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This article presents a new control strategy for a DC-DC boost converter used to supply a constant power load. The designed controller allows regulates the converter output voltage to a required reference value. By designing the controller based on the reduced order averaged model of the converter and using a nonlinear control technique based on feedback linearization, a simple-to-implement and stable controller for both operating modes of the converter (continuous and discontinuous conduction modes respectively) is obtained. The designed control strategy is validated through simulation and experimental results, demonstrating good dynamic performance and stability in both conduction modes, under variations in reference values and significant load changes. In addition, a comparison of the performance and implementation costs between the control strategy proposed in this paper and two strategies proposed by other authors is included.

The dynamic behavior of the hydraulic actuator in a system for regulating the electrode's position is crucial for the operation of a Ladle Furnace. This work aims to identify, model, and control the hydraulic actuator in the Ladle Furnace of ACINOX Las Tunas. For identifying the system, input signals of Pseudo-Random Binary type and black box models were used. As a result, three models were obtained, two reflecting the process's asymmetric behavior according to the upward or downward movement. The third model approximates the process dynamic behavior around the operating point and includes the uncertainty caused by the weight variation during the electrode wear. The models obtained, with a fit greater than 85%, allow a better understanding of the study case behavior. In addition, these allowed the evaluation of the electrode's weight variation and tuning of several controllers. The optimal one was a novel non-linear PI controller of guaranteed robustness. In future works, the use of a non-linear function could be evaluated to compensate for the asymmetric behavior of the process.

Power electronics is seeing an increase in the use of sophisticated self-learning controllers as single board computers and microcontrollers progress faster. Traditional controllers, such as PI controllers, suffer from transient instability difficulties. The duty cycle and output voltage of a DC/DC converter are not linear. Due to this non-linearity, the PI controller generates variable levels of voltage fluctuations depending on the operating region of the converter. In some cases, non-linear controllers outperform PI controllers. The non-linear model of a non-linear controller is determined by data availability. So, a self-calibrating controller that collects data and optimizes itself as the operation goes on is necessary. Iteration and oscillation can be minimized with a well-trained reinforcement learning model utilizing a non-linear policy. A boost converter's output power supply capacity changes with a change in load, due to which the maximum duty cycle limit of a converter also changes. A support vector calibrated by reinforcement learning can dynamically change the duty cycle limit of a converter under variable load. This research highlights how reinforcement learning-based non-linear controllers can improve control and efficiency over standard controllers. The proposed concept is based on a microgrid system. Simulation and experimental analysis have been conducted on how reinforcement learning-based controller works for DC-DC boost converter.

Transfemoral prostheses have evolved from mechanical devices to microprocessor-based, electronically controlled knee joints, allowing amputees to regain control of their limbs. For improved amputee experience at varying ambulation rates, these devices provide controlled damping throughout the swing and stance phases of the gait cycle. Commercially available microprocessor-based prosthetic knee (MPK) joints use linear controllers, heuristic-based methods, and finite state machine based algorithms to track the refence gait cycle. However, since the amputee experiences a variety of non-linearities during ambulation, such as uneven terrains, walking backwards and climbing stairs, therefore, traditional controllers produces error, abnormal movements, unstable control system and require manual-tuning. As a result, novel controllers capable of replicating and tracking reference gait cycles for a range of reference signals are needed to reduce the burden on amputees and improve the rehabilitation process. Therefore, the current study proposes two non-linear control techniques, the Lyapunov-redesign controller and the sliding mode controller for real-time tracking of various signals, such as walking on level ground at a normal speed and ambulation on uneven terrains. State-space model of MPK was developed along with the mathematical modelling of non-linear controllers. Simulations and results are presented using MATLAB to verify the ability of proposed non-linear controllers for constantly and dynamically tracking and maintaining desired motion dynamics. Furthermore, for selected reference signals, a linear controller was applied to the same mathematical model of MPK. During tracking of reference angel in case of general gait cycle, an accuracy of 99.95% and 99.96% was achieved for sliding mode controller and Lyapunov-redesign controller respectively. Whereas, for the same case, linear controller had an accuracy of 95.5% only. Therefore, it can be concluded that the performance of non-linear controllers was better than their linear counterparts while tracking various reference signals for microprocessor based prosthetic knee. [ABSTRACT FROM AUTHOR]

Conventional electricity generation methods are under the major revolution, and microgrids established on renewable energy sources are playing a vital role in this power generation transformation. This study proposes a hybrid AC/DC microgrid with a barrier function-based adaptive sliding mode controller, in which 8 kW wind energy system and 4.5 kW photovoltaic energy system perform as the hybrid RESs, and 33 Ah of battery works as the energy storage system. Barrier function-based adaptive sliding mode controller ensures the convergence of the system's output variable independent of the knowledge of the upper bound of the disturbances. Firstly, global mathematical modeling of the suggested system is ensured. Then, the control laws are defined, providing the DC bus voltage regulation during islanding mode and AC/DC link bus voltage regulation during the grid-connected mode. The proposed barrier function-based adaptive sliding mode controller technique is analyzed through 20 s simulations on MATLAB/Simulink, which validates the controller's robustness and effectiveness. Furthermore, a comparison of the proposed controller is made with the proportional integral derivative controller, Lyapunov controller, and sliding mode controller. In the end, hardware-in-loop tests are performed using C2000 Delfino MCU F28379D LaunchPad, showing the proposed structure's real-time performance. [ABSTRACT FROM AUTHOR]

Conventional electricity generation methods are under the major revolution, and microgrids established on renewable energy sources are playing a vital role in this power generation transformation. This study proposes a hybrid AC/DC microgrid with a barrier function-based adaptive sliding mode controller, in which 8 kW wind energy system and 4.5 kW photovoltaic energy system perform as the hybrid RESs, and 33 Ah of battery works as the energy storage system. Barrier function-based adaptive sliding mode controller ensures the convergence of the system’s output variable independent of the knowledge of the upper bound of the disturbances. Firstly, global mathematical modeling of the suggested system is ensured. Then, the control laws are defined, providing the DC bus voltage regulation during islanding mode and AC/DC link bus voltage regulation during the grid-connected mode. The proposed barrier function-based adaptive sliding mode controller technique is analyzed through 20 s simulations on MATLAB/Simulink, which validates the controller’s robustness and effectiveness. Furthermore, a comparison of the proposed controller is made with the proportional integral derivative controller, Lyapunov controller, and sliding mode controller. In the end, hardware-in-loop tests are performed using C2000 Delfino MCU F28379D LaunchPad, showing the proposed structure’s real-time performance.

Mathematical models and algorithms for maximizing power extraction have become an essential topic in renewable energies in the last years, especially in wind energy conversion systems. This study proposes maximum power point tracking using gain scheduling approximations for an emulated wind system in a direct-drive connection. Power extraction is obtained by controlling the duty cycle of a Multilevel Boost Converter, which directly varies the rotational speed of a permanent magnet synchronous generator directly coupled to a three-phase induction motor that emulates the wind turbine. The system’s complexity is linked to the inherent non-linearities associated with the diverse electrical, mechanical, and power electronic elements. In order to present a synthesized model without losing the system dynamic richness, several physical tests were made to obtain parameters for building several mathematical approaches, resulting in non-linear dynamic equations for the controller gains, which are dependant on wind speed. Thirty real operational wind speeds considering typical variations were used in several tests to demonstrate the mathematical models’ performance. Results among these gain scheduling approaches and a typical controller constant gains mathematical model were compared based on standard deviations, absolute error, and the time for reaching the optimum generator angular speed related to every wind speed.

Controller design for non-linear multi-input, multi-output systems, such as unmanned quadrotor vehicles, has always been a challenging issue due to the strong interconnection between state variables and highly nonlinear dynamic equations. In addition, quadrotor is an under-actuated non-linear dynamic device. Due to being under-actuated for moving in the horizontal direction, the combination of changes in the speed of the existing quadruple operators should be used. So that, by creating the angle between the quadrotor hypothetical plane and the horizon surface, the device can be forced to move in the longitudinal or transverse direction. Therefore, in the quadrotor control system, two nested control loops are required. An outer loop to determine the appropriate angle of the device relative to the horizon for horizontal movements and an inner loop that is required to angle of the device panel is equal to this angle. In this paper, a fuzzy hybrid super-twisting sliding mode non-linear controller for controlling a sample quadrotor is designed. For this purpose, a fuzzy controller in the outer loop and a super twisting sliding mode controller in inner loop are used. An important advantage of this strategy is that it optimizes the horizontal speed of the device. If the distance from the target is too high, the angle of the device panel also increases, and if the distance is reduced, the angle also decreases. As a result, the device reaches the target with the desired speed. The performed simulation results confirmed this fact. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP). Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL) non-linear controller combined with an artificial neural network (ANN) is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT) and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

This paper proposes a new development of a position-tracking control algorithm for a non-linear magnetic ball levitation system by utilizing the back-stepping technique based on the cognitive online auto-tune algorithm. The aim of the proposed cognitive robust non-linear controller is to find the optimal voltage or current control action for the real magnetic ball levitation model during the tracking of pre-defined position of the steel ball precisely and quickly. The cognitive methodology guided by the Lyapunov stability criterion is implemented as a stable and robust online auto-tune algorithm to find optimal parameters for the proposed controller. Matlab simulation results and laboratory work confirm the validity and the robustness of the proposed controller algorithm in terms of overcoming the noise signal and disturbances, minimizing the tracking error, and obtaining an optimal and smooth control signal, with the minimum number of fitness evaluations. [ABSTRACT FROM AUTHOR]

The dynamic behavior of the hydraulic actuator in a system for regulating the electrode's position is crucial for the operation of a Ladle Furnace. This work aims to identify, model, and control the hydraulic actuator in the Ladle Furnace of ACINOX Las Tunas. For identifying the system, input signals of Pseudo-Random Binary type and black box models were used. As a result, three models were obtained, two reflecting the process's asymmetric behavior according to the upward or downward movement. The third model approximates the process dynamic behavior around the operating point and includes the uncertainty caused by the weight variation during the electrode wear. The models obtained, with a fit greater than 85%, allow a better understanding of the study case behavior. In addition, these allowed the evaluation of the electrode's weight variation and tuning of several controllers. The optimal one was a novel non-linear PI controller of guaranteed robustness. In future works, the use of a non-linear function could be evaluated to compensate for the asymmetric behavior of the process., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Power electronics is seeing an increase in the use of sophisticated self-learning controllers as single board computers and microcontrollers progress faster. Traditional controllers, such as PI controllers, suffer from transient instability difficulties. The duty cycle and output voltage of a DC/DC converter are not linear. Due to this non-linearity, the PI controller generates variable levels of voltage fluctuations depending on the operating region of the converter. In some cases, non-linear controllers outperform PI controllers. The non-linear model of a non-linear controller is determined by data availability. So, a self-calibrating controller that collects data and optimizes itself as the operation goes on is necessary. Iteration and oscillation can be minimized with a well-trained reinforcement learning model utilizing a non-linear policy. A boost converter's output power supply capacity changes with a change in load, due to which the maximum duty cycle limit of a converter also changes. A support vector calibrated by reinforcement learning can dynamically change the duty cycle limit of a converter under variable load. This research highlights how reinforcement learning-based non-linear controllers can improve control and efficiency over standard controllers. The proposed concept is based on a microgrid system. Simulation and experimental analysis have been conducted on how reinforcement learning-based controller works for DC-DC boost converter., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

This paper presents a design and implementation of digital non-linear controller-based, three-phase shunt-active filter for power line conditioner system. The active power filter is implemented with PWM-Voltage Source Inverter and its switching pulses are generated from Space Vector Modulation techniques. The active filter is applied for power quality improvements by compensating harmonics in the distribution network due to non-linear loads. The harmonic components are extracted from the sensed load currents from proposed non-linear control strategy. A simple and efficient Phase Locked Loop is used to synchronize the angle of vector orientation, which cancels the effect of harmonics and notches in the distribution network. An inner and outer loop PI-control is incorporated with non-linear method for harmonic current control and dc-bus voltage regulator. Additionally, pre-filter is inserted at the input of each current loop in order to compensate for the effect of the left hand zero in the closed loop transfer functions. The entire control method is validated through MATLAB and also coded in assembly language for digital hardware implementation. The digital control algorithm is evaluated through TMS320F240 Digital Signal Processor for 10 kVAR active filter system. [ABSTRACT FROM AUTHOR]

A sliding-mode input–output linearization controller for the dc/dc zero-voltage switching (ZVS) CLL-T resonant converter is presented. The proposed controller significantly improves the transient response and disturbance rejection of the converter while preserving the closed-loop stability. An averaged large-signal dynamic model of the converter operating with a novel discrete ZVS modulation technique is developed and used as starting point for the controller design. Since the model derivation process does not include small signal approximations, it can be used for large-signal analysis, providing accurate predictions of the converter dynamic behavior. The combination of the proposed controller and modulation technique provides ZVS operation over a wide load range, narrowed regulating frequency range, robustness, and fast transient response against transient load changes. Experimental and simulation results are reported to validate the theoretical predictions and to confirm the superior performance of the nonlinear controller when it is compared with a conventional linear controller. [ABSTRACT FROM PUBLISHER]

The switch-mode power supplies have been controlled using different control algorithms like PID control, current mode control etc. Since, Switch-mode power supplies represent a particular class of variable structure systems (VSS). Thus, they can take advantage of non-linear control techniques developed for the variable structure systems. In this paper the sliding mode control is analyzed and developed with preference to buck converter. The simulation model of buck converter with its control circuit was build up in MATLAB/Simulink™ and simulation results are obtained. Then the dynamic response of buck converter controlled by SMC is studied. Sliding mode control extends the properties of hysteresis control to multi-variable environments, resulting in stability even for large supply and load variations. It presents a good dynamic response and simple implementation. The performance of SM control is compared with that of conventional PID control. It has been shown that the use of SM control can lead to an improved robustness in providing consistent transient responses over a wide range of operating conditions. [ABSTRACT FROM AUTHOR]

Mathematical models and algorithms for maximizing power extraction have become an essential topic in renewable energies in the last years, especially in wind energy conversion systems. This study proposes maximum power point tracking using gain scheduling approximations for an emulated wind system in a direct-drive connection. Power extraction is obtained by controlling the duty cycle of a Multilevel Boost Converter, which directly varies the rotational speed of a permanent magnet synchronous generator directly coupled to a three-phase induction motor that emulates the wind turbine. The system's complexity is linked to the inherent non-linearities associated with the diverse electrical, mechanical, and power electronic elements. In order to present a synthesized model without losing the system dynamic richness, several physical tests were made to obtain parameters for building several mathematical approaches, resulting in non-linear dynamic equations for the controller gains, which are dependant on wind speed. Thirty real operational wind speeds considering typical variations were used in several tests to demonstrate the mathematical models' performance. Results among these gain scheduling approaches and a typical controller constant gains mathematical model were compared based on standard deviations, absolute error, and the time for reaching the optimum generator angular speed related to every wind speed. [ABSTRACT FROM AUTHOR]