Adjacent-Agent Dynamic Linearization-Based Iterative Learning Formation Control

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

doi:10.1109/tcyb.2019.2899654

Cited by 56 publications

(21 citation statements)

References 36 publications

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“…These conclusions are drawn for the process, fuzzy controller, optimization problem and dynamic regime considered in this paper. Other processes, controllers, optimization problems and dynamic regimes are expected to lead to different conclusions; challenging processes in this regard are those considered in [57][58][59][60][61][62][63][64].…”

Section: Validation and Comparisonmentioning

confidence: 99%

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Precup¹,

David²,

Roman³

et al. 2021

IJCIS

104

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This paper suggests five new contributions with respect to the state-of-the-art. First, the optimal tuning of cost-effective fuzzy controllers represented by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs) is carried out using a fresh metaheuristic algorithm, namely the Slime Mould Algorithm (SMA), and a fuzzy controller tuning approach is offered. Second, a relatively easily understandable formulation of SMA is offered. Third, a real-world application of SMA is given, focusing on the optimal tuning of TSK PI-FCs for nonlinear servo systems in terms of optimization problems that target the minimization of discrete-time cost functions defined as the sum of time multiplied by squared control error. Fourth, using the concept of improving the performance of metaheuristic algorithms with information feedback models, proposed by Wang and Tan, Improving metaheuristic algorithms with information feedback models, IEEE Trans. Cybern. 49 (2019), 542-555, Gu and Wang, Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization, Fut. Gen. Comput. Syst. 107 (2020), 49-69, and Zhang et al., Enhancing MOEA/D with information feedback models for large-scale many-objective optimization, Inf. Sci. 522 (2020), 1-16, new metaheuristic algorithms are introduced in terms of inserting the model F1 in SMA and other representative algorithms, namely Gravitational Search Algorithm (GSA), Charged System Search (CSS), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA). Fifth, the real-time validation of the cost-effective fuzzy controllers and their tuning approach is performed in the framework of angular position control of laboratory servo system. The comparison with other metaheuristic algorithms that solve the same optimization problem for optimal parameter tuning of cost-effective fuzzy controllers suggestively highlights the superiority of SMA. Experimental results are included.

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Section: Validation and Comparisonmentioning

confidence: 99%

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Precup¹,

David²,

Roman³

et al. 2021

IJCIS

104

View full text Add to dashboard Cite

show abstract

“…Iterative learning control is very suitable for devising the tracking controller of high-speed trains that run periodically on the same railway, e.g., the same tunnels, slopes, bridges [1], [2], [3], [4], [5], [6], [7], [8], [9]. However, on account of the damping effect of wheel-rails, couplers, etc., as well as the disturbance of external environments, the speed delay occurs frequently during the operation of train, thus attenuating the control performance of trains.…”

Section: Introductionmentioning

confidence: 99%

A Novel Iterative Learning Approach for Tracking Control of High-Speed Trains Subject to Unknown Time-Varying Delay

Chen

Huang

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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“…For the control problems of unknown systems, datadriven control methodologies are a concerned research topic, in which the model free adaptive control (MFAC), proposed by Hou in [17] and further developed in [18], is valuable. The MFAC has been extended from the original single-input single-output systems [19], [20] and multi-input multi-output systems [21], [22] to nonlinear MASs [23]- [25]. Besides, the dynamic linearization based control methods have been successfully applied to many practical applications, such as servo motor systems [26] and exoskeleton robotic systems [27].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Distributed Adaptive Control Approach for Nonlinear Multi-Agent Systems

Jin

Liu

et al. 2020

IEEE Access

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In this paper the distributed leader-follower consensus tracking problem is investigated for unknown nonlinear non-affine discrete-time multi-agent systems. Via a dynamic linearization method both for the agent system and the local ideal distributed controller, a distributed adaptive control scheme is proposed in this paper using the Newton-type optimization method. The proposed approach is data-driven since only the local measurement information among neighboring agents is utilized in the control system design. The consensus tracking stabilities of the proposed approach are rigorously guaranteed in the cases of fixed and switching communication topologies. The simulations are conducted to verify the effectiveness of the proposed approach. INDEX TERMS Dynamic linearization, data-driven control, adaptive control, multi-agent systems, consensus tracking.

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Adjacent-Agent Dynamic Linearization-Based Iterative Learning Formation Control

Cited by 56 publications

References 36 publications

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

A Novel Iterative Learning Approach for Tracking Control of High-Speed Trains Subject to Unknown Time-Varying Delay

A Data-Driven Distributed Adaptive Control Approach for Nonlinear Multi-Agent Systems

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