Predictor-feedback synthesis in coordinate-free formation control under time-varying delays

González, Antonio; Aragüés, Rosario; López‐Nicolás, Gonzalo; Sagüés, Carlos

doi:10.1016/j.automatica.2020.108811

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…the system (1) achieves the GUES under the predictor ( 2), ( 3), ( 4) with the controller (7); where −1…”

Section: Design Methods Of the Predictor And Controllermentioning

confidence: 99%

“…To render the above objective accessible, considerable efforts have been devoted to deal with this critical issue and various approaches have been reported in literatures. Only mention a few, see examples in References 7‐11. Besides, the packet dropout/loss and disordering are also universally encountered in practical communication network and they are emerged if the maximal data transmission time delay is larger than time interval between any consecutive date sending time instants.…”

Section: Introductionmentioning

confidence: 99%

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Predictor‐based switching control for networked systems

Cui

2023

Intl J Robust & Nonlinear

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SummaryThis paper concerns a networked predictive control (NPC) for linear systems with data time‐varying delays, packet losses and disordering. By utilizing the received time‐delayed state measurement and control input, a predictive algorithm is firstly proposed for approximating the future state prediction. Afterwards, a probability dependent switching control law is proposed. Although the large data transmission delays emerge in the forward and backward channels, the packet disordering in the backward channel can be naturally excluded thus the actuator always receives valid control command during each sampling period with correct time sequence. The proposed NPC not only guarantees the global uniform exponential stability of the overall networked system but also brings merits in tolerating locally unstable sub‐systems. Numerical examples are provided to demonstrate the effectiveness and improvements of the proposed method.

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“…the system (1) achieves the GUES under the predictor ( 2), ( 3), ( 4) with the controller (7); where −1…”

Section: Design Methods Of the Predictor And Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictor‐based switching control for networked systems

Cui

2023

Intl J Robust & Nonlinear

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show abstract

“…where 0 < ρ < 1 is a penalty factor, which is added to improve the flexibility of law (8), and Q (l − 1, k) is defined as…”

Section: A Event-triggered Estimation Of Ppdmentioning

confidence: 99%

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems

Zhao

2024

IEEE Trans. Neural Netw. Learning Syst.

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In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo partial derivative technique. Then we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

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“…The sliding mode control approach was employed to overcome the consensus problems of higher‐order MASs with constant delays under a fixed and directed graph in Reference 21. In References 32 and 33, predictor‐feedback compensation mechanism was introduced to deal with time‐varying communication delays under switching topologies. The formation‐containment problems were solved in Reference 34.…”

Section: Introductionmentioning

confidence: 99%

Robust time‐varying formation control for uncertain multi‐agent systems with communication delays and nonlinear couplings

Yang,

Shi,

Zhong

2023

Intl J Robust & Nonlinear

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This paper investigates the robust time‐varying formation control problems for a class of higher‐order multi‐agent systems subject to communication delays and heterogeneous uncertainties accounting for not only the parameter perturbations, nonlinearities and external disturbances occurring in the node local dynamics but also the nonlinear couplings in the interconnections of different nodes. For both cases of partially known and unknown time‐varying communication delays, we propose a unified formation control protocol, which incorporates a nominal controller to achieve the desired formation and a compensating signal to restrain the influences of uncertainties. Based on the signal compensation theory and Lyapunov–Krasovskii arguments, sufficient conditions to achieve the desired time‐varying formation are derived in terms of LMI. Thereafter, an explicit expression to describe the formation reference dynamics is yielded. Moreover, an algorithm is concluded to design the presented formation protocol in four steps. It is proven that the formation error can be made as small as desired under the designed controller despite the uncertainties and communication delays. Numerical simulation results are shown to demonstrate the effectiveness of our proposed control schemes.

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Predictor-feedback synthesis in coordinate-free formation control under time-varying delays

Cited by 8 publications

References 26 publications

Predictor‐based switching control for networked systems

Predictor‐based switching control for networked systems

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems

Robust time‐varying formation control for uncertain multi‐agent systems with communication delays and nonlinear couplings

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