Sliding mode observer for nonlinear mechanical systems subject to nonsmooth impacts

Mohamed, Mchiri; Trabelsi, Karim; Belghith, Safya

doi:10.1109/ssd.2010.5585565

Cited by 4 publications

(2 citation statements)

References 18 publications

(22 reference statements)

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“…Many of them require detailed or precise knowledge of the system dynamics. For instance, the sliding mode observer (SMO) proposed in Wit and Slotine (1991) and Mohamed et al (2010) estimates the unmeasured states of a system based on the assumption of the availability of complete knowledge of the system dynamics. But, in practice, it is very difficult to get the precise model of the system, and therefore, the complete dynamical knowledge of the system (i.e.…”

Section: Introductionmentioning

confidence: 99%

A dynamical convolutional neural network–based adaptive observer for information-poor systems

Wallam

2023

Transactions of the Institute of Measurement and Control

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The objective of this paper is to propose a novel self-learning observer for effectively estimating unmeasured states of a dynamical information-poor system. By inspiring the success of convolutional neural network (CNN) in image and voice recognition applications, the dynamical convolutional neural network (DCNN) architecture (which is nonlinear-in-parameter unlike traditional basis functions–based neural network or fuzzy logic architectures) is proposed for approximating the unknown system dynamics, uncertainties, and disturbances. Instead of using an output observation error filtering method, complex weight-updating mechanism, strictly positive real (SPR) assumption, offline-tuning mechanisms, static approximation of gradients in back-propagation algorithm, or any other strong or strict requirement, the simple yet effective and novel rules for tuning DCNN weights and kernels are derived. Furthermore, the problems such as parameters over-tuning and unnecessary consumption of large amounts of computational power commonly found in traditional neuro-adaptive and fuzzy-adaptive controllers/observers are addressed and their solution is incorporated in the design of the observer. Thus, the proposed DCNN-based adaptive observer is energy-efficient. A quadratic function based on the convolution operator is also proposed which is utilized to provide the closed-loop stability analysis using Lyapunov’s approach. An example is simulated to demonstrate the performance of the proposed observer.

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Section: Introductionmentioning

confidence: 99%

A dynamical convolutional neural network–based adaptive observer for information-poor systems

Wallam

2023

Transactions of the Institute of Measurement and Control

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“…In literature, many different observers are available for estimating the unmeasured states of the process. For instance, the sliding mode observers (SMOs) proposed by References [1] and [2] require exact knowledge of the process dynamics 3 . But, in reality, many practical systems are information‐poor ; meaning that instead of complete or exact knowledge of the process dynamics, only little information about the system dynamics is available.…”

Section: Introductionmentioning

confidence: 99%

A rules‐firing strength‐based neuro‐fuzzy observer for information‐poor systems

Wallam¹

2020

Int J Intell Syst

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In this paper, a neuro-fuzzy observer (NFO) is proposed for estimating the unmeasured states of an information-poor system by relaxing the strictly positive real condition (without using filtered fuzzy basis function (FBF) and filtered output estimation error) and without using high-gain terms. To recover the performance of the observer in the absence of highgain terms, a concept of weighted fuzzy rules (or strengthened FBF) is proposed. The weighted fuzzy rules are then used to propose the concept of weighted function approximation which is then utilized in the design of NFO to estimate the unknown dynamical function. For reducing computational power and avoiding over-tuning of the weights, a concept of relayswitching is also introduced in the design of the NFO. The stability analysis of the proposed NFO is also presented using Lyapunov approach and the effectiveness of the proposed scheme is demonstrated through a simulation example.

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