Dynamic Sliding Mode Control of DC-DC Converter to Extract the Maximum Power of Photovoltaic System Using Dual Sliding Observer

This paper concerns the maximum power extraction of a photovoltaic generator system (PGS). The PGS consists of single photovoltaic (PV) cells. To improve the efficiency of a PGS, it is necessary to work within its maximum power point (MPP). In a PGS, output power is dependent on solar irradiance and the operating temperature and, therefore, MPP would be varied. To address this problem, a converter should be placed after the PGS and a smooth control signal should be used to adjust its duty cycle. The other challenge of a total system, i.e., PGS and converter, is the uncertainty involved. To overcome this uncertainty, a dynamic sliding mode control (SMC) can be used to regulate the smooth duty cycle. The low-pass integrator before the system can remove the chattering in dynamic SMC. However, due to the integrator, the states of the system increase and, hence, we propose a dual sliding observer (DSO) to estimate this added state. For a reliable comparison with the conventional SMC, the same proposed DSO can be applied in both dynamic and conventional SMC. The provided comparison shows the effectiveness of dynamic SMC in chattering suppression and real implementation with respect to conventional SMC

Higher Order Sliding Mode Control of MIMO Induction Motors: A New Adaptive Approach

In this paper the objective is to force the outputs of nonlinear nonaffine multi-input multi-output (MIMO) systems to track those of a linear system with the desired properties. The approach is based on designing higher order sliding mode controller (HOSMC) with the definition of a new proportional-integral (PI) sliding surface. To this end, a linear state feedback with an adaptive switching gain (ASG) is applied to the nonlinear MIMO systems. Therefore, the switching gain can increase or decrease based on the system conditions. Then, the chattering is completely removed using a combination of HOSMC and ASG. Moreover, the proposed procedure is independent from the upper bound of the matched uncertainty, which is in the direction of system inputs. The finite time convergence to the sliding surface is also proved, which provides an invariance property in finite time. Note that invariance is the most important property of SMC. Finally, the general model of MIMO induction motors (IM) is used to address and to verify the proposed controller.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ II (ELKARTEK KK-2023/00051), to the Diputación Foral de Álava (DFA), through the project CONAVANTER, to the UPV/EHU, through the project GIU20/063, and to the MobilityLab Foundation (CONV23/14. Proy. 16) for supporting this work

Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization

To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi–Sugeno–Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory

An Artificial Neural Network for Solar Energy Prediction and Control Using Jaya-SMC

In recent years, researchers have focused on improving the efficiency of photovoltaic systems, as they have an extremely low efficiency compared to fossil fuels. An obvious issue associated with photovoltaic systems (PVS) is the interruption of power generation caused by changes in solar radiation and temperature. As a means of improving the energy efficiency performance of such a system, it is necessary to predict the meteorological conditions that affect PV modules. As part of the proposed research, artificial neural networks (ANNs) will be used for the purpose of predicting the PV system’s current and voltage by predicting the PV system’s operating temperature and radiation, as well as using JAYA-SMC hybrid control in the search for the MPP and duty cycle single-ended primary-inductor converter (SEPIC) that supplies a DC motor. Data sets of size 60538 were used to predict temperature and solar radiation. The data set had been collected from the Department of Systems Engineering and Automation at the Vitoria School of Engineering of the University of the Basque Country. Analyses and numerical simulations showed that the technique was highly effective. In combination with JAYA-SMC hybrid control, the proposed method enabled an accurate estimation of maximum power and robustness with reasonable generality and accuracy (regression (R) = 0.971, mean squared error (MSE) = 0.003). Consequently, this study provides support for energy monitoring and control

Variable speed wind turbine control scheme using a robust wind torque estimation

This work proposes a robust controller for a variable speed wind turbine system with a doubly feed induction generator. The controller aims at tracking the optimal speed of the wind turbine so that extracts the maximum power from the wind. Also, a robust aerodynamic torque observer is proposed in order to avoid the use of wind speed sensors. This torque observer allows to estimate the aerodynamic torque to be used by the controller in order to calculate the value of the optimal reference speed for the wind turbine. The vector control theory is applied in the present approach, and thereby the stator flux-oriented control is used for controlling the speed of the wind turbine generator. The proposed robust control law is based on sliding mode control theory, which has proved to provide good performance under system uncertainties. The stability of the proposed controller under disturbances and parameter uncertainties has been analyzed using the Lyapunov stability theory. Finally, real time experimental results show that, on the one hand, the proposed controller provides high-performance dynamic characteristics, and on the other hand, this scheme is robust with respect to the uncertainties that usually appear in this kind of systems.The authors are very grateful to the UPV/EHU by its support through the projects PPGA18/04 and UFI11/07 and to the Basque Government by its support through the project ELKARTEK KK-2017/00033

Real time observer and control scheme for a wind turbine system based on a high order sliding modes

The introduction of advanced control algorithms may improve considerably the efficiency of wind turbine systems. This work proposes a high order sliding mode (HOSM) control scheme based on the super twisting algorithm for regulating the wind turbine speed in order to obtain the maximum power from the wind. A robust aerodynamic torque observer, also based on the super twisting algorithm, is included in the control scheme in order to avoid the use of wind speed sensors. The presented robust control scheme ensures good performance under system uncertainties avoiding the chattering problem, which may appear in traditional sliding mode control schemes. The stability analysis of the proposed HOSM observer is provided by means of the Lyapunov stability theory. Experimental results show that the proposed control scheme, based on HOSM controller and observer, provides good performance and that this scheme is robust with respect to system uncertainties and external disturbances.The authors are very grateful to the Basque Government by its support through the project EKOHEGAZ (ELKARTEK KK-2021/00092), to the Diputacion Foral de Alava (DFA) by its support through the project CONAVANTER, to Gipuzkoako Foru Aldundia by its support through the project Etorkizuna Eraikiz 2019, and to the UPV/EHU by its support through the project GIU20/063

Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization

To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi–Sugeno–Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory

Design of Sliding Mode Controller for Voice Coil Motor Using Nonlinear Observer

Voice coil motors (VCM) are used in very small equipment such as mobile phone cameras and the use of a robust control feature as sliding mode control (SMC) is inevitable in them. The most important property of SMC is its invariant against matched disturbances and uncertainties which is due to the using of Sign function in input control signal. The invariant property is stronger than robustness. But, this method is not invariant with respect to the mismatched uncertainties i.e. is variant and sensitive. This is the challenge of SMC in VCM because of existents mismatched uncertainty in their models. To solve this problem in this paper, input control signal is calculated at first via SMC and then, coefficients of the sliding surface are determined in such a way that the effect of mismatched uncertainties or disturbances is removed in closed loop system and invariant property is retained. The proposed approach is simple in concept and realization. Moreover, due to the inaccessible system states, a new nonlinear observer is proposed for system model identification. In simulation, the electro-mechanical model of motor has been used which has both matched and mismatched uncertainties. Simulation results show the effectiveness of this approach