In this paper, two new versions of modified active disturbance rejection control (MADRC) are proposed to stabilize a nonlinear quadruple tank system and control the water levels of the lower two tanks in the presence of exogenous disturbances, parameter uncertainties, and parallel varying input set-points. The first proposed scheme is configured from the combination of a modified tracking differentiator (TD), modified super twisting sliding mode (STC-SM), and modified nonlinear extended state observer (NLESO), while the second proposed scheme is obtained by aggregating another modified TD, a modified nonlinear state error feedback (MNLSEF), and a fal-function-based ESO. The MADRC schemes with a nonlinear quadruple tank system are investigated by running simulations in the MATLAB/SIMULINK environment and several comparison experiments are conducted to validate the effectiveness of the proposed control schemes. Furthermore, the genetic algorithm (GA) is used as a tuning algorithm to parametrize the proposed MADRC schemes with the integral time absolute error (ITAE), integral square of the control signal (ISU), and integral absolute of the control signal (IAU) as an output performance index (OPI). Finally, the simulation results show the robustness of the proposed schemes with a noticeable reduction in the OPI.

In the photovoltaic system, the performance, efficiency, and generated power of the PV system are affected by changes in the environment, disturbances, and parameter variations, and this leads to a deviation from the operating maximum power point (MPP) of the PV system. Therefore, the main aim of this paper is to ensure the PV system operates at the maximum power point under the influence of exogenous disturbances and uncertainties, i.e., no matter how the irradiation, temperature, and load of the PV system change, by proposing a maximum power point tracking for the photovoltaic system (PV) based on the active disturbance rejection control (ADRC) paradigm. The proposed method provides better performance with excellent tracking for the MPP by controlling the duty cycle of the DC–DC buck converter. Moreover, comparison simulations have been performed between the proposed method and the linear ADRC (LADRC), conventional ADRC, and the improved ADRC (IADRC) to investigate the effectiveness of the proposed method. Finally, the simulation results validated the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with excellent response and minimum output performance index (OPI).

The main aim of this study was to address the problem of congestion in TCP nonlinear systems in the presence of mismatched exogenous disturbances. To achieve this problem, two methods are proposed: the first is active queuing management, based on two proposed controllers, an NLPID and STC‐SM, while the second is the application of active queuing management‐based anti‐disturbance techniques such as active disturbance rejection control (ADRC) and the nonlinear disturbance observer (NLDO). The proposed ADRC consists of a new NLPID and a new super‐twisting sliding mode controller (STC‐SM), which functions as a novel NLSEF, and a proposed NLESO estimates the applied disturbance and cancels it in a responsive manner. A new tracking differentiator with a novel function is also used to generate a smooth and accurate reference signal and derivative. The NLDO is proposed to estimate the disturbance and combine this with the control signal of the designed nonlinear controller as a way to compensate for the disturbance. The simulation results for the proposed scheme (ADRC) as applied to a nonlinear model of the TCP network are thus found to provide smoother and more accurate tracking of the desired value, with high robustness against applied disturbance, as compared to the other schemes introduced in this study. The proposed scheme also shows a noticeable improvement in terms of the utilized performance indices and the OPI.

Since the outbreak of the COVID-19 epidemic, several control strategies have been proposed. The rapid spread of COVID-19 globally, allied with the fact that COVID-19 is a serious threat to peoples health and life, motivated many researchers around the world to investigate new methods and techniques to control its spread and offer treatment. Currently, the most effective approach to containing SARS-CoV-2 (COVID-19) and minimizing its impact on education and the economy remains a vaccination control strategy, however. In this paper, a modified version of the susceptible, exposed, infectious, and recovered (SEIR) model using vaccination control with a novel construct of active disturbance rejection control (ADRC) is thus used to generate a proper vaccination control scheme by rejecting those disturbances that might possibly affect the system. For the COVID-19 system, which has a unit relative degree, a new structure for the ADRC has been introduced by embedding the tracking differentiator (TD) in the control unit to obtain an error signal and its derivative. Two further novel nonlinear controllers, the nonlinear PID and a super twisting sliding mode (STC-SM) were also used with the TD to develop a new version of the nonlinear state error feedback (NLSEF), while a new nonlinear extended state observer (NLESO) was introduced to estimate the system state and total disturbance. The final simulation results show that the proposed methods achieve excellent performance compared to conventional active disturbance rejection controls.

This paper deals with the disturbance rejection, parameter uncertainty cancelation, and the closed-loop stabilization of the water level of the four-tank nonlinear system. For the four-tank system with relative degree one, a new structure of the active disturbance rejection control (ADRC) has been presented by incorporating a tracking differentiator (TD) in the control unit to obtain the derivate of the tracking error. Thus, the nonlinear-PD control together with the TD serves as a new nonlinear state error feedback. Moreover, a sliding mode extended state observer is presented in the feedback loop to estimate the system's state and the total disturbance. The proposed scheme has been compared with several control schemes including linear and nonlinear versions of ADRC techniques. Finally, the simulation results show that the proposed scheme achieves excellent results in terms of disturbance elimination and output tracking as compared to other conventional schemes. It was able to control the water levels in the two lower tanks to their desired value and exhibits excellent performance in terms of Integral Time Absolute Error (ITAE) and Objective Performance Index (OPI).

The thermal conductivity of polyethylene glycol (PEG)-nanocomposites is crucial for their use in thermal management systems, underscoring the need for accurate predictive models. This study develops a Gradient Boosting Machine (GBM) model optimized with four algorithms: Batch Bayesian Optimization (BBO), Evolution Strategies (ES), Bayesian Probability Improvement (BPI), and Gaussian Processes Optimization (GPO). A dataset of 229 experimental samples (90% training, 10% testing) was employed, using temperature, PEG molecular weight, nano concentration, and nano type as input variables. K-fold cross-validation minimized overfitting. Model performance was evaluated with R-squared (R²), mean squared error (MSE), average absolute relative error (AARE%), and runtime. Correlation analysis showed nano concentration had the strongest impact on conductivity (0.75), followed by PEG molecular weight (0.56), while temperature (0.33) and nano type (0.24) were weaker predictors. Among the algorithms, GBM-ES achieved the best accuracy (R² = 0.9966 training, 0.9158 testing), outperforming the other methods and traditional linear regression, which showed far lower precision. GPO was the most computationally efficient (153.6 s), while BBO was slowest (274 s). Sensitivity analysis confirmed the dominant role of nano concentration and molecular weight. Overall, the optimized GBM models provide reliable, data-driven tools for predicting PEG-nanocomposite conductivity, reducing dependence on costly experimental work.