Passivity ANFIS-Based Control for an Intelligent Compact Multilevel Converter

Mehrasa, Majid; Babaie, Mohammad; Zafari, Ali; Al‐Haddad, Kamal

doi:10.1109/tii.2021.3049313

Cited by 32 publications

(10 citation statements)

References 25 publications

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“…Theorem 1 For the system (22), assume that (37) and (38) hold. Then for any c > 1, the parameter estimation error of the RGLS algorithm in (30)- (33) satisfies…”

Section: Online Nf Model Learningmentioning

confidence: 99%

See 1 more Smart Citation

Online neuro-fuzzy model learning of dynamic systems with measurement noise

Gu,

Lan,

Mason

2024

Nonlinear Dyn

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Model identification of nonlinear time varying dynamic systems is challenging because the system behaviours may vary significantly in different operational conditions. If the changes are insufficiently captured by training data, the trained model is unable to capture the system response well when the operational condition changes. The model performance may also be deteriorated in real-time implementation due to the noise in sensors or the environment. This paper presents a self-adaptive Neuro-Fuzzy (NF) modelling framework to address these challenges. The NF model, trained offline based on experimental data, combines the Auto-Regressive with eXogenous (ARX) models and Gaussian activation functions to capture the nonlinear system behaviours. During online implementation, the ARX model parameters are updated using new data through a recursive generalised least squares method, which embeds a noise model to eliminate effects of the noise. The online updating algorithm has a provable convergence guarantee and enables the proposed NF model to adapt to changes in system behaviours automatically. Efficacy of the algorithm is verified through two numerical examples and an experiment on a commercial automotive engine.

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“…Theorem 1 For the system (22), assume that (37) and (38) hold. Then for any c > 1, the parameter estimation error of the RGLS algorithm in (30)- (33) satisfies…”

Section: Online Nf Model Learningmentioning

confidence: 99%

“…Most of the popular NF modelling methods like the ANFIS are incapable of online learning [33,51]. The evolving learning algorithms for Takagi-Sugeno models are introduced in [2, 13,27,30,32], where new fuzzy rules are generated through online clustering.…”

Section: Introductionmentioning

confidence: 99%

Online neuro-fuzzy model learning of dynamic systems with measurement noise

Gu,

Lan,

Mason

2024

Nonlinear Dyn

View full text Add to dashboard Cite

show abstract

“…During the training session, the parameters of the ANFIS are modified and updated based on training data using hybrid Aquila arithmetic [5] and artificial bee colony optimization techniques [6]. The ANFIS model is applied on control renewable energy system [7] and estimator collaborated with intelligent passivity control is used to regulate the grid-tied inverter in various operating conditions [8]. Forecasting power production in solar cell is done using the ANFIS combined with genetic algorithm [9].…”

Section: Introductionmentioning

confidence: 99%

Application of adaptive neuro-fuzzy inference system control in power systems

I. Made,

I. Made Ari,

Agung Budi

et al. 2024

Adaptive Neuro-Fuzzy Inference System as a Universal Estimator

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An adaptive neuro-fuzzy inference system (ANFIS) is developed by combining neural-networks and fuzzy system. The ANFIS model uses the advantages possessed by the properties of neural networks and its decision making is based on fuzzy inference. The ANFIS parameters are obtained and updated by training processes. The ANFIS consists of two inputs (by Gaussian or other membership function) and an output (with constant or linear membership function). The ANFIS control is implemented by building a power system stabilizer (PSS) in power systems. The PSS function is to produce an additional stabilizing signal on the reactive mode of the generator. Training data are obtained from the systems that controlled by a conventional PSS with various conditions. The training process is carried out repeatedly until the appropriate ANFIS parameters are found. Next, the PSS based on ANFIS is applied to replace the conventional PSS on a single machine and hybrid power plants. Peak overshoot and settling time of the power systems are smaller and shorter. The ANFIS PSS makes the power system stability improve significantly in small-signal studies.

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“…Passivity theory has been a major concern in the field of control engineering since the 1970s (see references [20][21][22][23][24]). As an effective tool for studying system stability, passivity theory has been applied to a variety of areas, for example, fuzzy control, complexity, power systems, and signal processing.…”

Section: Introductionmentioning

confidence: 99%

The Passivity of Uncertain Fractional-Order Neural Networks with Time-Varying Delays

Liu

Han

2022

Fractal Fract

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In this paper, we study the passive problem of uncertain fractional-order neural networks (UFONNs) with time-varying delays. First, we give a sufficient condition for the asymptotic stability of UFONNs with bounded time-varying delays by using the fractional-order Razumikhin theorem. Secondly, according to the above stability criteria and some properties of fractional-order calculus, a delay-dependent condition that can guarantee the passivity of UFONNs with time-varying delays is given in the form of a linear matrix inequality (LMI) that can be reasonably solved in polynomial time using the LMI Control Toolbox. These conditions are not only delay-dependent but also order-dependent, and less conservative than some existing work. Finally, the rationality of the research results is proved by simulation.

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Passivity ANFIS-Based Control for an Intelligent Compact Multilevel Converter

Cited by 32 publications

References 25 publications

Online neuro-fuzzy model learning of dynamic systems with measurement noise

Online neuro-fuzzy model learning of dynamic systems with measurement noise

Application of adaptive neuro-fuzzy inference system control in power systems

The Passivity of Uncertain Fractional-Order Neural Networks with Time-Varying Delays

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