Adaptive iterative learning reliable control for a class of non‐linearly parameterised systems with unknown state delays and input saturation

Ji, Honghai; Hou, Zhongsheng; Fan, Lingling; Lewis, Frank L.

doi:10.1049/iet-cta.2016.0209

Cited by 46 publications

(31 citation statements)

References 29 publications

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“…Figure 5 shows an exemplary block diagram for two coupled controllers with these characteristics. With respect to the general representation in Equations (9) and 10 In this case, the control input can then be defined by a consensus protocol (for a regulator problem) by ( [26], p. 26):…”

Section: Distributed Consensus Approachmentioning

confidence: 99%

“…Especially for applications with robustness requirements and partly unknown system parameters adaptive iterative learning reliable control (AILRC) is of interest (cf. e.g., [9]). Beyond that, Haidegger et al [10] present a robust cascade control approach for telerobots in space medicine which is designed with focus on the high robustness requirements in space.…”

Section: Introductionmentioning

confidence: 99%

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Combining Distributed Consensus with Robust H∞-Control for Satellite Formation Flying

Scharnagl¹,

Kempf

Schilling

2019

Electronics

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Control methods that guarantee stability in the presence of uncertainties are mandatory in space applications. Further, distributed control approaches are beneficial in terms of scalability and to achieve common goals, especially in multi-agent setups like formation control. This paper presents a combination of robust H ∞ control and distributed control using the consensus approach by deriving a distributed consensus-based generalized plant description that can be used in H ∞ synthesis. Special focus was set towards space applications, namely SFF. The presented results show the applicability of the developed distributed robust control method to a simple, though realistic space scenario, namely a spaceborne distributed telescope. By using this approach, an arbitrary number of satellites/agents can be controlled towards an arbitrary formation geometry. Because of the combination with robust H ∞ control, the presented method satisfies the high stability and robustness demands as found e.g., in space applications.

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Section: Distributed Consensus Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Distributed Consensus with Robust H∞-Control for Satellite Formation Flying

Scharnagl¹,

Kempf

Schilling

2019

Electronics

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“…16 However, due to the complexity of the train operation, the static auxiliary compensator is slightly insufficient in the performance of dynamic adjustment. 17 Ji et al 18 presented an adaptive iterative learning control strategy, which compensated for the nonlinear effects brought by input saturation and solved the problem of time-varying time lag in highspeed train operation. Li et al 19 designed an antisaturation controller based on a static auxiliary compensator to realise the problem of multi-car and multitrain input saturation separately.…”

Section: Introductionmentioning

confidence: 99%

Tracking control via sliding mode for heavy-haul trains with input saturation

Yang

Zhang

et al. 2020

Measurement and Control

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To address the tracking control problem of heavy-haul trains (HHTs) with input saturation during operation, an anti-saturation sliding mode (SMES) control method based on dynamic auxiliary compensator (DAC) is presented. Firstly, an HHT model with nonlinear coupling and uncertain disturbances is built. Secondly, a new type of DAC is introduced to overcome the difficulty of traditional dynamic auxiliary compensator (TDAC) with a large upper bound on the compensation signal. Finally, an anti-saturation SMES control algorithm is designed to reduce the influence of input saturation on the tracking accuracy of each carriage. Simulation results verify the effectiveness of the algorithm in terms of tracking accuracy, anti-interference, and anti-saturation.

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“…Iterative learning control (ILC) is an effective approach to realize accurate tracking performance under the repetitive environment [14], [15]. ILC for an individual system, [16] designed an ILC algorithm for the uncertain nonlinear systems having IS, and [17]- [19] proposed the adaptive ILC method for non-linearly parameterised systems having IS. Nevertheless, the results of [16]- [19] having IS were performed under the identical initial conditions (i.i.c) [20] which was a rigorous condition and may not be realized in practice.…”

Section: Introductionmentioning

confidence: 99%

“…ILC for an individual system, [16] designed an ILC algorithm for the uncertain nonlinear systems having IS, and [17]- [19] proposed the adaptive ILC method for non-linearly parameterised systems having IS. Nevertheless, the results of [16]- [19] having IS were performed under the identical initial conditions (i.i.c) [20] which was a rigorous condition and may not be realized in practice. In addition, until now, ILC has been extensively used to study MASs without IS [21]- [23] for first-and second-order MASs and [24]- [26] for high-order nonlinear MASs (HON-MASs), where [21]- [25] were performed under the alignment initial condition (a.i.c) (a more practical initial condition), whereas [26] assumed the initial state learning containing the global information, and [27] investigated the first-order MASs with IS using a.i.c.…”

Section: Introductionmentioning

confidence: 99%

Distributed Robust Adaptive Learning Coordination Control for High-Order Nonlinear Multi-Agent Systems With Input Saturation

Yang

2020

IEEE Access

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The paper involves the distributed robust adaptive learning coordination control for highorder nonlinear multi-agent systems, where the leader has nonzero input and followers are subject to input saturation. To solve the problem, two initial assumptions concerning initial state learning and alignment initial condition are introduced, and the distributed learning protocols as well as parameter adaptive laws are designed. It should be noted that the protocols proposed under initial state learning containing the global information are not fully distributed, while the fully distributed protocols can be obtained by the alignment initial condition. Through the rigorous analysis, it is proved that each follower can perfectly track the leader on a finite time interval under both two assumptions. Then, the consensus results under the alignment initial condition are generalized to formation control and two simulation examples verify the correctness and feasibility of the proposed algorithms. INDEX TERMS Adaptive coordination control, iterative learning control, high-order nonlinear multi-agent systems, input saturation.

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Adaptive iterative learning reliable control for a class of non‐linearly parameterised systems with unknown state delays and input saturation

Cited by 46 publications

References 29 publications

Combining Distributed Consensus with Robust H∞-Control for Satellite Formation Flying

Combining Distributed Consensus with Robust H∞-Control for Satellite Formation Flying

Tracking control via sliding mode for heavy-haul trains with input saturation

Distributed Robust Adaptive Learning Coordination Control for High-Order Nonlinear Multi-Agent Systems With Input Saturation

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