Certainty-Equivalence-Based Sensorless Robust Sliding Mode Control for Maximum Power Extraction of an Uncertain Photovoltaic System

Abstract

Photovoltaic (PV) arrays and their electronic converters are subject to various environmental disturbances and component-related faults that affect their normal operations and result in a considerable energy loss. Therefore, it is ever demanding to design such closed-loop operating algorithms that tolerate faults, present acceptable performance, and avoid wear and tear in the systems. In this work, the core objective is to extract maximum power from a PV array subject to environmental disturbances and plant uncertainties. The system is considered under input channel uncertainties (i.e., faults) along with variable resistive load and charging stations. A neuro-fuzzy network (NFN)-based reference voltage is generated to extract maximum power while considering variable temperature and irradiance as inputs. Furthermore, the estimated reference is tracked by the actual PV voltage under two types of controllers: certainty-equivalence-based robust sliding mode (CERSMC) and certainty-equivalence-based robust integral sliding mode (CERISMC). These strategies benefit from improving the robustness against faults (disturbances). The proposed methods use the inductor current, which is recovered via the velocity observer and the flatness property of nonlinear systems. The system’s stability is proven in the form of very appealing theorems. These claims are validated by the simulation results, which are carried out in a MATLAB environment.

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

“…Owing to its lack of stability and nonlinearity, it provides a path for testing the prototype controller. Therefore, different researchers have designed NN-based controllers to test IP [ 37 , 38 ]. A supervised NN reduces errors more efficiently and keeps the system stable [ 39 , 40 ].…”

Robust finite-time integral terminal sliding mode control design for maximum power extraction of PMSG-based standalone wind energy system

“…In this study, the critical wind turbine parameters include the air density ρ (set to 1.25 kg/m 3 ), gears ratio or transmission ratio i (equal to 7), blade radius R t (measuring 2.5 m), maximum power coefficient C p max (equal to 0.47), optimal tip-speed ratio λ opt (valued at 7), and high-speed shaft inertia J h (measured as 0.0552 kg ⋅ m 2 ) Chand et al (2023); Majout et al (2022); Khan et al (2022Khan et al ( , 2021; Ullah A. et al (2020); Alam et al (2022).…”

“…In the existing literature, robust controllers have been developed and studied for extracting maximum power P max from the wind (Aboudrar et al, 2019;Syahputra and Soesanti, 2019;Cheikh et al, 2020;Saidi et al, 2020;Khan et al, 2021;Alam et al, 2022;Khan et al, 2022;Chand et al, 2023). Some studies have focused on reactive power and maximum inverter power extraction without wind speed sensors (Abdullah et al, 2012), whereas others have explored adaptive MPPT schemes and improved control techniques for grid-connected wind turbines (Jaramillo-Lopez et al, 2016;Pan and Shao, 2020;Hawkins and McIntyre, 2021).…”

Chattering-free terminal sliding-mode tracking control for uncertain nonlinear systems with disturbance compensation

“…It eliminates the singularity problem in sliding mode control while increasing the speed of convergence (Ullah et al, 2022b). Specially, Alam et al (2022) proposed two controllers based on deterministic equivalence-based robust SMC and deterministic equivalence-based robust integral SMC for external and system fault effects. However, the above control strategies can only get bounded tracking performance.…”