This article considers the containment of heterogeneous linear time-invariant multi-human multi-agent systems with a distributed dynamic state feedback control law. The unique aspect this problem lies in presence multiple human operators. Each operator commands subset agents while observing same or different subset. Commanding based on their observation approach operators makes them integral components network rather than simple providers reference signals. setup requires new set conditions...

It is known that a closed-loop dynamical system subject to an adaptive controller remains stable either if there does not exist significant unmodelled dynamics or the effect of uncertainties negligible. This implies these controllers cannot tolerate large even when satisfy set conditions. In this paper, we present control architecture such proposed augmented with robustifying term. Unlike standard controllers, allows remain in presence A numerical example provided demonstrate efficacy approach.

Abstract This work tackles the tracking control problem of robotic manipulators where robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when manipulator is required to perform a periodic task repetitively, as in most industrial applications, repetitive learning controller proposed that does require can compensate uncertainties dynamical additive disturbances caused due motion. The solution achieved via use novel component design...

The contribution of this paper is a control synthesis and stability verification framework for linear time-invariant multiagent systems with heterogeneous actuator dynamics system uncertainties. In particular, we first propose distributed adaptive architecture in leader-follower setting class high-order systems. proposed uses hedging method, which alters the ideal reference model each agent order to ensure correct adaptation presence these agents. We then use Lyapunov theory matrix...

Summary Discrete‐time adaptive control algorithms can be executed directly in embedded code unlike their continuous‐time counterparts, which require discretization. However, designs predicated on quadratic Lyapunov‐based frameworks are quite intricate due to the resulting complexity Lyapunov difference expressions. Therefore, a wide array of available results addressing transient performance issues using cannot applied or readily extended discrete‐time case. In this article, we present new...

Multi-agent systems offer low-cost and flexible solutions for complex tasks thanks to their advantages efficient algorithms. One of the most important features multi-agent is operating in a formation example, carrying slung-load by using several multi-copters or platooning autonomous trucks. However, building control algorithms provide not straightforward as real physical are affected non-linear uncertainties non-identical actuator-related issues. Being inspired nature systems, this...

In this paper, a distributed adaptive control algorithm is designed for an uncertain multiagent system in the presence of unmeasurable coupled dynamics that adopts user-assigned Laplacian matrix nullspaces. Specifically, we use observer help us to guarantee overall stability, low-frequency learning methods deal with high-frequency learning, and modified coordinate system. Our proposes coordination systems asymptotic decoupling approach. An illustrative numerical example given demonstrate our...

On model reference adaptive control for uncertain dynamical systems, it is well know that there exists a fundamental stability limit, where the closed-loop system subject to this class of laws remains stable either if does not exist significant unmodeled dynamics or effect uncertainties negligible. Specifically, implies controllers cannot tolerate large even when satisfy set conditions. Motivated from standpoint, paper proposes approach relax an signal augmented with robustifying term. The...

This paper studies distributed adaptive architectures for controlling uncertain multiagent systems with unmeasurable coupled dynamics. Specifically, we first analyze a standard control method system uncertainties and dynamics in leader-follower setting, where present local stability conditions. We second propose an additional feedback term within the signal of each agent order to relax aforementioned An illustrative numerical example is then given demonstrate our contributions.

This paper provides a distributed adaptive control architecture for uncertain multiagent systems with non-identical actuation capacities and unknown effectiveness to achieve cooperative behaviors real-world applications. In detail, our approach includes user-assigned Laplacian matrix creating multiple agents, hedging-based reference model provide correct adaptation that is not affected by the presence of heterogeneous actuator dynamics in networked system, deal system anomalies. The...

Distributed adaptive control is a powerful framework to preserve stability of networked multiagent systems in the presence uncertainties resulting from, for example, modeling errors, unknown effectiveness, and perturbed information exchange. However, considering that consist agents with heterogeneous actuator capabilities, implementation distributed approaches not trivial task. This due fact each agent this case cannot identically execute given local laws can lead poor system performance or...

As it is well-known, system uncertainties and unmodeled dynamics can deteriorate stability properties of model reference adaptive control systems. Motivated by this standpoint, we first analyse conditions architectures in the presence unstructured dynamics. We then synthesise robustifying terms to relax aforementioned condition, which presents our main contribution. Specifically, these feedback loop guarantee overall even significant when satisfy a condition. further demonstrate theoretical...

View Video Presentation: https://doi.org/10.2514/6.2022-0359.vid By using distributed adaptive control algorithm with observer dynamics, this paper proposes an asymptotic decoupling approach for uncertain multiagent systems in the presence of state-dependent coupled dynamics. The major contribution proposed framework is that it guarantees convergence error between trajectories system and a given reference model without relying on any measurements from A generalization to address uncertainty...

Abstract Two important properties of industrial tasks performed by robot manipulators, namely, periodicity ( i.e. , repetitive nature) the task and need for to be end‐effector, motivated this work. Not being able utilize manipulator dynamics due uncertainties complicated control design. In a seemingly novel departure from existing works in literature, tracking problem is formulated space input torque aimed decrease error directly without making use inverse kinematics at position level. A...

It is recently discovered that the distributed control architectures can be used to drive agents desired different positions by using a modified Laplacian matrix. In addition, adaptive methods are powerful tools deal with presence of unknown effectiveness. The first contribution this paper theoretically present new for purpose achieving behaviors in networked robots second experimental results order demonstrate efficacy dynamics multi agent system, where we Quanser’s QBot-2e’s,...

View Video Presentation: https://doi.org/10.2514/6.2023-1632.vid In this paper, a model-based control approach is proposed for autonomous vision-based tracking of moving target by quadrotor vehicle. The considered problem presents several key challenges related to the lack observability relative range, which affects line-of-sight measurements, and incomplete knowledge dynamic response. These are addressed from an output feedback perspective within robust framework. effectiveness demonstrated...

View Video Presentation: https://doi.org/10.2514/6.2023-1812.vid This paper focuses on distributed adaptive control design for unknown multiagent systems in the presence of both state and dependent coupled dynamics with without effectiveness matrix. Specifically, we obtained asymptotic convergence absence actuator deficiencies, while uniformly ultimately bounded results deficiencies. An illustrative numerical example is then given to demonstrate our theoretical contributions.

A distributed adaptive control design for an uncertain modular robotic system is proposed in this paper. Specifically, a that consists of multiple robot manipulators are subject to non-linear parameters and robot-based uncertainties considered. designed compensate uncertainties, closed-loop stability analysis shown via Lyapunov analysis, simulation results provided on system.