Self-Triggered DMPC Design for Cooperative Multiagent Systems

Mi, Xiaoxiao; Zou, Yuanyuan; Li, Shaoyuan; Karimi, Hamid Reza

doi:10.1109/tie.2019.2896098

Cited by 81 publications

(37 citation statements)

References 27 publications

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“…In the execution of the decision scheme, there are complex dependencies and time-space constraints among agents, and there also exist resource and time constraints between agents and tasks [8]. [9]. e longer the time consumption, the more serious the loss of people's property will be.…”

Section: Complex Cooperative Relationshipmentioning

confidence: 99%

“…e reusable resource conflict coordination mechanism can be used by multiple decision-making agents simultaneously to generate repeated resource conflicts. For these resource conflicts, the tasks exhibiting higher priority are reserved and the tasks with lower priorities are coordinated (1) Procedure MA-HTN (s, T, D) (2) set s, initial state (3) set T, initial task set (4) set D, domain (5) initial operator ActionID � 0 (6) set P � the empty plan (7) T 0 ⟵ t belongs to T : no other task in T is constrained to precede t (8) loop (9) if T is empty then return P (10) nondeterministically choose any t in T 0 (11) if t is a primitive task then (12) choose a ground instance a for t with the smallest cost among the available resources (13) ActionID � ActionID + 1 (14) opmessage(a) � (ID, Resourceused(a), st(a), et(a), cost(a)) (15) sendlist ⟵ opmessage(a) (16) send opmessage(a) to the other planners (17) if receivelist ≠ ∅ : (18) ResourceID � Resourceused(a) (19) conflict actions a′ ⟵ a′ belongs to the extopmessages of receivelist : (20) has Resource(a′) � ResourceusedID and has the smallest st(a′) (21) and duration(st, et) is overlapped with duration(st′, et′)}, then (22) if st < st′ (23) delete the rest extopmessage in receivelist (24) Resourcestate(r) � i, Resourcest(r) � st, Resourceet(r) � et (25) delete T in T 0 and add a into P (26) else if st > st′ (27) delete opmessage(a) in sendlist (28) Resourcestate(r) � i′, Resourcest(r) � st′, Resourceet(r) � et′ (29) Backtrack (30) else delete T in T 0 and add a into P (31) if t is a nonprimitive task then (32) choose a method to decompose t into subtasks t 1 , t 2 , . .…”

Section: Coordination Mechanism Of Local Conflicts For the Contradicmentioning

confidence: 99%

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Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

Zhang

Gang

Song

et al. 2019

Mathematical Problems in Engineering

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For task planning of the command and control structure, the existing algorithms exhibit low efficiency and poor replanning quality under abnormal conditions. Given the requirements of the current accusation architecture, a distributed command and control structure model is built in this paper based on multiagents, which exploits the superiority of multiagents in achieving complex tasks. The concept of MultiAgent-HTN is proposed based on the framework. The original hierarchical task network planning algorithm is optimized, the multiagent collaboration framework is redefined, and the coordination mechanism of local conflict is developed. With the classical resource scheduling problem as the experimental background, the proposed algorithm compared with the classical HTN algorithm is drawn. According to the experimental results, the proposed algorithm exhibits higher quality and higher efficiency than the existing algorithm and the space anomaly is significant in the course of processing. The planning is more efficient and the time is more complicated and superior in solving the same problem, and the algorithm exhibits good convergence and adaptability. In the conclusion, it is proved that the distributed command and control structure proposed in this paper exhibits high practicability in relevant fields and can solve the problem of distributed command and control structure in a multiagent scenario.

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Section: Complex Cooperative Relationshipmentioning

confidence: 99%

Section: Coordination Mechanism Of Local Conflicts For the Contradicmentioning

confidence: 99%

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

Zhang

Gang

Song

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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“…A coordinated energy dispatch based on Distributed model predictive control (DMPC) is proposed, and the corresponding simulation results show the effectiveness of the proposed method [16]. A co-design of the self-triggered mechanism and distributed model predictive control (DMPC) is proposed to achieve the cooperative objectives while efficiently exploiting communication network [17]. A model predictive control-based droop current regulator to interface PV in smart dc distribution systems is proposed [18].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Controller Based on Online Obtaining of Softness Factor and Fusion Velocity for Automatic Train Operation

Wang

Sheng

et al. 2020

Sensors

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This paper develops an improved model predictive controller based on the online obtaining of softness factor and fusion velocity for automatic train operation to enhance the tracking control performance. Specifically, the softness factor of the improved model predictive control algorithm is not a constant, conversely, an improved online adaptive adjusting method for softness factor based on fuzzy satisfaction of system output value and velocity distance trajectory characteristic is adopted, and an improved whale optimization algorithm has been proposed to solve the adjustable parameters; meanwhile, the system output value for automatic train operation is not sampled by a normal speed sensor, on the contrary, an improved online velocity sampled method for the system output value based on a fusion velocity model and an intelligent digital torque sensor is applied. In addition, the two improved strategies proposed take the real-time storage and calculation capacities of the core chip of the controller into account. Therefore, the proposed improved strategies (I) have good performance in tracking precision, (II) are simple and easily conducted, and (III) can ensure the accomplishing of computational tasks in real-time. Finally, to verify the effectiveness of the improved model predictive controller, the Matlab/simulink simulation and hardware-in-the-loop simulation (HILS) are adopted for automatic train operation tracking control, and the tracking control simulation results indicate that the improved model predictive controller has better tracking control effectiveness compared with the existing traditional improved model predictive controller.

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“…However, when actuators and/or sensors faults occur, under the traditional control schemes, system performance will degrade and even the stability of system cannot be ensured . Therefore, it is required that the developed control strategies have ability to deal with emergency situations resulting from actuators and/or sensors faults . It is well known that fault tolerant control (FTC) is an effective approach to improve the reliability of control systems and maintain an desired performance/stability of systems.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5] Therefore, it is required that the developed control strategies have ability to deal with emergency situations resulting from actuators and/or sensors faults. [6][7][8][9] It is well known that fault tolerant control (FTC) is an effective approach to improve the reliability of control systems and maintain an desired performance/stability of systems. Nowadays, many FTC approaches have been developed to handle the FTC problems.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous adaptive output feedback sliding mode control for Takagi‐Sugeno fuzzy Markovian jump systems with actuator faults

Liu

Zhang

et al. 2019

Adaptive Control & Signal

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Summary In this article, the problem of asynchronous adaptive dynamic output feedback sliding mode control (SMC) for a class of Takagi‐Sugeno (T‐S) fuzzy Markovian jump systems (MJSs) with actuator faults is investigated. The asynchronous dynamic output feedback control strategy is employed, as the nonsynchronization phenomenon of jump modes exists between the plant and the controller. A novel asynchronous adaptive SMC approach is proposed to solve the synthesis problem for T‐S fuzzy MJSs with actuator faults. Sufficient conditions for stochastic asymptotic stability of T‐S fuzzy MJSs are given. Under the designed asynchronous adaptive SMC scheme, the effects of actuator faults and external disturbance can be completely compensated and the reachability of sliding surface is ensured. Finally, an example is provided to demonstrate the effectiveness of the proposed design techniques.

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Self-Triggered DMPC Design for Cooperative Multiagent Systems

Cited by 81 publications

References 27 publications

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

Model Predictive Controller Based on Online Obtaining of Softness Factor and Fusion Velocity for Automatic Train Operation

Asynchronous adaptive output feedback sliding mode control for Takagi‐Sugeno fuzzy Markovian jump systems with actuator faults

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