Design of neural network fractional‐order backstepping controller for MPPT of PV systems using fractional‐order boost converter

In this work, a backstepping controller design for fractional-order strict feedback systems is investigated and the neural network control method is used. It is noted that in the standard backstepping design, the fractional derivative of the virtual quantity needs to be calculated repeatedly, which will lead to a sharp increase in the number of controller terms with the increase of the system dimension and finally make the control system difficult to bear. To handle the estimation error, certain robust terms in the controller at the last step are designed. The stability of the controlled system is proven strictly. In addition, the proposed controller has a simple form which can be easily implemented. Finally, in order to verify our theoretical method, the control simulation based on a fractional-order chaotic system is implemented.

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Command filtered adaptive neural network synchronization control of fractional-order chaotic systems subject to unknown dead zones

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2021.02.012 ◽

2021 ◽

Vol 358 (7) ◽

pp. 3376-3402

Author(s):

Shumin Ha ◽

Liangyun Chen ◽

Heng Liu

Keyword(s):

Neural Network ◽

Fractional Order ◽

Chaotic Systems ◽

Synchronization Control ◽

Network Synchronization ◽

Adaptive Neural Network ◽

Dead Zones

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An Optimized PV Control System Based on the Emperor Penguin Optimizer

Energies ◽

10.3390/en14030751 ◽

2021 ◽

Vol 14 (3) ◽

pp. 751

Author(s):

Mariam A. Sameh ◽

Mostafa I. Marei ◽

M. A. Badr ◽

Mahmoud A. Attia

Keyword(s):

Duty Cycle ◽

Optimization Technique ◽

Pso Algorithm ◽

Boost Converter ◽

Emperor Penguin ◽

Pv System ◽

Partial Shading ◽

Pv Systems ◽

Point Tracking ◽

Grid Connected Inverter

During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.

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Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Measurement and Control ◽

10.1177/00202940211021107 ◽

2021 ◽

pp. 002029402110211

Author(s):

Tao Chen ◽

Damin Cao ◽

Jiaxin Yuan ◽

Hui Yang

Keyword(s):

Neural Network ◽

Nonlinear Systems ◽

Sliding Mode Control ◽

Fractional Order ◽

Sliding Mode ◽

Tracking Error ◽

Adaptive Neural Network ◽

Dynamic Surface ◽

Mode Control ◽

The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

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