Multiple-input multiple-output Radial Basis Function Neural Network modeling and model predictive control of a biomass boiler

Alitasb, Girma Kassa; Salau, Ayodeji Olalekan

doi:10.1016/j.egyr.2023.11.063

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…RBFs are specific type of functions that deliver an output depending on the absolute difference between the input and a central parameter. RBFs have been successfully utilized as activation functions in neural networks for modeling and control of highly nonlinear systems such as biomass boilers [49], unmanned aerial vehicles [50] and new generation nuclear power plants [51].…”

Section: Hybrid Modelmentioning

confidence: 99%

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Aguilar,

Chanal,

Chamagne

et al. 2024

Energies

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The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.

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Section: Hybrid Modelmentioning

confidence: 99%

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Aguilar,

Chanal,

Chamagne

et al. 2024

Energies

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Design of an artificial neural network and proportional‐integral‐derivative controller using particle swarm optimization for Boeing 747‐400 aircraft pitch control

Mitiku,

Salau,

Sharew

2024

Adv Control Appl

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This paper presents the design of an artificial neural network (ANN) and proportional integral derivative (PID) controller using particle swarm optimization (PSO) for Boeing 747‐400 aircraft pitch control (APC). The combinations of disturbance, open loop unstable and nonlinear dynamics are major problems in a Boeing 747‐400 commercial aircraft. This paper investigates the control mechanism of pitch angle control of Boeing 747‐400 with small disturbance theory linearization methods and ANN based non‐linear controllers. A PID controller is tuned by PSO, whereas the PID is tuned by graphical user interface (GUI) when compared with an ANN controller. The controller for this system was designed using an ANN controller and PID tuned using a recent optimization technique such as the PSO method with integral square error (ISE) as an objective function. A comparative study of the time domain performances of the pitch control of the Boeing 747‐400 commercial aircraft was presented. The ANN controller outperformed the PID‐PSO and PID‐GUI controllers in terms of system performance, including rising time (tr), settling time (ts), percentage overshoot (percent OS), and steady state error, across various elevator deflection angles. Basically, the percentage overshoot and steady state error were 0% and 0 respectively, indicating that the ANN controller achieved an improvement of 100%. Various parameters were compared with the PID‐GUI, PID‐PSO, and ANN controllers for pitch control of the Boeing 747‐400 air craft. The ANN controller architecture comprises of two input neurons, two hidden layer neurons, and one output layer neuron. The simulation was performed using Matlab/Simulink. The results show that the PID‐PSO controller was improved by the ANN controller and the performance specifications of the aircraft obtained by the ANN controller were satisfactory.

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Position and Speed Estimation for a Surface-Mount PMSM using RBFNN Observer with Stability Guarantee

El-bakkouri,

Mansouri,

Ouadi

et al. 2024

IFAC-PapersOnLine

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Multiple-input multiple-output Radial Basis Function Neural Network modeling and model predictive control of a biomass boiler

Cited by 6 publications

References 15 publications

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Design of an artificial neural network and proportional‐integral‐derivative controller using particle swarm optimization for Boeing 747‐400 aircraft pitch control

Position and Speed Estimation for a Surface-Mount PMSM using RBFNN Observer with Stability Guarantee

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