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多智能体系统、车辆跟踪控制、卷积网络、离散时间系统、优化...
Jun Tang, Gang Liu and Qingtao Pan, "A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1627-1643, Oct. 2021. >Provide a comprehensive survey of swarm intelligence and represent a categorization scheme. >Describe the achievements of the swarm intelligence in various applications of related fields. >Summarize the major strengths and limitations, along with main future research trends. >Taking into account the effects of DoS attacks and external disturbance, a state observer is designed to remove the assumption that the states of the agent are measurable. >A distribute adaptive memory event-triggered mechanism is designed, which does not need global information when updating, thus reducing the communication burden. >This method can effectively reduce the difficulty caused by unmeasurable state and improve observer performance. In addition, the consensus performance of the system can also be improved. >Framework: We combine intention prediction and trajectory prediction for the specified intersection scenarios. >Distribution Parameter Constraints: We propose an intersection prior trajectories model (IPTM) to create statistics of the historical trajectories, which approximates the distribution of the ground truth. >Evaluation Metrics: We analyse the credibility of the estimated trajectory by applying the modified Hausdorff distance criteria to the predicted trajectory and the prior trajectory distribution, which does not require the ground truth. >Proposing a finite-time and a fixed-time backstepping control scheme with which the vehicle can track the given heading with all the signals uniformly bounded and the tracking errors converge to a neighborhood of zeros within the settling time. >Incorporating the compensator-based command filter technique into the proposed control scheme, by which the issue of “explosion of complexity” is eliminated, the filtering error can be compensated within finite/fixed time. >Adaptive technique is introduced in the context of controller design, and the problem of unknown model parameters is solved. >This paper proposes a control strategy called enclosing control. >A continuous-time protocol and a sampled-data based protocol are designed for enclosing control problems. >A special enclosing control with no leader located on the convex hull boundary under the protocols is studied. >The sub-goals are detected automatically via computer vision. >The agent receives an inner reward after completing the sub-goal. >The combined input of the sub-goal and the image achieves a better result. >Mitigating the problem of exponentially-explosive sampling times in random walk. >Making the convolution operation on graphs more efficient and flexible like CNN. >Experimentally validating the efficiency and effectiveness. >A model of multi-echelon logistic networks exhibiting complex topology is constructed. >Goods reflow is subject to positive delay. >Uncertain demand is placed at any network node. >Under directed switching topology, the leader-following formation control problem for discrete-time linear multi-agent systems is first considered in this work. >A novel reduced-order observer is designed for each following agent based on the relative output information, which can estimate the state effectively. >Based on the Lyapunov method and the modified discrete-time Algebraic Riccati Equation, a multi-step control algorithm is established for achieving the expected leader-following formation. >The practical trade-off design between energy consumptions and consensus performances is realized with a limited total budget. >The impacts of both switching topologies and intermittent communications are considered in consensus design and analysis. >The leaderless and leader-following consensus are achieved in a unified framework.
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