Leader–Follower Formation Learning Control of Discrete-Time Nonlinear Multiagent Systems

Shi, Haotian; Wang, Min

doi:10.1109/tcyb.2021.3110645

Cited by 28 publications

(9 citation statements)

References 53 publications

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“…By reusing the stored knowledge, a novel ETNLC scheme was constructed to fulfill the same or similar control tasks, which improved the transient control performance and alleviated the communication burden. On the basis of the ETNLC strategy presented in this paper, there are some interesting future research topics, including: (i) ETNLC for more general systems, such as switched nonlinear systems 55 or multi-agent systems 56 with state constraints; (ii) ETNLC for practical systems including underactuated surface vessels 57 and robotic manipulator systems.…”

Section: Discussionmentioning

confidence: 99%

Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance

Wang

Yang

2022

Intl J Robust & Nonlinear

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This paper is concerned with the event-triggered neural learning control for strict-feedback nonlinear systems (SFNSs) subject to predefined tracking performance. A system transformation approach is utilized to transform the original SFNSs into the normal form, which makes it easily to verify the convergence of neural weights since only one neural network (NN) approximator is employed. Then, a novel finite-time performance function is first proposed to characterize the performance constraints of tracking error, thus guaranteeing the prescribed tracking performance. On this basis, a new lemma is given, which is crucial to ensure the stability of the closed-loop system. By using the error transformation technique, the constrained tracking control problem is converted into the stabilization problem of unconstrained error system. Subsequently, by introducing the first-order sliding mode differentiator into the backstepping procedure and with the help of the high-gain observer, a novel adaptive neural tracking control scheme is presented to ensure that the prescribed tracking performance is satisfied and all closed-loop signals are ultimately bounded. After ensuring the satisfaction of the partial persistent excitation condition for radial basis function NN, the neural weight estimates are verified to converge to the ideal values, and then the convergent weights are stored as the constant values. By utilizing the stored knowledge, an event-triggered neural learning controller is constructed to accomplish the same or similar control tasks, which can effectively improve the transient control performance, save the communication resource, and relieve the online computation at the same time. Finally, the effectiveness of the presented scheme is testified by numerical and practical examples.

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Section: Discussionmentioning

confidence: 99%

Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance

Wang

Yang

2022

Intl J Robust & Nonlinear

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“…Moreover, the event-triggering mechanism (26) is properly introduced to reduce communication burden between agents. Compared with the existing schemes, 40,42,44,[49][50][51] the novel weight updating law (25) designed reduces the storage space of the convergent NN weights, brings convenience to knowledge reuse, and enhances the learning generalization ability. Remark 4.…”

Section: Distributed Adaptive Nn Control Design Using Internal and Ex...mentioning

confidence: 99%

“…1. Compared with the n-step-interval independent weight updating process in the existing NN weight updating law 40,42,44,[49][50][51] based on the NSFP method, the system decomposition method is proposed and further a virtual undirected and connected interconnection topology is established among n independent subsystems of each agents, which provides an effective way to connect independently updating NN estimated weights in different time series spaces. 2.…”

Section: Introductionmentioning

confidence: 99%

“…3. Unlike the NN convergence result, 40,42,44,[49][50][51] in which each NN estimated weight converges to an ideal weight sequence, a graph multiplication operation criteria is developed based on the internal and external interaction topology. Such a criteria, combining the exponential stability conditions of DT LTV systems, verifies strictly that estimated weights of all agents converge to their joint and unique ideal values.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative learning event‐triggered control for discrete‐time nonlinear multi‐agent systems by internal and external interaction topology

Wen,

Wang,

Dai

2023

Intl J Robust & Nonlinear

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This paper investigates distributed cooperative learning event‐triggered control for discrete‐time strict‐feedback nonlinear multi‐agent systems with an identical system structure and different recurrent reference orbits. A clever system decomposition method is firstly proposed to divide every ‐order agent system into first‐order subsystems, which makes it possible to solve the tough problem that every estimated neural weight cannot converge to an unique ideal value based on the existing control scheme. By constructing two interaction typologies, a novel distributed cooperative weight updating law is designed by the introduction of the internal interaction terms between subsystems of every agent, the external interaction terms between agents, and the triggering mechanism between agents. With the combination of the graph multiplication operation, the consensus theory and the matrix null space property, the proposed cooperative event‐triggered control scheme guarantees that every agent can track their corresponding recurrent reference trajectories. And further the exponential convergence of all agents' neural estimated weights to a close vicinity around their mutual and unique ideal weights is verified under the condition that the directed interaction topology between agents is strongly connected and balanced. Such a weight convergence makes the proposed scheme has some significant advantages including the small data storage space, the convenient knowledge reuse, the good generalization ability, and the low communication burden. By reinvoking the saved constant weights, a static learning controller is presented for the high‐performance control of similar diversified tracking tasks. Simulation studies and some comparisons are given to show the advantages and effectiveness of the presented scheme.

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“…Consider a simple directed graph G = (V, E) with a nonempty finite set of vertices or nodes (whereby each quadrotor translational subsystem for each is a vertice It is important to introduce RBFNN because they are used for universal function approximation and they also exhibits fast learning speed. RBFNN has been used [30], [31] for smooth nonlinear function approximation. Consider the function G(x) ∈ R m , for m RBFNN nodes, Fig.…”

Section: Preliminaries 1) Graph Theorymentioning

confidence: 99%

Control of Multiple-UAV Conveying Slung Load With Obstacle Avoidance

Aliyu

El-Férik

2022

IEEE Access

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Quadrotor Unmanned Aerial Vehicles (UAVs) are generally underactuated systems and when load is attached for transportation purposes, the system complexity increases. Therefore, the need to appropriately control such systems becomes paramount as they usually navigate in cluttered environments. In this work, we conceptualize the problem of cooperative tracking control for a Multi-agent UAV load system (MUAVLs) whereby each UAV is divided into global position and local attitude subsystems. To ensure that formation is maintained in a desired path, Neural Network Graph-theoretic Distributed Adaptive Control (NNGDAC) is used for the position subsystem with a modified virtual force artificial potential field for obstacle avoidance. Another Adaptive Feedback Linearization (AFBL) controller is also designed for the attitude subsystem which is verified by simulation results.

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Leader–Follower Formation Learning Control of Discrete-Time Nonlinear Multiagent Systems

Cited by 28 publications

References 53 publications

Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance

Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance

Cooperative learning event‐triggered control for discrete‐time nonlinear multi‐agent systems by internal and external interaction topology

Control of Multiple-UAV Conveying Slung Load With Obstacle Avoidance

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