3-D Learning-Enhanced Adaptive ILC for Iteration-Varying Formation Tasks

Yu, Hui; Chi, Ronghu; Huang, Biao; Hou, Zhongsheng

doi:10.1109/tnnls.2019.2899632

Cited by 35 publications

(18 citation statements)

References 35 publications

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“…Remark 1: The above assumptions are there fundamental assumptions of DBCILC approach and the reasonability of them have been discussed in [8], [23] and [38].…”

Section: B Problem Formationmentioning

confidence: 99%

“…It is noted that most of the schemes mentioned above need to establish neural networks to design controllers, which makes preparing the external testing signals and training processes inescapable. Recently, some useful results have been reported for unknown multiagent systems, such as Model-Free Adaptive Control (MFAC) [23]- [24], Q-Learning [25]- [27], Iterative Feedback Tuning (IFT) [28]- [29], Simultaneous Perturbation Stochastic Approximation (SPSA) [30]- [31], Iterative Learning Control (ILC) [32]- [38], Virtual Reference Feedback Tuning (VRFT) [39].…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [37] research the MFAILC scheme for consensus tracking, where a general dynamic linearization mold is introduced to estimate the dynamic of MASs with the disturbance input. An interesting research presented in [38] adds a space dimension, where iterative variations are compensated which can improve the tracking performance and the speed for MASs formation control. Moreover, several other novel instances are investigated in [40]- [41].…”

Section: Introductionmentioning

confidence: 99%

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Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

Zhao

2020

IEEE Access

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This paper explores a data-driven distributed bipartite consensus tracking (DBCT) problem for discrete-time multi-agent systems (MASs) with coopetition networks under repeatable operations. To solve this problem, a time-varying linearization model along the iteration axis is first established by using the measurement input and output (I/O) data of agents. Then a data-driven distributed bipartite consensus iterative learning control (DBCILC) algorithm is proposed considering both fixed and switching topologies. Compared with existing bipartite consensus, the main characteristic is to construct the proposed control protocol without requiring any explicit or implicit information of MASs' mathematical model. The difference from existing iterative learning control (ILC) approaches is that both the cooperative interactions and antagonistic interactions, and time-varying switching topologies are considered. Furthermore, through rigorous theoretical analysis, the proposed DBCILC approach can guarantee the bipartite consensus reducing tracking errors in the limited iteration steps. Moreover, although not all agents can receive information from the virtual leader directly, the proposed distributed scheme can maintain the performance and reduce the costs of communication. The results of three examples further illustrate the correctness, effectiveness, and applicability of the proposed algorithm. INDEX TERMS Iterative control (ILC), Bipartite consensus, Data-driven control (DDC), Multi-agent systems (MASs), Nonlinear discrete-time systems.

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“…Remark 1: The above assumptions are there fundamental assumptions of DBCILC approach and the reasonability of them have been discussed in [8], [23] and [38].…”

Section: B Problem Formationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

Zhao

2020

IEEE Access

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“…Multi-agent systems (MASs) and machine learning, two exciting trends in the robotics field, have recently attracted more and more researchers' attention due to the new epoch of artificial intelligence (AI) [1,2]. How to introduce intelligent algorithms into traditional control theories is one of the hottest and significant research topics.…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, in the past few years, with the development of machine learning, another branch of control theory has been developed that is inspired by machine learning and tries to introduce the leaning approach into traditional theories to avoid the difficulties in acquiring or estimating the dynamics of physical systems. To complete similar control tasks as those solved by MBC schemes, the new control theory works by merely using the interactive information between itself and its external environment, improving the control performance by self-leaning; this is called model-free control (MFC) or data-driven control [2,4].…”

Section: Introductionmentioning

confidence: 99%

Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology

Zhao

2020

Sensors

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This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks.

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Backstepping based adaptive iterative learning control for non‐strict feedback systems with unknown input nonlinearities

Shi,

Chen,

et al. 2024

Adaptive Control & Signal

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SummaryThe initial state inconsistency and iteration‐varying trajectory problems are considered in adaptive iterative learning control (AILC) to enhance the tracking performance of the non‐strict feedback systems with unknown input nonlinearities. Through constructing an error reference trajectory independence of the reference signal, the restrictions on the initial condition and reference trajectory are both relaxed. Subsequently, a backstepping‐based AILC methodology is systematically presented to ensure that the error reference trajectory can be followed by the actual tracking error. Integral Lyapunov functions are employed to design the recursive controllers, avoiding potential singularity problems resulting from the differentiation of gain functions. Rigorous analysis is provided without imposing constraints on the control gain functions to demonstrate tracking error convergence. Numerical simulations are included to illustrate the efficacy of the proposed method.

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3-D Learning-Enhanced Adaptive ILC for Iteration-Varying Formation Tasks

Cited by 35 publications

References 35 publications

Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology

Backstepping based adaptive iterative learning control for non‐strict feedback systems with unknown input nonlinearities

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