Adaptive Leader‐Following Consensus for Uncertain Nonlinear Multi‐Agent Systems

In order to suppress traffic congestion and reduce traffic accidents, a cooperative driving systems with time-varying delay and nonlinearity under uncertain external disturbances in a variable speed-limited area is proposed from a cyberphysical perspective. Robust stabilization of the cooperative driving system is investigated by using Lyapunov-Krasovskii functional stability theory. Robust H ∞ control is designed to guarantee that the proposed system is robustly stable. Meanwhile, sufficient conditions for the state feedback controller are proposed to attenuate the external disturbances on the basis of linear matrix inequality. Finally, some useful results are obtained from the comparisons between without control scheme, existing ACC (adaptive cruise control) scheme, and the proposed control scheme, which could suppress traffic congestion and reduce traffic accidents.

Section: Robust H ∞ Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Stabilization and H_∞ Control of Cooperative Driving System with Time Delay in Variable Speed‐Limited Area from Cyber‐Physical Perspective

Chen

Sun

et al. 2018

“…E ( e ) denotes the angle deviation, and EC ( ec ) denotes the angle deviation's changing rate. Based on different time's E and EC, the fuzzy rules are used to correct P , I , D parameters online .…”

Section: Theoretical Analysismentioning

confidence: 99%

Control for the New Harsh sea Conditions Salvage Crane Based on Modified Fuzzy PID

Liu

Wang

et al. 2017

The new salvage crane for harsh sea conditions is a salvage and rescue device that is used for the space launch capsule. The wave compensation algorithm can improve search and rescue ability at sea, and can also provide wave compensation guidance for other sea vessels under harsh sea conditions. Firstly, compared with the traditional fuzzy PID algorithm, the wave compensation algorithm is improved in three aspects: fuzzy rules, fuzzy inputs, and fuzzy outputs. For the fuzzy rules, E and EC are used separately. E plays a main role and EC plays an auxiliary role. For the fuzzy inputs, the angle deviation is the sum of current angle and angle's changing rate. For the fuzzy outputs, the cylinder's inertia and the electro-hydraulic valve's reverse dead zone factors are taken into account, and the PID output value is set with upper limit, lower limit, and dead zone. Secondly, the control valve and the hydraulic system's model are established in AMESim. The improved algorithm is established in SIMULINK. Combining the hydraulic model with the algorithm model, we do the co-simulation analysis. Thirdly, the improved algorithm's verification experiments are carried out on a rocky platform, which can simulate the ship's instability in different sea conditions. Finally, the simulation and experimental results verify that the improved fuzzy PID algorithm enhances the salvage crane's response and robustness, and also improves the salvage effect.

“…Numerous multi-agent coordination and cooperation problems have been the subject of considerable attention from researchers over the past several years: aggregation, consensus, formation, social foraging, synchronization, containment, distributed averaging/optimization, etc. [11,[18][19][20]22,29,34,39,40]. In this paper, we are primarily concerned with the formation problem, which regards the situation where a desired geometric shape in space is assumed and maintained by a team of agents.…”

Section: Introductionmentioning

confidence: 99%

3D Multi‐Agent Formation Control with Rigid Body Maneuvers

Zhang

Queiroz

2018

In this paper, we propose a control scheme for the formation maneuvering problem of multi-agent systems where the team of agents can translate and rotate as a virtual rigid body in 3D. Using the single-integrator model, we formulate the basic control law which is comprised of a formation acquisition term, function of the graph rigidity matrix, and a rigid body maneuvering term. The control is dependent on the relative position of agents that are connected in an infinitesimally and minimally rigid framework in addition to the desired rigid body motion of the formation. To facilitate the design of the rigid body maneuver, one agent in the convex hull of the formation serves as the reference point for the rotation component. A simulation study demonstrates the formation controller.