Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

External disturbance and random noise put forward higher requirements for the stability of motors in daily work. Here, a neural network sliding mode method is showed in this article. First, a new sliding mode surface change rate and a disturbance compensation measure are defined. They reduce the gain value to prevent chattering. Second, to improve the neural network, the adaptive theory is used to update the weights, and a peak-valley measure is designed to limit the output range. Third, the stability of this method is proved by mathematical derivation of a typical nonlinear system. Last, a typical motor model is built on the Simulink, and the influence of external disturbance and random noise on the motor is analyzed. The result shows the effectiveness of this method.

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“…In Equation (),

c={\left[{c}_1,{c}_2,\dots, {c}_{\left(n-1\right)},1\right]}^T

satisfies Hurwitz's polynomial, namely, Equation () 22–24

$$ {p}^{\left(n-1\right)}+{c}_{n-1}{p}^{\left(n-2\right)}+\cdots +{c}_2p+{c}_1.

…”

Section: Neural Network Sliding Mode Methodsmentioning

confidence: 99%

A neural network sliding mode method for nonlinear motors

Zou

Zheng

2022

Optim Control Appl Methods

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“…Remark 7. Due to ∀t ∈ [t κ , t κ+1 ), ˙ψ(t) = 0, ˙χ 1 will not appear in (13). In addition, the differential term of ˙χ 2 is not involved in the design process of a NN-based state observer (11), and ˙χ 1 will only appear in the theoretical derivation and stability analysis.…”

Section: Remarkmentioning

confidence: 99%

“…For the issue of CKC, many effective design approaches have been reported, such as active disturbance rejection control (ADRC) [10], PID [11], backstepping [12], sliding mode control (SMC) [13], and H∞ [14]. Furthermore, intelligent ADRC [15], fuzzy PID [16,17], neural PID [18], adaptive backstepping [19], and non-fragile H∞ [20] are proposed to resolve the issue of CKC for USVs.…”

Section: Introductionmentioning

confidence: 99%

Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data

Zhi,

Pan,

Zhu

2023

Applied Sciences

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This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transmission, are taken into account. To tackle the issue of network resource constraints, an event-sampled scheme is developed to obtain the course data, and a novel event-sampled adaptive neural-network-based state observer (NN–SO) is developed to achieve the state reconstruction of discontinuous yaw. Using a backstepping design method, an event-sampled mechanism, and an adaptive NN–SO, an adaptive neural output feedback (ANOF) control law is designed, where the dynamic surface control technique is introduced to solve the design issue caused by the intermission course data. Moreover, an event-triggered mechanism (ETM) is established in a controller–actuator (C–A) channel and a dual-channel event-triggered adaptive neural output feedback control (ETANOFC) solution is proposed. The theoretical results show that all signals in the closed-loop control system (CLCS) are bounded. The effectiveness is verified through numerical simulations.

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“…González-Prieto et al [2] investigated the course keeping control problem for a USV in the presence of unknown disturbances and system uncertainties. The authors developed the adaptive integral non-linear controller based on the sliding mode surface with adaptive gains.…”

Section: Papers Detailsmentioning

confidence: 99%