2023
DOI: 10.1108/ria-04-2023-0056
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Hierarchical sliding mode-based adaptive fuzzy control for uncertain switched under-actuated nonlinear systems with input saturation and dead-zone

Shuai Yue,
Ben Niu,
Huanqing Wang
et al.

Abstract: Purpose This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances. Design/methodology/approach A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system stat… Show more

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Cited by 31 publications
(13 citation statements)
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References 61 publications
(138 reference statements)
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“…The landing decision process is categorized into three classes: feasible, cautious, and not feasible. The work of [ 11 ] proposes a hierarchical sliding mode-based adaptive fuzzy control for underactuated nonlinear systems, such as a UAV, in which there is a mismatch between the control input and the degrees of freedom being controlled that increases the difficulty of control. In [ 12 ], a decision support system is introduced utilizing fuzzy logic to aid the pilots of Boeing 747-100 aircraft.…”
Section: Introductionmentioning
confidence: 99%
“…The landing decision process is categorized into three classes: feasible, cautious, and not feasible. The work of [ 11 ] proposes a hierarchical sliding mode-based adaptive fuzzy control for underactuated nonlinear systems, such as a UAV, in which there is a mismatch between the control input and the degrees of freedom being controlled that increases the difficulty of control. In [ 12 ], a decision support system is introduced utilizing fuzzy logic to aid the pilots of Boeing 747-100 aircraft.…”
Section: Introductionmentioning
confidence: 99%
“…A property of an environment and its state signals is called the Markov, and a reinforcement learning problem that satisfies the Markov property is called a Markov Decision Process (MDP) [ 35 ]. A reinforcement learning problem is modeled as an MDP that consists of a set of actions ( ), a set of states ( ), and a reinforcement signal ( ).…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, different techniques have been applied for advanced control strategies like state-space controllers, optimal controllers, artificial neural networks, sliding mode controllers, and fuzzy logic [7,8]. Fuzzy controllers have been developing exponentially in increasingly complex applications.…”
Section: Introductionmentioning
confidence: 99%