Application Of Controller Research Articles

Fuzzy Logic-Based Smart Control of Wind Energy Conversion System Using Cascaded Doubly Fed Induction Generator

This paper introduces a robust system designed to effectively manage and enhance the electrical output of a Wind Energy Conversion System (WECS) using a Cascaded Doubly Fed Induction Generator (CDFIG) connected to a power grid. The solution that was investigated is the use of a CDFIG that is based on a variable-speed wind power conversion chain. It comprises the electrical and mechanical connection of two DFIGs through their rotors. The originality of this paper lies in the innovative application of a fuzzy logic controller (FLC) in combination with a CDFIG for a WECS. To demonstrate that this novel configuration enhances control precision and performance in WECSs, we conducted a comparison of three different controllers: a proportional–integral (PI) controller, a fractional PID (FPID) controller, and a fuzzy logic controller (FLC). The results highlight the potential of the proposed system in optimizing power generation and improving overall system stability. It turns out that, according to the first results, the FLC performed optimally in terms of tracking and rejecting disturbances. In terms of peak overshoot for power and torque, the findings indicate that the proposed FLC-based technique (3.8639% and 6.9401%) outperforms that of the FOPID (11.2458% and 10.9654%) and PI controllers (11.4219% and 11.0712%), respectively. These results demonstrate the superior performance of the FLC in reducing overshoot, providing better control stability for both power and torque. In terms of rise time, the findings show that all controllers perform similarly for both power and torque. However, the FLC demonstrates superior performance with a rise time of 0.0016 s for both power and torque, compared to the FOPID (1.9999 s and 1.9999 s) and PI (0.0250 s and 0.0247 s) controllers. This highlights the FLC’s enhanced responsiveness in controlling power and torque. In terms of settling time, all three controllers have almost the same performance of 1.9999. An examination of total harmonic distortion (THD) was also employed to validate the superiority of the FLC. In terms of power quality, the findings prove that a WECS based on an FLC (0.93%) has a smaller total harmonic distortion (THD) compared to that of the FOPID (1.21%) and PI (1.51%) controllers. This system solves the problem by removing the requirement for sliding ring–brush contact. Through the utilization of the MATLAB/Simulink environment, the effectiveness of this control and energy management approach was evaluated, thereby demonstrating its capacity to fulfill the objectives that were set.

Application of an Optimal Fractional-Order Controller for a Standalone (Wind/Photovoltaic) Microgrid Utilizing Hybrid Storage (Battery/Ultracapacitor) System

Nowadays, standalone microgrids that make use of renewable energy sources have gained great interest. They provide a viable solution for rural electrification and decrease the burden on the utility grid. However, because standalone microgrids are nonlinear and time-varying, controlling and managing their energy can be difficult. A fractional-order proportional integral (FOPI) controller was proposed in this study to enhance a standalone microgrid’s energy management and performance. An ultra-capacitor (UC) and a battery, called a hybrid energy storage scheme, were employed as the microgrid’s energy storage system. The microgrid was primarily powered by solar and wind power. To achieve optimal performance, the FOPI’s parameters were ideally generated using the gorilla troop optimization (GTO) technique. The FOPI controller’s performance was contrasted with a conventional PI controller in terms of variations in load power, wind speed, and solar insolation. The microgrid was modeled and simulated using MATLAB/Simulink software R2023a 23.1. The results indicate that, in comparison to the traditional PI controller, the proposed FOPI controller significantly improved the microgrid’s transient performance. The load voltage and frequency were maintained constant against the least amount of disturbance despite variations in wind speed, photovoltaic intensity, and load power. In contrast, the storage battery precisely stores and releases energy to counteract variations in wind and photovoltaic power. The outcomes validate that in the presence of the UC, the microgrid performance is improved. However, the improvement is very close to that gained when using the proposed controller without UC. Hence, the proposed controller can reduce the cost, weight, and space of the system. Moreover, a Hardware-in-the-Loop (HIL) emulator was implemented using a C2000™ microcontroller LaunchPad™ TMS320F28379D kit (Texas Instruments, Dallas, TX, USA) to evaluate the proposed system and validate the simulation results.

Nonlinear Model Predictive Control of Heaving Wave Energy Converter with Nonlinear Froude–Krylov Forces

Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability in the energy market. This paper presents the application of a nonlinear model predictive controller (MPC) to enhance the energy extraction of a heaving point absorber. The wave energy converter (WEC) model accounts for the nonlinear dynamics and static Froude–Krylov forces, which are essential in accurately representing the system’s behavior. The nonlinear MPC is tested under irregular wave conditions within the power production region, where constraints on displacement and the power take-off (PTO) force are enforced to ensure the WEC’s safety while maximizing energy absorption. A comparison is made with a linear MPC, which uses a linear approximation of the Froude–Krylov forces. The study comprehensively compares power performance and computational costs between the linear and nonlinear MPC approaches. Both MPC variants determine the optimal PTO force to maximize energy absorption, utilizing (1) a linear WEC model (LMPC) for state predictions and (2) a nonlinear model (NLMPC) incorporating exact Froude–Krylov forces. Additionally, the study analyzes four controller configurations, varying the MPC prediction horizon and re-optimization time. The results indicate that, in general, the NLMPC achieves higher energy absorption than the LMPC. The nonlinear model also better adheres to system constraints, with the linear model showing some displacement violations. This paper further discusses the computational load and power generation implications of adjusting the prediction horizon and re-optimization time parameters in the NLMPC.

Modeling, simulations, and Simulink developments in the analysis of optimal control of temperature and pH in a batch ethanol fermentation process

Transfer of laboratory-scale experiments to production-scale ethanol fermentation is time-consuming and involves expensive prototype systems from complex experimental designs that determine optimal operating conditions for minimal substrate and product inhibitions. The study developed and validated a Simulink-based model for optimal pH and temperature control using fuzzy logic and PID controllers respectively and taking advantage of 2D and 1D substrate and product inhibition models from which suitable ethanol fermentation reaction rates models were selected. Temperature and pH levels and substrate, product, and biomass concentrations were measured. Selected inhibition models were linear-product, linear substrate-sudden stop product, and linear substrate for cassava, maize, and sorghum, respectively. Fuzzy logic controller ascertained optimal flow rate of acid and base as 0.000196 ml/s and 0.000204 ml/s, respectively, and pH error and rate of pH error as 0.00334 and 0.00368, respectively. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1905; maize: F critical 0.9704, F calculated = 0.2149; sorghum: F critical = 0.9704, F calculated = 0.2488). PID logic controller showed model curves and experimental curves with good fit. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1288; maize: F critical = 0.9704, F calculated = 0.2083; sorghum: F critical = 0.9704, F calculated = 0.2016). The study provided an improved approach as solution for optimal pH and temperature conditions in order to mitigate substrate and product inhibitions during ethanol fermentation. It illustrated that the application of artificial intelligence-based controllers provides satisfactory outcomes that are desirable for implementation in the industrial space.Graphical

Solar PV focused LFC studies utilizing an SFS-optimized PID with fractional derivative (PIDDμ), and incorporating BESS and FESS applications

This study investigates the application of a PID controller with a fractional derivative component (PIDDμ), optimized using the stochastic fractional search (SFS) technique, for load frequency control (LFC) in power systems. Additionally, solar PV and battery energy storage systems (BESS) and flywheel energy storage systems (FESS) are integrated as backup sources to enhance the LFC performance. To assess the effectiveness of the proposed approach, three error metrics—Integral Time Absolute Error (ITAE), Integral Time Multiplied Square Error (ITSE), and Integral of Absolute Error (IAE)—are evaluated. The results are compared with recent optimization-based LFC techniques across various scenarios. It is found that the SFS-PIDDμ achieves IAE=0.006786, ITSE=1.07e-05, and ITAE=0.01853, which are significantly lower than the error values produced by other LFC techniques. Furthermore, integrating solar PV, along with BESS and FESS, further reduces these error values. The graphical LFC responses of the SFS-PIDDμ outperform other optimization-based LFC techniques. The robustness of the approach is also validated against random load changes of varying magnitudes and for broader parametric variations.

A maiden application of an MGO-tuned cascade controller for the secondary frequency control in biogas-powered marine shipboard microgrid

The use of renewable energy sources in marine microgrids has become crucial to limiting the emissions from the shipping industry. Hence, sources like biogas, solar photo-voltaic, sea wave energy, etc. are being integrated rapidly into marine microgrids. In this study, a novel implementation of cascaded controllers like 1 + PI-TID and 1 + PD-PI is modelled for the secondary load frequency control of the shipboard system. The proposed controllers are then tuned with a newly developed metaheuristic algorithm i.e. mountain gazelle optimisation. Moreover, a comparative study is done with other newly developed algorithms. The performance of these controllers is checked in diverse scenarios. The proposed method gives a 23.79% and 54.07% increase stability for the conventional PI controllers. Furthermore, for studying cyber-attacks in the proposed system, various durations of communication delay are introduced and its stability is evaluated. Finally, sensitivity and uncertainty analysis is performed for the validation of the effectiveness of the proposed controller.

Experimental Evaluation of Damping and Stiffness in Optimized Active Sport Utility Vehicle Suspension Systems

On a flat city road, the specificity of the suspension poses a challenge. Sport utility vehicles (SUVs) have less flexibility compared to regular cars, despite providing a firm grip on asphalt and concrete. Constant adjustments to the center of gravity of SUVs are necessary. Surprisingly, they are less reliable and stable in urban settings due to this characteristic. In this study, a mathematical model of the suspension system of SUVs based on the Newtonian approach is introduced and validated with data from experiments carried out by the MTS machine system on the mono-tube (oil/air) mixed damper element. The model accurately predicts the performance of this damper, commonly used in SUVs, across various operating conditions, including different frequencies. A coil spring element, serving as a passive suspension unit with this damper, is also tested experimentally under similar conditions. By integrating passive suspension elements with the active actuator, the proposed modified design reduces the power consumption needed for the actuator to function and ensures a certain level of reliability. The effectiveness and performance of the modified active suspension system in comparison to the traditional passive suspension system are assessed using three different strategies: hybrid PID-LQR, linear quadratic regulator (LQR), and proportional-integral-derivative (PID). The genetic algorithm is utilized to determine the optimal parameter values for each controller by minimizing a cost function, maximizing performance, and minimizing energy consumption. Simulation results demonstrate that the active suspension system controlled by the PID-LQR controller offers significantly improved ride comfort and vehicle stability compared to other systems. This suggests that the performance of the active suspension system is greatly enhanced by the combined application of the hybrid PID-LQR controller compared to other systems.

Comparative Analysis of the Guided Bomb Flight Control System for Different Initial Conditions

The article presents research on the influence of initial conditions on the self-guidance of a guided bomb towards a stationary ground target. The aim of the study was to investigate the correlation between the initial tilt angle of the guided bomb and the precision of impact, as well as the time required to reach the surface target. To fulfil this purpose, the flight control system of the guided bomb needed to be designed accordingly. The need to develop these systems arises from emerging information that Ukraine and Israel are converting unguided bombs into precision-guided ones. This justification is based on objective reasons. The article analyses the application of classical controllers: PI, PD and PID. Their task was to accurately guide a stationary ground target under various initial conditions. A preliminary method has been proposed for selecting optimal gain coefficients for the PID controller, which constitutes the main component of the autopilot of the guided bomb flight control system. A proprietary interpolation method was suggested, starting with the use of an optimization function in MATLAB software. The numerical findings are presented in a graphical manner.

Real‐Time Model Predictive Control of Air‐Conditioners Through IoT—Results From an Experimental Setup in a Tropical Climate

ABSTRACTThere is an increasing demand for air‐conditioners (ACs) to maintain a comfortable indoor environment for all types and sizes of buildings including commercial, industrial, and office spaces. Such ACs are mostly operated by traditional ON–OFF controllers to maintain a temperature setpoint. Extensive control engineering knowledge resulting from experiments on actual buildings is needed before the wide application of an advanced control methods, such as model predictive control (MPC), which are more effective and energy‐efficient than the traditional controllers. Simulation studies on the application of control may provide promising results, but the corresponding experimental validations may prove otherwise. User‐friendly experimental setups to investigate the performance of real‐time advanced control on an actual building and its HVAC system is scarce. This paper details the design, implementation and testing of a user‐friendly, remote and autonomous test bed to acquire measured data through an IoT platform, and to control ACs in real time through MATLAB Thingspeak. Measurement and data acquisition equipment are installed in a two‐zone concrete building in Mauritius. MPC of the indoor air temperature achieved an AC temperature setpoint tracking RMSE which was 0.7°C lower than that achieved by the built‐in ON/OFF AC control. The test bed also revealed that portable air‐conditioners are not very efficient, given that the maximum cooling efficiency achieved in this work was only 60%. It also provided valuable insights based on the experiments carried out, in terms of improvements to sensing and data acquisition. Control engineers can implement such a test bed for the development and application of advanced controllers as per their needs and applications.

A novel weighted exponential sliding mode controller with a modified reaching law for the frequency regulation of a renewable integrated isolated AC microgrid

This paper investigates the development and application of a novel hybrid controller through novel sliding surface and novel reaching law, Weighted Exponential Sliding Mode Control with Modified Reaching Law (WESMC-MRL), for frequency regulation in an isolated Microgrid (MG) with intermittent Renewable Energy Sources (RESs) such as solar and wind. Major contributions include the analysis of frequency regulation of isolated MG controlled by WESMC-MRL under various load perturbations. The performance of WESMC-MRL is compared with conventional Linear Quadratic Regulator (LQR), Sliding Mode Control (SMC), and Integral Sliding Mode Control (ISMC) controllers. Outcomes indicate that the WESMC-MRL effectively regulates the MG frequency with minimal chattering, and outperforms LQR SMC, and ISMC in terms of settling time, overshooting, and undershooting in frequency deviation. With the WESMC-MRL the maximum frequency deviation is found only 0.00724 Hz and undershoot is negligible. The proposed controller is also robust against RES integration variability, disturbances, and nonlinearities making it a promising solution for reliable and stable MG operation.

Design of a PID Controller for Microbial Fuel Cells Using Improved Particle Swarm Optimization

The microbial fuel cell (MFC) is a renewable energy technology that utilizes the oxidative decomposition processes of anaerobic microorganisms to convert the chemical energy in organic matter, such as wastewater, sediments, or other biomass, into electrical power. This technology is not only applicable to wastewater treatment but can also be used for resource recovery from various organic wastes. The MFC usually requires an external controller that allows it to operate under controlled conditions to obtain a stable output voltage. Therefore, the application of a PID controller to the MFC is proposed in this paper. The design phase for this controller involves the identification of three parameters. Although the particle swarm optimization (PSO) algorithm is an advanced optimization algorithm based on swarm intelligence, it suffers from issues such as unreasonable population initialization and slow convergence speed. Therefore, this paper proposes an improved particle swarm algorithm based on the Golden Sine Strategy (GSCPSO). Using Circle chaotic mapping to make the distribution of the initial population more uniform, and then using the Golden Sine Strategy to improve the position update formula, not only improves the convergence speed of the population but also enhances convergence precision. The GSCPSO algorithm is applied to execute the described design process. The results of the simulation show that the designed control method exhibits smaller steady-state error, overshoot, and chattering compared with sliding-mode control (SMC), backstepping control, fuzzy SMC (FSMC), PSO-PID, and CPSO-PID.

Active control vibrations of aircraft wings under dynamic loading: Introducing PSO-GWO algorithm to predict dynamical information

This study presents an innovative approach to mitigate vibrations induced by external shock on composite structures through the application of an intelligent controller. Leveraging the first-order shear deformation panel theory, a sophisticated controller scheme is developed, integrating methodologies such as the differential quadrature approach and Laplace transform. Furthermore, deep neural network (DNN) and support vector regression (SVR) techniques are employed to enhance prediction accuracy and control efficiency. Additionally, two optimized hybrid models are proposed, incorporating Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) algorithms, to further refine the controller's performance. The proposed methodology aims to address the challenges associated with vibrations in composite structures by providing a comprehensive and adaptive control solution. By utilizing advanced optimization algorithms and machine learning techniques, the controller can effectively adapt to dynamic changes in external shock conditions, thereby minimizing vibrations and ensuring structural integrity. The integration of ANN and SVR enhances the controller's predictive capabilities, enabling it to anticipate and respond to varying shock scenarios with precision. Through theoretical analysis and numerical simulations, the effectiveness of the proposed intelligent controller is demonstrated in reducing vibrations and enhancing the structural stability of composite systems. The optimized hybrid models, employing PSO and GWO algorithms, further improve the controller's performance by fine-tuning its parameters for optimal control efficiency. Overall, this research contributes to the development of robust control strategies for mitigating vibrations in composite structures subjected to external shock, with potential applications in aerospace, automotive, and civil engineering industries.

A Deep Reinforcement Learning Approach to DC-DC Power Electronic Converter Control with Practical Considerations

In recent years, there has been a growing interest in using model-free deep reinforcement learning (DRL)-based controllers as an alternative approach to improve the dynamic behavior, efficiency, and other aspects of DC–DC power electronic converters, which are traditionally controlled based on small signal models. These conventional controllers often fail to self-adapt to various uncertainties and disturbances. This paper presents a design methodology using proximal policy optimization (PPO), a widely recognized and efficient DRL algorithm, to make near-optimal decisions for real buck converters operating in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) while handling resistive and inductive loads. Challenges associated with delays in real-time systems are identified. Key innovations include a chattering-reduction reward function, engineering of input features, and optimization of neural network architecture, which improve voltage regulation, ensure smoother operation, and optimize the computational cost of the neural network. The experimental and simulation results demonstrate the robustness and efficiency of the controller in real scenarios. The findings are believed to make significant contributions to the application of DRL controllers in real-time scenarios, providing guidelines and a starting point for designing controllers using the same method in this or other power electronic converter topologies.

Model Validation and Real-Time Process Control of a Continuous Flow Ohmic Heater

Ohmic heating is a highly efficient method for rapid fluid heating, with applications in fields such as food processing, pharmaceuticals, and chemical engineering. Prior to its industrial application, thorough analysis and modeling are crucial to ensure safe and efficient operations. Therefore, this research focuses on the development and validation of a transfer function-based model for a continuous flow ohmic heater (CFOH). Validation metrics include root mean square error (RMSE) and mean absolute percentage error (MAPE). The developed model achieves an RMSE of ±1.48 and a MAPE of ±2.58% compared to experimental results, demonstrating its accuracy. Furthermore, the research presents the implementation of robust real-time applications of advanced process controllers, including PID, MPC, and AMPC. These controllers were first simulated using the developed model and subsequently deployed in the pilot plant ohmic heater system to achieve precise temperature control and optimised input voltage. The reliability of this procedure was affirmed through a comparison between simulated results and empirical data obtained from the CFOH pilot plant. The study concludes by suggesting potential applications in fault diagnosis, educational training, system identification, and controller design.

Teaching Reform and Exploration of Practical Courses Based on Programmable Control

As the country continues to promote the development of intelligent manufacturing, all industries are carrying out enterprise automation upgrading, the Pearl River Delta Intelligent Manufacturing Conference held in March 2024 provides a direction guide for each enterprise on how to integrate the intelligent manufacturing technology into each link and provide direction guidance for enterprises to create new models and new business formats. College teachers, in focusing on the teaching process, should closely match the enterprise and social needs and cultivate excellent students. As the core controller of automation control, the application of programmable controllers in teaching is particularly important. In practical classes, by setting progressive difficulty, project guidance, team collaboration, and other links, students can master the automation equipment design of programmable control in repeated practice.

Simulation Application of BPNN-PID Controller with Improved Ant Colony Algorithm in Temperature Control System

Aiming at the nonlinear and hysteresis of PID controller in temperature control, this paper proposes a BPNN-PID control scheme with improved ant colony algorithm, and uses MATLAB to simulate and compare experiments. The results show that compared with the traditional ant colony algorithm control and BPNN control methods, the improved system has higher precision and better anti-interference ability in temperature control, and has higher practical application value.

The Effects of Pointing Error Sources on Energy Delivery from Orbiting Solar Reflectors

Many proposals are being made for cleaner, more sustainable forms of energy production. Terrestrial solar photovoltaic farms (SPFs) could potentially deliver large quantities of energy to the grid, although these are limited to daytime use. The output from these SPFs could be enhanced, particularly around dawn and dusk, by the use of orbiting solar reflectors (OSRs) in near-polar orbit. These would reflect an image of the solar disk, or solar image (SI), onto the SPFs to augment their energy output. Pointing requirements are therefore to ensure that reflected sunlight is delivered to the terrestrial SPF, avoiding the losses incurred by an offset of the SI and the SPF itself. The SI would typically be of order 10 km for a reflector in a 1000 km orbit. Given the potentially large size of the reflectors, this presents a challenge for the attitude determination and control system (ADCS) to ensure that the maximum quantity of energy can be delivered to a SPF, typically requiring large control moment gyro actuators. In addition, there exist numerous sources of error in the ADCS which can cause further degradation in the quantity of energy delivered to the SPF. These errors can manifest in the resolution of the various sensors, flexible structural modes, manufacturing inaccuracies, and misalignments due to vibration during launch. This paper will investigate the effects of pointing error sources (PES) on the reflector ADCS and so on the quantity of energy delivered to the SPF. With the application of a PD controller with feedforward compensation, and the set of noise characteristics defined in this paper, numerical simulations will show the typical losses in energy delivered to SPFs of 0.015% when the model accounts for PES in onboard sensors, actuator uncertainty and flexible structural modes.

Adaptive Network-Based Fuzzy Inference System–Proportional–Integral–Derivative Controller Based on FPGA and Its Application in Radiofrequency Ablation Temperature Control

The radiofrequency ablation temperature system is characterised by its time-varying, non-linear, and hysteretic nature. The application of PID controllers to the control of radiofrequency ablation temperature systems has a number of challenges, including overshoot, dependence on high-precision mathematical models, and difficulty in parameter tuning. Therefore, in order to improve the effectiveness of radiofrequency ablation temperature control, an adaptive network-based fuzzy inference system combined with an incremental PID controller was used to optimise the shortcomings of the PID controller in radiofrequency ablation temperature control. At the same time, the learning rate at the time of updating the consequence parameters was set by segmentation to solve the problem of poor control accuracy when the ANFIS-PID controller is implemented based on FPGA fixed-point decimals. Based on FPGA-in-the-loop simulation experiments and ex vivo experiments, the effectiveness of the ANFIS-PID controller in the temperature control of radiofrequency ablation was verified and compared with the PID controller under the same conditions. The experimental results show that the ANFIS-PID controller has a superior performance in terms of tracking capability and stability compared with the PID controller.

Final state control-based active compensation for backlash in vibration suppression of automobile powertrain

This paper proposes an effective backlash compensation method based on the final state control (FSC) for active vibration control of an automobile powertrain. Transient vibration of the powertrain caused by sudden changes in the engine torque significantly degrades the driving performance and driver comfort. In addition, the nonlinear characteristics owing to the backlash increase the adverse effects of these oscillations. To suppress the transient vibration of the powertrain, a controller is designed based on the mixed H2/H∞ control theory. Subsequently, to adjust for the vibration amplification owing to the backlash, we propose the application of a shock-reducing controller based on the FSC. Linearly decreasing the inertia weight in particle swarm optimisation (LDWPSO) was applied to the design of this controller and parameters were optimised. Finally, simulations were conducted using the control method; the controller could prevent the occurrence of shocks owing to the gear collisions and achieved smooth an acceleration.

Key technologies for airworthiness compliance verification of the solid-state power controller

Solid-state power distribution technology has advantages such as lightweight, automation, high power quality, and high fault tolerance, which has a very broad prospect. As the core device of solid-state power distribution technology, the application of the solid-state power controller (SSPP) is faced with the challenge that it is difficult to use the traditional compliance verification methods to demonstrate compliance with relevant airworthiness regulations in the application process. Based on the C919 aircraft power distribution system, this article proposed corresponding compliance verification methods, which combine explanation, analysis, and test verification, to confirm and validate the relevant functions, performance, and impact after the failure of SSPC. This may provide a reference for the development of related technologies in the future.