive control algorithm for the consensus of nonlinear second-order multi-agent systems. At each update time, all the agents are permitted to optimize. A positively invariant terminal region and a corresponding auxiliary controller are developed for each agent. Furthermore, time-varying compatibility constraint is presented to denote a degree of consistency between the assumed trajectories and the actual trajectories of each agent. Given the designed terminal ingredients (terminal region, auxiliary controller, and terminal cost) and compatibility constraints, the recursive feasibility and closed-loop stability of the whole system are guaranteed. The simulation results are given to illustrate the effectiveness of the proposed approach. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： nonlinear second-order system distributed model predictive control compatibility constraint invariant terminal region

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distributed model predictive control of linear discrete-time systems with local and global constraints

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AUTOMATICA 2017年 81卷 184-195页

作者： Wang, Zheming Ong, Chong Jin Natl Univ Singapore Dept Mech Engn Singapore 117576 Singapore

This paper proposes a distributed model predictive control (DMPC) approach for a family of discrete time linear systems with local (uncoupled) and global (coupled) constraints. The proposed approach is based on the dual problem of a MPC optimization problem involving all systems. This dual problem is then distributedly solved, based on the Alternating Direction Multiplier Method (ADMM) with several known simplifications. When the network of systems is large or sparsely connected, the computation of the optimal control using ADMM can be expensive. The proposed approach mitigates this problem by allowing early termination of the ADMM process. This is made possible via a finite-time consensus algorithm that determines the satisfaction of the termination condition and by appropriate tightening of the coupled constraints. Under reasonable assumptions, the approach is guaranteed to converge to a small neighborhood of the optimal so long as the network is connected. Recursive feasibility and exponential stability of the closed-loop system are shown. The performance of the proposed approach is demonstrated by a numerical example. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Coupled constraints Suboptimality Tightened constraints

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Constrained Optimization and distributed model predictive control-Based Merging Strategies for Adjacent Connected Autonomous Vehicle Platoons

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IEEE ACCESS 2019年 7卷 163085-163096页

作者： Min, Haigen Yang, Yiming Fang, Yukun Sun, Pengpeng Zhao, Xiangmo Changan Univ Sch Informat Engn Xian 710064 Peoples R China Changan Univ China Mobile Commun Corp Joint Lab Internet Vehicles Minist Educ Xian 710064 Peoples R China

Vehicle platooning has been a major research topic in recent years because of its ability to reduce fuel consumption, enhance road traffic safety and utilize the road more efficiently. A practical and applicable platoon merging maneuver is the key to forming new platoons while ensuring safety and economy. This study proposes merging strategies that consider both safe space and acceleration limitations for two adjacent platoons comprising connected autonomous vehicles (CAVs). The distributed model predictive control (DMPC) algorithm is adopted to design a DMPC2 controller, which includes 1) a space-making DMPC controller that controls the vehicles in one platoon, i.e. the target platoon, to make space for the vehicles in a second platoon, i.e. the merge platoon, and 2) a DMPC platoon controller that controls the merging vehicles to fill in the space in the target platoon. The former considers the explicit acceleration constraint of the vehicle, making the generated trajectory more feasible, and the latter controls the merge platoon to perform an overall mergence, which reduces the complexity of the merge problem. The low computation load of DMPC makes online computing and real-time control possible in practical scenarios. A simulation study is conducted with different scenarios and parameters, and the results demonstrate that the proposed strategy is more feasible and efficient, and less time-consuming than the existing state-of-the-art methods and have the advantages of taking safety distance and control input constraints into account.

关键词： Platoons merging space making distributed model predictive control connected and autonomous vehicles

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