A NARMA-L2 Controller Based on Online LSSVR for Nonlinear Systems

Şen, Gökçen Devlet; Günel, Gülay Öke

doi:10.1007/978-3-030-85318-1_14

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…This bench included a fuel cell emulator, two photovoltaic modules of 200 (W), and Li-ion batteries of 24 (V). In addition, four educational Semikron converters, which contained three IGBT arms and a The dynamic model inputs of the NARMA-L2, defined in Equation (37), are represented in the DC bus energy profile. The test and validation results shown in Figure 10 indicate that the control signal managed by the neuro-controller offers high precision concerning the DC bus adjustment.…”

Section: Practical Resultsmentioning

confidence: 99%

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Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory

et al. 2023

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This paper proposes a practical solution to address the energy management issue in fuel cell hybrid electric vehicles (FCHEVs). This solution revolves around a powertrain system that contains a fuel cell (FC) as the main supply, a photovoltaic cell (PC) as the secondary energy source, and a battery bank (Batt) as backup storage to compensate for the FC’s low response rate. The energy in this hybrid powertrain system alternated between the designated elements and the load via a DC bus, and to maintain a stable output voltage, the DC link was adjusted using a nonlinear approach that is based on the flatness theory and the nonlinear autoregressive moving average (NARMA-L2) neuro-controller. As for the current regulation loops, the sliding mode technique was employed to attain the high dynamic of the reference signals produced by the energy manager loop. To validate the accuracy of the proposed energy management approach (EMA), a test bench was equipped with digital, electronic circuits and a dSPACE DS-1104 unit. This experimental bench contained a fuel cell emulator FC of 1200 W and 46 A, lithium-ion batteries of 24 V, and a solar source capable of 400 W. The obtained results, indeed, attested to the validity of the approach used, yielding a notable performance during multiple charge variations. This ultimately demonstrated that the management approach enhanced the efficiency of the hybrid powertrain.

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Section: Practical Resultsmentioning

confidence: 99%

“…The NARMA-L2 is one of the most prominent among the ANN approaches due to its implementation simplicity and offline training, referring to its ability to approximate the nonlinear functions. Theoretically, this type of neuro-controller does not necessitate a precise mathematical description of the model to be controlled [36,37].…”

Section: Introductionmentioning

confidence: 99%

Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory

et al. 2023

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“…The dynamics of the NARX model should be decomposed into a N ARM A − L2 model in order to apply an inverse optimal controller [89]- [92]. Therefore, obtaining the The online LSSVR method has been used both to obtain the NARX model and then to convert it to the N ARM A − L2 model.…”

Section: Lssvr Based Narma-l2 Model Of Nonlinear Non-affine Systemsmentioning

confidence: 99%

A Novel Adaptive LSSVR-Based Inverse Optimal Controller With Integrator for Nonlinear Non-Affine Systems

Sanci,

Uçak,

Günel

2023

IEEE Access

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In this study, a novel inverse optimal controller based on NARMA-L2 modelling technique and online least squares support vector regression (LSSVR) method has been proposed for nonlinear nonaffine systems. Firstly, the nonlinear autoregressive with exogenous inputs (NARX) model of the system is obtained using online LSSVR method, then this model is decomposed into NARMA-L2 submodels. Hence, the non-affine system model is converted to a nonlinear affine system model. The obtained NARMA-L2 submodels are used in computing the inverse optimal control law. Furthermore, the parameters of the inverse optimal controller have also been optimized online using the Levenberg-Marquadt algorithm. The performance of the proposed LSSVR based inverse optimal controller using NARMA-L2 model has been evaluated by simulations carried out on two benchmark systems, and the results show that the LSSVR based NARMA-L2 model and inverse optimal controller attain good modelling and control performances.

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“…Here, a technique for converting from nonlinear autoregressive with exogenous (NARX) model to the NARMA-L2 model is introduced. This method is extended to two-input–two-output nonlinear systems in Şen and Günel (2022). Later, the method proposed in Uçak and Günel (2016) has been improved to obtain the NARMA-L2 submodels directly in Uçak and Günel (2021).…”

Section: Introductionmentioning

confidence: 99%

“…In this proposed work, the approach in the previous studies presented in Uçak and Günel (2016) and Şen and Günel (2022) is used as a modification to the conventional CTC. Instead of online SVR, online LSSVR is utilized due to its computational simplicity.…”

Section: Introductionmentioning

confidence: 99%

NARMA-L2–based online computed torque control for robotic manipulators

Şen

Günel

2023

Transactions of the Institute of Measurement and Control

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Computed torque is an effective method for control of robotic manipulators. In this paper, a novel approach is proposed for computed torque control method, and also, it is integrated with online least squares support vector regression algorithm. The motive is to improve control performance and to build an adaptive architecture where no prior information on system model is required; the model is obtained continuously using a machine learning technique. There are two main contributions of the proposed work: the first one is the formulation of the computed torque control law using the nonlinear autoregressive moving average-L2 control methodology and the second contribution is the implementation of online least squares support vector regression method in estimating the dynamical model of the system and calculating the control law. The performance of the proposed method has been evaluated by simulations on a two-link robotic arm. The robustness of the method has also been evaluated for cases with disturbance and model uncertainty. Comparison with an existing computed torque control approach in the literature demonstrates the superiority of the proposed approach. Thus, it can be concluded that the method can be effectively used for first-order and second-order nonlinear systems without inherent instability.

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A NARMA-L2 Controller Based on Online LSSVR for Nonlinear Systems

Cited by 4 publications

References 15 publications

Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory

Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory

A Novel Adaptive LSSVR-Based Inverse Optimal Controller With Integrator for Nonlinear Non-Affine Systems

NARMA-L2–based online computed torque control for robotic manipulators

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