Adaptive Hierarchical Sliding Mode Control with Input Saturation for Attitude Regulation of Multi-satellite Tethered System

Ma, Zhiqiang; Sun, Guanghui

doi:10.1007/s40295-016-0101-5

Cited by 21 publications

(4 citation statements)

References 42 publications

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“…In this article, since the saturation and dead-zone parameters are usually unknown, the effects of these two nonlinearities are considered as parts of the lumped uncertainty. Previously, to compensate the input saturation, multiple adaptive control structures have been designed based on defining compensation gains 25–29 and auxiliary systems. 30–33 However, compared to the proposed approach, the computational burden will be increased by employing the compensation gains or the auxiliary systems in the adaptive controller design.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy sliding-mode control of under-actuated systems with unknown input gain function

Mousavi,

Markazi

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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This research addresses the design of an Adaptive Fuzzy Sliding-Mode Control (AFSMC) for a group of nonlinear under-actuated systems with unknown input gain functions. In the proposed approach, to provide a structure for controlling the under-actuated subsystem, the sliding manifold corresponding to the whole dynamic system is derived based on the backstepping technique. In addition, two separate fuzzy inference systems are utilized for approximating the system’s internal dynamics and the unknown input gain function. In order to derive the robust part of the controller, a novel method is proposed to define the upper bound of the uncertain term in the form of a state-dependent second-degree polynomial. It should be mentioned that the output vectors of the fuzzy systems and the coefficients of the robust part of the controller are obtained by the designed adaptation laws. The asymptotic stability analysis for the proposed AFSMC structure is provided by the second theorem of Lyapunov and Barbalat’s lemma. In the end, the AFSMC effectuality is confirmed by the depth control of a REMUS Autonomous Underwater Vehicle (AUV).

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Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy sliding-mode control of under-actuated systems with unknown input gain function

Mousavi,

Markazi

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…In order to design adaptive control schemes in the presence of input saturation, several methods have been proposed based on defining compensation gains [31][32][33][34][35] and auxiliary systems. [36][37][38][39] Furthermore, for nonlinear MASs in the non-strict-feedback form under input saturation, distributed adaptive control schemes have been designed to achieve leader-follower synchronization, 40 finite-time prescribed performance leader-follower tracking, 41 and containment.…”

Section: Introductionmentioning

confidence: 99%

An adaptive fuzzy sliding‐mode control method for leader‐follower consensus of uncertain non‐square nonlinear systems

Mousavi

Markazi

2022

Adaptive Control & Signal

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Summary This study presents a new adaptive fuzzy sliding‐mode control method for the leader‐follower consensus problem of uncertain non‐square nonlinear systems in the presence of input saturation and dead‐zone. In the proposed approach, to transform the dynamic equations of every agent into fully‐actuated multi‐input/multi‐output nonlinear systems, some auxiliary inputs are defined based on the weighted pseudo‐inverse theory. Furthermore, the effects of input nonlinearities and the introduced terms due to the system transformation are considered as part of the unknown nonlinear dynamic model. The auxiliary input vector of each agent consists of a fuzzy term employed to approximate the unknown nonlinear model and a robust term to compensate for any possible mismatches. Therefore, by employing the adaptation laws proposed based on a Lyapunov function, the output vectors of the fuzzy systems and the upper bounds of the approximation errors are computed. The closed‐loop asymptotic stability is proved using the second Lyapunov theorem and Barbalat's lemma. Furthermore, the effectiveness of the proposed method is verified by the leader‐follower consensus control of a group of REMUS autonomous underwater vehicles.

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“…As sensor technology advances, communication and network technology, when solving complex practical problems, the ability of communication and coordination between various agents in a multiagent system (MAS) is used to solve the problem that a single agent cannot complete complex task [1,2]. In recent years, MASs's cooperative control has been widely used in the industry, such as formation control for drones [3], satellite attitude control [4], and congestion control of communication networks [5]. At present, the main problems are collaborative control of MASs cluster control [6], formation control [7], and consensus control [8].…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

Wang

Dai

et al. 2021

Complexity

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This paper considers the consensus control problem of nonlinear spatial-temporal hyperbolic partial difference multiagent systems and parabolic partial difference multiagent systems with time delay. Based on the system’s own fixed topology and the method of generating the desired trajectory by introducing virtual leader, using the consensus tracking error between the agent and the virtual leader agent and neighbor agents in the last iteration, an iterative learning algorithm is proposed. The sufficient condition for the system consensus error to converge along the iterative axis is given. When the iterative learning number k approaches infinity, the consensus error in the sense of the L 2 norm between all agents in the system will converge to zero. Furthermore, simulation results illustrate the effectiveness of the algorithm.

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Adaptive Hierarchical Sliding Mode Control with Input Saturation for Attitude Regulation of Multi-satellite Tethered System

Cited by 21 publications

References 42 publications

Adaptive fuzzy sliding-mode control of under-actuated systems with unknown input gain function

Adaptive fuzzy sliding-mode control of under-actuated systems with unknown input gain function

An adaptive fuzzy sliding‐mode control method for leader‐follower consensus of uncertain non‐square nonlinear systems

Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

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