Adaptive hierarchical formation control for uncertain Euler–Lagrange systems using distributed inverse dynamics

Rosa, Muhammad Ridho; Baldi, Simone; Wang, Ximan; Lv, Maolong; Yu, Wenwu

doi:10.1016/j.ejcon.2018.11.001

Cited by 24 publications

(13 citation statements)

References 39 publications

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“…Remark 6. The idea stems from [22], and is the following: In the case of multiple followers, each one can implement a control law in form (36) to synchronize the follower dynamics (9) to the leader dynamics (8). Due to the distributed matching conditions, the follower dynamics will indirectly match the reference dynamics.…”

Section: Propositionmentioning

confidence: 99%

“…The distributed model reference adaptive control framework allows the states/output and the input to be shared between the neighbors [20,21]. The extended version of the framework in the nonlinear domain has been proposed to synchronize uncertain heterogenous Euler-Lagrange (EL) in the directed acyclic networks [22]. In the presence of communication constraints such as time-varying delay and packet dropout, the distributed synchronization algorithm has been designed to synchronize the EL agents [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Leader–Follower Synchronization of Uncertain Euler–Lagrange Dynamics with Input Constraints

Rosa

2020

Aerospace

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This paper addresses the problem of leader–follower synchronization of uncertain Euler–Lagrange systems under input constraints. The problem is solved in a distributed model reference adaptive control framework that includes positive μ-modification to address input constraints. The proposed design has the distinguishing features of updating the gains to synchronize the uncertain systems and of providing stable adaptation in the presence of input saturation. By using a matching condition assumption, a distributed inverse dynamics architecture is adopted to guarantee convergence to common dynamics. The design is studied analytically, and its performance is validated in simulation using spacecraft dynamics.

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Section: Propositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leader–Follower Synchronization of Uncertain Euler–Lagrange Dynamics with Input Constraints

Rosa

2020

Aerospace

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“…In this section, the subscript l in (21) represents the values for leader type UAVs. Model reference control [42] is a typical control approach in which the plant is made to have the dynamics as of a reference model by using an appropriate control law. The control law is developed by first defining a control structure, and then finding matching conditions that makes the closed loop dynamics as that of the reference model.…”

Section: Synchronization Of Leader Dynamics To Reference Dynamicsmentioning

confidence: 99%

A Semi-Physical Platform for Guidance and Formations of Fixed-Wing Unmanned Aerial Vehicles

Yang

Thomas

Singh

et al. 2020

Sensors

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Unmanned Aerial Vehicles (UAVs) have multi-domain applications, fixed-wing UAVs being a widely used class. Despite the ongoing research on the topics of guidance and formation control of fixed-wing UAVs, little progress is known on implementation of semi-physical validation platforms (software-in-the-loop or hardware-in-the-loop) for such complex autonomous systems. A semi-physical simulation platform should capture not only the physical aspects of UAV dynamics, but also the cybernetics aspects such as the autopilot and the communication layers connecting the different components. Such a cyber-physical integration would allow validation of guidance and formation control algorithms in the presence of uncertainties, unmodelled dynamics, low-level control loops, communication protocols and unreliable communication: These aspects are often neglected in the design of guidance and formation control laws for fixed-wing UAVs. This paper describes the development of a semi-physical platform for multi-fixed wing UAVs where all the aforementioned points are carefully integrated. The environment adopts Raspberry Pi’s programmed in C++, which can be interfaced to standard autopilots (PX4) as a companion computer. Simulations are done in a distributed setting with a server program designed for the purpose of routing data between nodes, handling the user inputs and configurations of the UAVs. Gazebo-ROS is used as a 3D visualization tool.

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“…In case the sensors, actuators, and controllers in a control loop are coordinated by the exchange of information over a communication network, it can be classified as NCS [1,2]. NCS can be found in many technological areas such as underwater unmanned vehicle [3], platoon vehicles [4], smart grids [5], transportation [6,7], distributed robot system [8], and many other applications.…”

Section: Introductionmentioning

confidence: 99%

Networked control system stability analysis of pipeline system with networked-induced delay

2022

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This paper presents the design of Networked Control Systems (NCS) for pipeline systems. NCS plays an important role in controlling and monitoring large-scale systems such as pipeline systems. To implement the NCS, one must derive the pipe model and consider the network communication constraints. Here, the pipeline model is divided into two sections for simplicity. For example, in a long pipeline system, one can use a higher number of sections in order to give a better result for the analysis. Then let’s consider a networked-induced delay as the network communication constraint. The discretize pipe dynamics model is derived to support the NCS scheme in the pipeline system. The stability analysis of the proposed NCS is derived by taking into account the small and the large networked-induced delay. Then the optimal LQR control is designed for both stabilizing and tracking. The stability region of the pipeline system in the NCS scheme with networked-induced delay is derived and depicted to provide stability information. The design of the proposed controller under network constraint (networked-induced delay) must consider the stability plot that is divided into the stable region and unstable region. In this research, let’s assume that it is possible to measure the exact time delay and then consider the allowable sampling time for the controller. The proposed controller is designed by considering the NCS scheme with time delay both for regulator and tracking problems. By using the proposed controller, the pipeline system can be controlled in the presence of network-induced delay, which commonly occurs in a distributed system. The simulation verifies the stability analysis of the proposed optimal control for the pipeline systems with the NCS scheme under networked induced delay

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Adaptive hierarchical formation control for uncertain Euler–Lagrange systems using distributed inverse dynamics

Cited by 24 publications

References 39 publications

Leader–Follower Synchronization of Uncertain Euler–Lagrange Dynamics with Input Constraints

Leader–Follower Synchronization of Uncertain Euler–Lagrange Dynamics with Input Constraints

A Semi-Physical Platform for Guidance and Formations of Fixed-Wing Unmanned Aerial Vehicles

Networked control system stability analysis of pipeline system with networked-induced delay

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