Self-Triggered Consensus of Vehicle Platoon System With Time-Varying Topology

Wang, Weiping; Wang, Chunyang; Guo, Yongzhen; Luo, Xiong; Gao, Yang

doi:10.3389/fnbot.2020.00053

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…Researchers are developing new materials that are capable of responding to stimuli such as heat, light, or electricity, allowing soft robots to perform a wider range of tasks [5]. Soft grippers can be made using a variety of materials, including silicone, hydrogels, and polymers [6]. The Harvard Wyss Institute has developed an octopus-inspired soft robot that is capable of crawling, swimming, and grasping objects [7].…”

Section: Introductionmentioning

confidence: 99%

Tendon-Driven Variable-Stiffness Pneumatic Soft Gripper Robot

Mawah,

Park

2023

Robotics

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In recent times, the soft robotics field has been attracting significant research focus owing to its high level of manipulation capabilities unlike traditional rigid robots, which gives room for increasing use in other areas. However, compared to traditional rigid gripper robots, being capable of controlling/obtaining overall body stiffness when required is yet to be further explored since soft gripper robots have inherently less-rigid properties. Unlike previous designs with very complex variable-stiffness systems, this paper demonstrates a soft gripper design with minimum system complexity while being capable of varying the stiffness of a continuum soft robotic actuator and proves to have potential applications in gripping objects of various shapes, weights, and sizes. The soft gripper actuator comprises two separate mechanisms: the pneumatic mechanism for bending control and the mechanical structure for stiffness variation by pulling tendons using stepper motors which compresses the actuator, thereby changing the overall stiffness. The pneumatic mechanism was first fabricated and then embedded into another silicon layer during which it was also merged with the mechanical structure for stiffness control. By first pneumatically actuating the actuator which causes bending and then pulling the tendons, we found out that the actuator stiffness value can be increased up to 145% its initial value, and the gripper can grasp and lift a weight of up to 2.075 kg.

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Section: Introductionmentioning

confidence: 99%

Tendon-Driven Variable-Stiffness Pneumatic Soft Gripper Robot

Mawah,

Park

2023

Robotics

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“…If information is being exchanged by direct sensing, the locally visible neighbors of vehicles or aircrafts will likely change over time. 18,19 Possible changes over time in each agents nearest neighbors are explicitly taken into account, which is an example of information consensus under dynamically changing interaction topologies. The communication topology gives rise to unpredictable influence on the consensus performance, which suggests the consensus of MASs with time-varying topology is more interesting and valuable than that with fixed topology.…”

Section: Introductionmentioning

confidence: 99%

Guaranteed cost optimal leader‐synchronization strategy design for distributed multi‐agent systems with input saturation

Shi

Yuan

2022

Intl J Robust & Nonlinear

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In this article, a guaranteed cost optimal leader-synchronization control strategy design problem is investigated for the input-saturated multi-agent systems (MASs). A time-varying network topology is adopted to describe the varying network communication among the MASs in practical situation. With considering input saturation, time-varying network topology, and stochastic nonlinearities disturbance, it is literally impossible to obtain the accurate value of the associate cost function for the leader-synchronization performance. As an alternative, an upper bound of cost function is derived for the distributed MASs. Then, the guaranteed cost optimal leader-synchronization strategy is designed such that the upper bound can be minimized under a fixed domain of attraction (DOA). Meanwhile, an optimization problem is solved to maximize the DOA by the linear matrix inequality (LMI) method over an infinite horizon. Finally, a B747 − 100∕200 aircraft example consisting of a leader and four followers is provided to illustrate the validity of the proposed methodology.

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“…To reduce the communication and computation consumptions, Li et al 5 proposed a self‐triggered scheme for the high‐speed trains system. Wang et al 6 applied the self‐triggered strategy to the consensus control for vehicle platoon systems to keep the safe distance. Wan et al 7 developed a dynamic self‐triggered control for Markov jump systems to fit trigger parameters with different submodels.…”

Section: Introductionmentioning

confidence: 99%

Model‐free self‐triggered control based on deep reinforcement learning for unknown nonlinear systems

Wan

Karimi

Luan

et al. 2022

Intl J Robust & Nonlinear

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This article proposes a joint learning technique for control inputs and triggering intervals of self‐triggered control nonlinear systems with unknown dynamics. First, deep reinforcement learning is introduced to the self‐triggered control system by considering both the control performance and triggering performance in the reward function. Then, the control inputs and triggering intervals are simultaneously learned by the developed deep deterministic policy gradient approach. Under this strategy, not only the desired control performance is guaranteed for unknown nonlinear systems, but also both the computation and communication occupation for the controlled system are decreased without any triggering thresholds. Finally, simulations for the cart‐pole swing‐up system are illustrated to verify the effectiveness of the proposed scheme.

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Self-Triggered Consensus of Vehicle Platoon System With Time-Varying Topology

Cited by 11 publications

References 27 publications

Tendon-Driven Variable-Stiffness Pneumatic Soft Gripper Robot

Tendon-Driven Variable-Stiffness Pneumatic Soft Gripper Robot

Guaranteed cost optimal leader‐synchronization strategy design for distributed multi‐agent systems with input saturation

Model‐free self‐triggered control based on deep reinforcement learning for unknown nonlinear systems

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