Proposed Control Structure Research Articles

Improvement of Power Quality in Grid Connected Wind Energy System

Abstract: This research covers industrial plant-connected wind turbine system control. An algorithm permits a control structure that uses a four-leg inverter linked to the grid to inject energy and operate as an active power filter, reducing load current disturbances and improving power quality. Algorithms enable this control structure. A four-wire system considers linear and nonlinear three-phase and single-phase loads. The utility-side controller supplies active and reactive power and adjusts for reactive, nonlinear, and unbalanced single- and intra-phase demands during wind turbine connection. Besides providing power, this is done. If there is no wind power, the controller uses the DC-link capacitor and grid-connected power converter to improve power quality. The proposed control structure is based on conservative power theory decompositions, which distinguishes it from previous techniques. This option separates the inverter control power and current references, giving a broad variety of adaptable, selective, and powerful functions. Real-time software benchmarking has tested the recommended control technique for comprehensive real-time implementation. The real-time simulator "FACTS" and the DSP microcontroller test and implement the control strategy in hardware-in-the-loop. These results confirmed our power quality improvement control, allowing us to omit passive filters. This created an electronic smart grid-based control system that was more efficient, adaptive, and reliable.

Optimization of wind energy extraction for variable speed wind turbines using fuzzy backstepping sliding mode control based on multi objective PSO

This study addresses the problem of designing a nonlinear feedback control strategy for horizontal axis variable speed wind turbines in the below-rated wind speed operating region (whether the wind turbine is offshore or onshore). The objective is to operate the wind turbines to maximum wind energy extraction while reducing the mechanical load. To overcome both issues, a robust nonlinear control strategy based on sliding mode control (SMC) is proposed which tries to seek for an improved performance. The proposed controller has been developed to compensate for negative consequences of external disturbances, measurement noises and unmodeled dynamics. This control strategy exploits a tracking controller that keeps an optimal value for the ratio between the rotor angular speed and the wind speed. For maximum wind energy extraction, the control algorithm employs the sliding mode state output feedback torque controller to provide robustness, combined with backstepping scheme to ensure better performance in the presence of matched and mismatched uncertainties. Next, a fuzzy logic system (FLS) approach is introduced to the backstepping sliding mode controller (BSMC) to achieve better mechanical loads prevention suffered by the transmission shaft by adjusting the parameters of the controller. Finally, multi-objective particle swarm optimization (MOPSO) algorithm is used to find the optimal values for the adjustable parameters of the proposed fuzzy BSMC (FBSMC). To validate the proposed control structure, FAST aeroelastic simulator is used. The obtained results illustrate that the presented control scheme has satisfactory performance.

Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent

Starting from the fact that in sensorless control systems of the Permanent Magnet Synchronous Motor (PMSM), the load torque can have short and significant variations, this paper presents the sensorless control of a PMSM based on a Linear Adaptive Disturbance Rejection Controller (LADRC) type controller. Essentially, the successful operation of the LADRC controller to achieve PMSM rotor speed control performance depends on a good estimation of the disturbances acting on the system. Traditionally, an Extended State Observer (ESO) is used to make such an estimate. In this paper, it is proposed to use a Disturbance Observer (DO) to estimate the external disturbances, and after their rejection, the LADRC controller ensures an equivalent global behavior of the control system with an ideal double integrator, thus increasing ease in achieving the desired control performance. Control structures and Matlab/Simulink implementation of the PMSM sensorless control system based on the LADRC controller with an ESO-/DO-type observer are presented, as is its use in tandem with a Reinforcement Learning Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) specially trained agent that provides correction signals for more accurate estimation of external disturbances and hence improved control performance. To optimize the gain value of the DO-type observer, a computational intelligence algorithm such as the Ant Colony Algorithm (ACO) is used. Qualitatively superior performance is achieved by using LADRC with the RL-TD3 agent control structure in terms of parametric robustness, response time, and steady-state error. In addition, by calculating the fractal dimension (DF) of the controlled signal and the PMSM rotor speed, it is found that the higher the DF, the better the performance of the control system. The validation of the superiority of the proposed control structures is carried out by means of numerical simulations in the Matlab/Simulink environment.

A Novel Electric Vehicle Battery Management System Using an Artificial Neural Network-Based Adaptive Droop Control Theory

The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized control architecture, communication-free capability, and improved reliability. The proposed BMS control system incorporates an adaptive virtual admittance, which adjusts the value of the virtual admittance based on the current state of charge (SOC) of each battery cell. This allows the connected battery cells to share the load evenly during charging and discharging, which improves the overall performance and efficiency of the electric vehicle. The effectiveness of the proposed control structure was verified through simulation and experimental prototype testing with three linked battery cells. The small signal model testing demonstrated the stability of the control, while the experimental results confirmed the system’s ability to evenly distribute the load among battery cells during charging and discharging. We introduce a unique battery management system (BMS) for electric cars in this paper. Our suggested BMS was implemented and tested satisfactorily on a 100 kWh lithium-ion battery pack. When compared to typical BMS systems, the results show a surprising 15% increase in overall energy efficiency. Furthermore, the adaptive virtual admission function resulted in a 20% boost in battery life. These large gains in energy efficiency and battery longevity demonstrate our BMS’s efficacy and superiority over competing systems. Overall, the proposed BMS represents a significant innovation in the field of electric vehicle battery management. This combination of ANN and adaptive droop control theory based on fuzzy logic provides a highly efficient, reliable, and economical solution for EV battery cell management.

An environmentally benign, energy intensified single-column side stream extractive distillation (SC-SSED) process for Acetone/n-heptane separation

The single-column, side-stream extractive distillation (SSED) process has been proposed as a simple, novel, and energy-saving configuration for separating azeotropic mixtures, using an intermediate-boiling entrainer. However, within limited progress in separating the mixture of acetone (ACE) and n-heptane (HEP) through such processes, benzene (BEN), a human carcinogen, was frequently used as an entrainer. In view of this, our study aims to propose an environmentally benign SSED process to separate the same mixture. In the proposed process, 1-chlorobutane (CHL) is used as an entrainer. With thorough analysis, the economic performance of this process was found to outperform the other process using heavy entrainers (i.e. p-xylene and butyl propionate), while being economically comparable to the process using BEN as an entrainer. In addition, limited improvement is achieved by employing the SSED configuration for heavy entrainer-based processes, since a higher-grade steam is required at the reboiler of the extractive distillation column. Subsequently, a suitable control strategy with two temperature loops and three measurements (i.e. a side-stream temperature control loop, and a temperature difference control loop in the extractive section of the SSED column) was established. It can satisfactorily reject the specified feed disturbances (i.e. ±20% change in flowrate and composition). Compared to all other existing studies, the currently proposed control structure does not require any composition loop and, at the same time, is able to handle disturbances to a greater extent.

An active disturbance rejection control design for the distillation process with input saturation via quantitative feedback theory

The distillation process is a major contributor to high energy consumption in the petrochemical industry. It is a complex multivariable system with input saturation, and its control problem is a critical factor that affects process energy efficiency. To address these control challenges, a new active disturbance rejection control (ADRC) design is proposed for the distillation process. The design includes a saturation compensation element with an integral structure and a nonlinear error feedback component with switched gain. The proposed control structure is effective in helping the system exit saturation in time, resulting in better dynamic performance than traditional ADRC. Additionally, a parameter tuning strategy based on quantitative feedback theory (QFT) is proposed to achieve the desired performance. The closed-loop stability conditions are analyzed using the describing functions technique to ensure the robust stability of the system. Finally, the proposed control structure and corresponding tuning strategy are applied to the distillation process. A numerical simulation and a simulation based on Aspen Plus confirm its superiority over traditional control structures. The proposed ADRC design with input saturation compensation has clear potential in the application of the distillation process.

A Modular Approach for Diesel Engine Air Path Control Based on Nonlinear MPC

This article presents a systematic approach to realize highly dynamic control strategies for the air path of diesel engines. It is based on grey-box models of individual air path components, designed to be applied in nonlinear model predictive control (NMPC). Specifically, they are suited for algorithmic differentiation and gradient-based optimization. This modular approach allows to derive models for a variety of complex air path systems and can be identified with a low amount of measurement data. An NMPC structure, which is based on these models, enables the tracking of arbitrary air path reference signals and allows the introduction of further control objectives for overactuated systems. We demonstrate our approach’s general applicability and effectiveness on two different laboratory engines using rapid control prototyping hardware. For a turbocharged light-duty diesel engine with dual-loop exhaust gas recirculation (EGR), we apply the proposed control structure to track intake manifold gas conditions while simultaneously minimizing engine pumping losses. Experimental results show an excellent tracking performance and reduced pumping losses compared to other control strategies. For a turbocharged heavy-duty engine with high-pressure EGR, we experimentally demonstrate a superior tracking performance to that obtained with a reference controller. Due to the modular and systematic approach, the algorithm design is straightforward, and the experimental calibration effort is low.

An adaptive control strategy for grid-forming of SCIG-based wind energy conversion systems

One of the crucial issues in the expansion of isolated wind energy conversion systems (WECSs) is the design of a grid forming control structure that is robust to changes of the system’s operating point in the steady-state as well as transient conditions. This paper has addressed the voltage/frequency control of a local microgrid as well as flux and slip control of a squirrel cage induction generator that feeds the microgrid via a back-to-back converter in an isolated WECS. An adaptive nonlinear voltage control structure has been proposed for load side converter (LSC) consists of a constant frequency voltage regulator and a current controller based on the Lyapunov function method (LFM), which are connected in cascade. In an outer loop, the reference current of LSC is generated by the voltage regulator designated for maintaining the voltage/frequency of the microgrid. In an inner loop, the reference signals are robustly tracked by the current controller. In a novel reference frame that its angle difference with the stationary reference frame is determined by a dual loop DC link voltage controller, an LFM-based nonlinear flux controller is proposed for the machine side converter (MSC). Using the proposed flux control structure, in addition to maintaining the flux magnitude at a nominal value, by controlling the generator slip through adjusting the speed of the proposed reference frame, the power balance is established and the DC voltage is maintained at a pre-set value. The performance of the proposed controllers has been investigated through simulation in the MATLAB® software. The obtained results confirm the robustness and stability of the proposed control structure with respect to various disturbances and uncertainties.

Undiscounted reinforcement learning for infinite-time optimal output tracking and disturbance rejection of discrete-time LTI systems with unknown dynamics

This paper proposes a novel control structure to solve the infinite-time linear quadratic tracking (LQT) problem. The major challenge in the LQT problem is the boundedness issue of the cost function in an infinite time framework. In many studies, a discount factor is utilised to overcome the challenge. However, it can affect the stability of the closed-loop system and the steady-state error. This paper proposes an optimal control structure that guarantees zero steady-state error with bounded cost function without utilising the discount factor. The optimal gains of the proposed control structure are computed via model-based and model-free reinforcement learning (RL) algorithms. As a novelty in model-based RL algorithms, a model predictive RL algorithm is proposed to reduce the number of iterations in the learning phase. A model-free reinforcement learning algorithm is utilised to obtain optimal control for tracking the reference online and without any knowledge of system dynamics. Finally, the simulation results verify the advantages of the proposed optimal control structure.

Optimal sliding mode controller for lower limb rehabilitation exoskeleton in constrained environments

<p>In this article, a lower limb exoskeleton (LLE) under contacting constrained motion has been modelled using augmented Lagrange equations which include Lagrange multiplier and Jacobian vectors. A sliding mode Controller optimized by the grey Wolf optimization algorithm has been used for controlling (LLE) in the case of constrained motion with uncertainties and outside perturbation. The grey wolf optimization algorithm has been used as an optimization algorithm for finding the optimal controllers’ parameters in order to improve the performance of the system. The stability analysis of the closed-loop system has been performed using Lyapunov theory of stability. To validate the effectiveness of the proposed controller structure grey wolf optimization algorithm controller (GW-SMC), a series of comparative simulations have been carried out with other types of recently existing sliding mode control (SMC). The results of numerical simulations indicate the superiority of the sliding mode optimized by the GW-SMC over other types of recently existing controller in terms of tracking errors and robustness towards uncertainties and external disturbances.</p>

Utilizing controlled plug-in electric vehicles to improve hybrid power grid frequency regulation considering high renewable energy penetration

This study proposes an incorporated strategy based on energy storage systems (ESSs) like plug-in electric vehicles (PEVs) with load frequency control (LFC) to enhance the frequency stability during high penetration of renewable energy sources (RESs). Furthermore, an innovative-cascaded controller is used in the LFC loop, and PEVs are considered in the two identical areas' multi-source power grids. The proposed control structure is based on using two terms, the first term is the fractional-order-proportional-derivative (FOPD), and the second cascaded term is a fractional-order-proportional-integral (FOPI) to form the cascaded (FO-(PD-PI)) controller. Additionally, the gains of the proposed controller are well designed, utilizing an improved version of the manta ray foraging optimization algorithm (MRFO) labeled as leader MRFO (LMRFO). The effectiveness of the proposed FO-(PD-PI) controller based on the LMFRO algorithm is validated by comparing its performance with that of other control approaches such as (i.e., the proportional-integral-derivative (PID) controller, the tilt-integral-derivative (TID) controller, and the fractional-order-PID (FOPID) controller) based on different optimizers. Different load perturbation patterns, a high penetration level of RESs, and the communication delay time are broadly implemented for ensuring the superiority of the proposed controller in penalizing the power grid perturbations. Furthermore, for validating the efficiency of the proposed controller, an IEEE 39 bus is used. As a result, the proposed integrated strategy is a viable and effective control scheme for modern power grids with high-RESs penetration.

Load frequency regulation of multi-area power systems with communication delay via cascaded improved ADRC

Load frequency regulation (LFR) is regarded as a significant method for the stable operation of modern power grids. However, the LFR is facing many challenges such as communication delays in the area control error, and external disturbances caused by renewable energy integrating into power grids. This paper presents a cascaded improved active disturbance rejection control (ADRC) to handle these control challenges, where the inner and outer controllers are the improved ADRC and regular ADRC, respectively. The advantages of the improved ADRC to reject external disturbances and system uncurtains are discussed by simulations. Then a practical parameter tuning method is provided to guide the selection of controller parameters. Finally, comparative simulations are implemented to illustrate the abilities of the proposed control scheme in different cases. The proposed control structure can handle the communication delay well and have a strong ability to reject the disturbances of uncertain consumed power and wind power. The simulation results imply that the cascaded improved ADRC may have broad application prospects for the LFR.

Extended-State-Observer-Based Angular Acceleration Estimation for Supersonic Aircraft Lateral–Directional Control

Large-slenderness-ratio (LSR) aircraft exhibit more severe lateral–directional coupling compared with other aircraft, which poses a significant challenge to their flight safety, especially during high-speed maneuvers. Reliable attitude decoupling control is, therefore, essential for LSR aircraft. In this study, a novel control framework that combines incremental nonlinear dynamic inversion (INDI) and extended state observer (ESO) is proposed for supersonic roll maneuver control of LSR aircraft. The ESO is used to estimate the angular acceleration on the basis of an onboard mathematical model. The acceleration estimator based on ESO achieves superior noise reduction compared with the complementary filter (CF) and reduces the onboard model requirement without significantly sacrificing estimation accuracy. Monte Carlo (MC) simulations and frequency–domain analysis demonstrate the effectiveness and robustness of the proposed controller. The sensitivity of parameter uncertainties is also investigated, revealing that the natural frequency of the actuator is the most critical parameter affecting robustness. Finally, flight tests validate the effectiveness of the proposed control structure.

Energy Management of P2 Hybrid Electric Vehicle Based on Event-Triggered Nonlinear Model Predictive Control and Deep Q Network

Hybrid electric vehicles (HEVs) are used as a bridge during the transition to battery electric vehicles (BEVs) and to make energy consumption more efficient. The main problem in improving the efficiency of HEV energy consumption is torque management. In this study, a novel approach based on a nonlinear model predictive controller to solve the reference tracking and torque distribution problem is proposed. That is to say, in order to increase the efficiency of torque distribution, the weights of nonlinear model predictive control (NMPC) are trained with a Deep Q Network (DQN), and an event-triggered mechanism is designed with DQN to reduce the computational cost of MPC. The considered torque distribution problem varies according to the type and structure of the HEV. In this study, a parallel type 2 hybrid electric vehicle (P2 HEV) is considered and modeled via publicly shared passenger vehicle data of the engine, motor, high-voltage battery, transmission, clutch, differential, and wheel characteristics. NMPC is formulated so that the torque values remain within the physical limits of the engine, and the battery also operates at its physical limits. Namely, it is guaranteed that the battery works according to a certain state of charge (SOC) window and current limits. The state of health (SOH) of the battery is also considered in the optimization. The motor and engine efficiencies increase by 3.61% and 2.86%, respectively, with the proposed control structure, while the computational cost is reduced by 52.01% when utilizing the proposed event-triggering mechanism in the NMPC controller.

Feedback-Optimizing Heuristic Model Predictive Control Applied to a Wind Turbine for Suppressing the Tower Vibrations

To have accurate control of a wind turbine, especially when facing wind loads, it is vital to model the dynamic behavior of the system accurately and select the controller parameters properly. To mitigate these challenges, here, an optimal model-based controller is utilized using particle swarm optimization to suppress the vibrations of the tower for an NREL 5 MW turbine in the presence of the turbulence wind. For this purpose, first, a discrete state-space model is derived for the wind turbine based on the finite difference method, and the validity of the model structure is verified with its analytical solution. Then, the structure of a model predictive controller is implemented on the model of the wind turbine with optimization of the plant behavior, extracted from state-space form, in a prediction horizon. Next, to prevent the dependence of the proposed controller structure on its parameters, a number of training scenarios including the turbulent wind with different intensities are run in parallel. The cost of each scenario is continually updated based on the tracking error, the control effort and the control command generation time in total run time. Consequently, the best parameters of the controller are adjusted using a stochastic robust optimization algorithm such as particle swarm optimization. Results demonstrate that the proposed approach has an excellent performance in dissipating the tower vibration.

Stabilization of a chaotic oscillator via a class of integral controllers under input saturation

This work presents the straightforward design of an integral controller with an anti-windup structure to prevent undesirable behavior when actuator saturation is considered, and the proposed controller improves the performance of the closed-loop dynamics of a class of nonlinear oscillators. The proposed integral controller has an adaptive control gain, which includes the absolute value of the named control error to turn off the integral action when it is saturated. Closed-loop stability analysis is performed under the Lyapunov theory framework, where it can be concluded that the system behaves in an asymptotically stable way. The proposed methodology is successfully applied to a Rikitake-type oscillator, considering a single input-single output (SISO) structure for regulation and tracking trajectory purposes. For comparison, an equivalent fixed gain integral controller is also implemented to analyze the corresponding anti-windup properties of the proposed control structure. Numerical experiments are conducted, showing the superior performance of the proposed controller.

Design of model-based control strategies for a novel MISO PEM fuel cell control structure.

Voltage control is critical for the performance of proton exchange membrane fuel cells. However, accurately controlling voltage is challenging, specifically during the current variation. The present study proposes a novel multiple-input single-output (MISO) control structure for a proton exchange membrane fuel cell system to improve performance. Also, this study focuses on airflow optimization and hydrogen consumption optimization, as the literature focuses more on airflow optimization for compressor or pump performance. Firstly, using a genetic algorithm optimization technique, the fractional order model is realized from the existing integer order fuel cell model. Then, the proposed control structure aims to control the output cell voltage by regulating the air and hydrogen inlet rates by designing various model-based controllers for integer and fractional order models. The control performance is evaluated for set point tracking, disturbance rejection, inverse response rejection and time delay compensation. The simulation results show that the fractional order system is observed to give better results than the integer order system, with model predictive controller showing the best results for controlling the stack voltage.

Decoupled Control Design of Aerial Manipulation Systems for Vegetation Sampling Application

A key challenge in the use of drones for an aerial manipulation task such as cutting tree branches is the control problem, especially in the presence of an unpredictable and nonlinear environment. While prior work focused on simplifying the problem by modeling a simple interaction with branches and controlling the system with nonlinear and non-robust control schemes, the current work deals with the problem by designing novel robust nonlinear controllers for aerial manipulation systems that are appropriate for vegetation sampling. In this regard, two different potential control schemes are proposed: nonlinear disturbance observer-based control (NDOBC) and adaptive sliding mode control (ASMC). Each considers the external disturbances and unknown parameters in controller design. The proposed control scheme in both methods employs a decoupled architecture that treats the unmanned aerial vehicle and the manipulator arm of the sampler payload as separate units. In the proposed control structures, controllers are designed after comprehensively investigating the dynamics of both the aerial vehicle and the robotic arm. Each system is then controlled independently in the presence of external disturbances, unknown parameter changes, and the nonlinear coupling between the aerial vehicle and robotic arm. In addition, fully actuated and underactuated aerial platforms are examined, and their stability and controllability are compared so as to choose the most practical framework. Finally, the simulation findings verify and compare the performance and effectiveness of the proposed control strategies for a custom aerial manipulation system that has been designed and developed for field trials.

Design and development of solar photovoltaic fed modular multilevel inverter using intelligent techniques for renewable energy applications

This paper presents the design and development of Modular Multilevel Inverter (MMI) to reduce Total harmonic distortion (THD) using intelligent techniques towards marine applications. Many researchers have described the additional advantage of inverter control challenges such as voltage imbalance, increasing the number of voltage levels, power quality issues, reducing the number of semiconductors switches and achieving higher efficiency. Under the intelligent techniques, the implementation is carried out with aid of Artificial Neural Networks (ANN), Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to calculate the modulation index (ma) and switching angles (θ) for MMI. Based on the calculation, it is trained to form a mapping between inputs and outputs for obtaining reduced Total Harmonics Distortion (THD). The objective of the intelligent controller is to control the inverter for regulating the output voltage with lowest THD. The proposed control structure has been estimated and compared for better robustness in terms of switching angle and modulation index with least THD in the inverter. Simulations and prototype models are made to analyze the controller’s performance, for inverter output voltage and harmonics. This proposed system is designed for marine lighting load application. The FPGA performance with all intelligent methods are analyzed by in SPARTAN3E500 FPGA device.

A grid forming control for wind energy conversion systems

This paper presents an effective and practical structure to improve voltage and frequency regulation in grid-forming control of self-excited induction generator-based wind energy conversion systems. In the proposed control structure, in addition to a fixed parallel excitation capacitor, a small wound rotor synchronous generator is also used as a reactive power compensator. The excitation voltage of the small synchronous generator is controlled in such a way that by injecting the required amount of reactive power, this generator maintains the balance between the inductive and the capacitive reactive powers in the isolated system, and thus, improves the system voltage regulation. In addition, an active power control is proposed in which the speed of the prime mover of the self-excited squirrel cage induction generator is adjusted in such a way that the active power of the small synchronous generator is maintained at zero value. The proposed power controller, by creating the balance between the generated and the consumed active powers, improves the frequency regulation of the wind energy conversion system. The validity and effectiveness of the proposed structure are demonstrated through both simulation studies and experimental implementation. The obtained results confirm that the poor regulation of voltage and frequency, which is one of the major drawbacks of self-excited induction generator-based wind energy conversion systems, has been significantly mitigated by using the proposed method.