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Linear–quadratic regulator (LQR) is a control technique successfully implemented for several complex systems. However, LQR technique in certain cases fails due to disturbances mainly caused by the improper choice of weighting matrices Q and R. Methods using particle swarm optimisation (PSO) are used to find the weighting matrices of LQR and have been proven to be superior. The paper proposes an output feedback LQR proportional–integral–derivative (PID) controller, which is also compared with LQR and PSO-based LQR. The output feedback LQR–PID provides a better response, and the effectiveness of the proposed method is demonstrated through simulations, and experimental validations are conducted on various cases. [ABSTRACT FROM AUTHOR]

The performance of the robotic manipulator is negatively impacted by outside disturbances and uncertain parameters. The system's variables are also highly coupled, complex, and nonlinear, indicating that it is a multi-input, multi- output system. Therefore, it is necessary to develop a controller that can control the variables in the system in order to handle these complications. This work proposes six control structures based on neural networks (NNs) with proportional integral derivative (PID) and fractional-order PID (FOPID) controllers to operate a 2-link rigid robot manipulator (2-LRRM) for trajectory tracking. These are named as set-point-weighted PID (W-PID), set-point weighted FOPID (W-FOPID), recurrent neural network (RNN)-like PID (RNNPID), RNN-like FOPID (RNN-FOPID), NN+PID, and NN+FOPID controllers. The zebra optimization algorithm (ZOA) was used to adjust the parameters of the proposed controllers while reducing the integral-time-square error (ITSE). A new objective function was proposed for tuning to generate controllers with minimal chattering in the control signal. After implementing the proposed controller designs, a comparative robustness study was conducted among these controllers by altering the initial conditions, disturbances, and model uncertainties. The simulation results demonstrate that the NN+FOPID controller has the best trajectory tracking performance with the minimum ITSE and best robustness against changes in the initial states, external disturbances, and parameter uncertainties compared to the other controllers. [ABSTRACT FROM AUTHOR]

This paper presents an elitism-crossover barnacle mating optimization (ECBMO). It is an improvement of barnacle mating optimization (BMO). The original BMO suffers from local optima problem leading to a low accurate solution. A new method of offspring generation is adopted into the original BMO structure. Some features of the best-so-far solution are incorporated into the generated offspring. The accuracy performance of the proposed algorithm is tested on several IEEE functions. A statistical analysis is conducted to compare its performance over the original BMO. It is also applied to optimize proportional integral derivative (PID) parameters for controlling output voltage of a buck converter. Result of benchmark functions test shows the proposed algorithm has attained higher accuracy for all functions compared to BMO algorithm. Application on the real problem shows both algorithms control the converter voltage satisfactorily. However, the ECBMO has achieved more optimal PID parameters and leading to a better output voltage response. [ABSTRACT FROM AUTHOR]

This paper's goal is to use a proportional integral derivative controller and an artificial neural network controller (ANNC) to control the speed of a BLDC motor. The outcomes of the MATLAB/SIMULINK simulation are used as the basis for comparing the two controllers. On the other hand, the ANNC makes the BLDC motor's reaction more consistent and dependable. In other words, it reduces peak time, overshoot, and settle time while speeding up system responses. ANNC is implemented in Processor in the Loop (PIL) experimenting with Arduino Mega. It is based on the generated C-code and executed it in the embedded card. The experiment's outcomes match those of the MATLAB/SIMULINK simulation.

This study employs an FPGA board to implement a robust control technique for wind energy conversion systems (WECS). This approach facilitates extensive testing and validation of the control system across diverse wind conditions, utilizing the FPGA's parallel processing capabilities and advanced control algorithms. This method ensures robustness against nonlinearities and system uncertainties. FPGA-in-the-loop (FIL) testing provides precise and effective simulation results, enabling rapid prototyping and iterative modifications of control algorithms. The effectiveness of the robust control strategy is confirmed by FIL findings, demonstrating significant improvements in WECS stability and efficiency. Furthermore, the study highlights the strategy's potential to enhance WECS reliability and efficiency in real-world applications.

Electric power steering (EPS) poses significant control challenges in autonomous vehicles due to their inherent complexity and non-linearity. This study explores the application of artificial neural network (ANN) to address these limitations. Two approaches are proposed: 1) an ANN-based identifier utilizing the backpropagation (BP) algorithm to learn the system’s non-linear dynamics, and 2) an ANN-based controller leveraging the Levenberg-Marquardt (LM) algorithm to improve control performance. Our findings demonstrate the efficacy of the proposed ANN-based BP algorithm in EPS system identification achieving over 99.6% accuracy in predicting EPS system dynamics compared to the traditional approach. Additionally, the LM-learned ANN-based controller aiming a faster response and precise reference tracking compared to the traditional controller method. These advancements underscore the potential of employing ANN methodologies to optimize EPS performance in autonomous vehicles.

This paper attempts to obtain the optimal solution to enhance the performance of the Automatic Voltage Regulator (AVR) Controller, as it is an essential tool to control the synchronous generator output voltage. The controller improves AVR system stability and response time; moreover, it is demonstrated that the Proportional Integral Derivative (PID) controller achieves the goal by applying two artificial intelligence techniques to design the optimal values of the Automatic Voltage Regulator (AVR) PID controller for a single area model. The first is the Exponential Distribution Optimization Algorithm (EDO), and the second is the Transit Search Optimization Algorithm (TS). EDO and TS are used to determine the best PID controller parameters and have also recently been developed in the breadth of optimization problems and associated computational complexities field. The objective function, Integrated Square Error (ISE), minimizes the error voltage for improved stability and response. The outcomes are compared to various optimization techniques to prove the validation of the two proposed methods. The results show that the EDO and TS proved their superiority through their stability level to the AVR system and their steady-state error improvement. Moreover, the dominant effect of damping frequency decreases the oscillation and the reduced maximum overshoot that protects the loads from being subjected to non-permissible over-voltage levels. Finally, a robustness test is applied to the two proposed optimization methods to prove their reliability and effectiveness. [ABSTRACT FROM AUTHOR]

Power system oscillation is an unavoidable threat to the stability of an interconnected modern power system. The reliable operation of a modern power system is widely related to the dampening of electromechanical low‐frequency oscillations (ELFOs). These ELFOs must be dampened appropriately to maintain the stability and reliability of the system. However, it is relatively difficult to resolve the problem of ELFOs completely with traditional power system stabilizers (PSSs). Consequently, research should be directed towards the development of efficient damping controllers, or PSSs, for power oscillation damping. Motivated from the aforementioned fact, this article presents the design of a proportional integral derivative power system stabilizer (PID‐PSS) via a long short‐term memory neural network (LSTM) based deep neural approach for damping power system oscillations in interconnected power systems. LSTM is used to train the parameters of PID‐PSS. To evaluate the performance of the proposed LSTM based PID‐PSS, diverse test cases under different operating conditions are examined. Further, the performance of the proposed LSTM based PID‐PSS is compared with traditional PSSs through time‐domain simulations. The test cases reveal the desired efficiency achieved by the proposed LSTM based PID‐PSS under diverse loading conditions. [ABSTRACT FROM AUTHOR]

Presently, PV system is considered the best renewable energy source for electricity production. In order to maximize energy output in photovoltaic systems, a system for tracking the sun's position and adjusting panel positions was created. Despite the fact that several models for tracking solar radiation have been suggested to improve energy production, it faces challenges in continuous tracking and power consumption. This paper proposes a novel sensor-based solar tracking system with numerical optimization to increase photovoltaic systems' energy output. The initial model was for a two-axis tracking system based on sensors. Solar panel and sun positions are detected by this system using ultraviolet and microelectromechanical sun sensors. To improve tracking movements and photovoltaic energy production, we recommend using solar sensors to construct a novel two-axis solar tracking device. This technology benefits from increased solar radiation and solar energy harvesting capabilities. The main disadvantage of dual-axis active solar tracking systems is that the drive mechanism frequently uses up the output power of the solar panels. As a result, the net power gain of the solar panel is less than its maximum. Further, a control system was developed with proportional integral derivative (PID) controller and arithmetic optimization (AO) to adjust the panel position relative to suns movement. The AO technique tunes the PID controller gains until it reaches its desired level to enhance the production of power. With the use of sensors, the tracking system can use mathematical optimization algorithms and the sun's position to minimize power consumption while still maintaining optimal solar panel positioning. The developed model was designed in the MATLAB software and the outcomes are analyzed. The paper also presents an energy analysis of the system, which evaluates the energy input, output and overall system efficiency. We recommend using solar sensors to create a unique dual-axis solar tracking system. This technology benefits from increased solar radiation and solar energy harvesting capabilities.

This paper attempts to obtain the optimal solution to enhance the performance of the Automatic Voltage Regulator (AVR) Controller, as it is an essential tool to control the synchronous generator output voltage. The controller improves AVR system stability and response time; moreover, it is demonstrated that the Proportional Integral Derivative (PID) controller achieves the goal by applying two artificial intelligence techniques to design the optimal values of the Automatic Voltage Regulator (AVR) PID controller for a single area model. The first is the Exponential Distribution Optimization Algorithm (EDO), and the second is the Transit Search Optimization Algorithm (TS). EDO and TS are used to determine the best PID controller parameters and have also recently been developed in the breadth of optimization problems and associated computational complexities field. The objective function, Integrated Square Error (ISE), minimizes the error voltage for improved stability and response. The outcomes are compared to various optimization techniques to prove the validation of the two proposed methods. The results show that the EDO and TS proved their superiority through their stability level to the AVR system and their steady-state error improvement. Moreover, the dominant effect of damping frequency decreases the oscillation and the reduced maximum overshoot that protects the loads from being subjected to non-permissible over-voltage levels. Finally, a robustness test is applied to the two proposed optimization methods to prove their reliability and effectiveness.

In recent, photovoltaic (PV) power generation has increased in importance. The growing significance of PV power production has generated the demand for enhancing energy efficiency via continuous operation at the maximum power point (MPP). To enable effective MPP tracking, the suggested system integrates a proportional-integral (PI) controller with the perturb and observe (P&O) technique. In order to improve performance in a PV grid system, this work provides a unique method using a proportional-integral-derivative (PID) controller optimized using a genetic algorithm (GA). The proposed controller architecture integrates the GA algorithm with a PID controller in the voltage source inverter (VSI) of the PV system. To enable effective grid integration, the GA is used to continually optimize the PID controller settings. The converter's design criteria and computations are discussed, and MATLAB simulations are used to assess the system's performance. Compared to traditional PID controllers, the observed findings show increased efficiency, cheaper cost, and enhanced controllability. The suggested GA-PID controller offers opportunities for more study and development in this area while showing potential for improving PV grid system performance. [ABSTRACT FROM AUTHOR]

The load frequency control of a thermal power producing system, the ant colony optimization (ACO) approach adjusted proportional integral derivative (PID) controller is proposed. This work examines the single area thermal power system with/without generation rate constraint. For emergency situations, a PID controller is developed and used to regulate power system characteristics. This study uses the ACO method with the integral time absolute error objective function optimize controller gain values. In addition, the performance of the suggested approach is evaluated by introducing non-linearity components to the power-generating systems under study. Conventional, particle swarm optimization (PSO), and genetic algorithms (GA) approach tuned controller responses are compared with ACO. Fast settling with minimal peak and undershoots in the producing power supply of the system under emergency loading situations demonstrates the superiority of the proposed controller over competing controllers. [ABSTRACT FROM AUTHOR]

This study provides an efficient torque and speed control method for brushless DC (BLDC) motor utilizing a fractional order proportional integral derivative (FOPID) controller based on the multiverse optimization algorithm and salp swarm optimization (SSO). When combined with a solar photovoltaic (PV) system and a voltage boosting architecture, standard PID controllers regulated the BLDC drive parameters due to their simplicity and improved steady-state performances. A solar PV system with a specific power range is chosen for the best output solution on the output side. The MPPT method employed is the incremental conductance approach for tracking maximum power from the source. However, it has a problem with unpredictability owing to load changes. The PID controller tuning also relates to the structure's parametric uncertainties. Therefore, precise control methods may be offered with the aid of the FOPID controller to address the aforementioned issues. Simulation results of the proposed MVO and SSO-based FOPID controller for BLDC speeds run in the Matlab Simulink platform. To confirm the adaptability of the suggested control scheme for BLDC motor, the outcomes are interrelated using genetic algorithm, gray wolf, and PSO-based FOPID controllers. [ABSTRACT FROM AUTHOR]

This paper presents a hardware implementation upon a Field Programmable Gate Array (FPGA) of improved Vector Control Strategies (VCSs) based on a Fuzzy Logic System (FLS) of a grid-connected wind energy conversion system. Usually, the classical VCS is based on fixed-gain Proportional Integral Derivative (PID) controllers, which are known to exhibit limited performance against the nonlinear behavior of wind systems, such as rapid fluctuations of the wind speed and uncertainties of the system parameters. In order to overcome this limitation, an improved VCS based on Fuzzy Gain Scheduling PID controllers (VCS-FGS-PID) is suggested in this work to guarantee good tracking, high accuracy and good robustness under system parameter variations. Indeed, the PID controller gains are tuned, in real-time, by the FLS. In addition, the proposed VCS-FGS-PID methods are implemented on the FPGA in order to reduce the delays and the period of the system of the control loop, thanks to its parallel processing. In fact, the performance of the suggested VCS-FGS-PID approaches are proved by digital simulation with the Xilinx system generator tool under Matlab/Simulink, in addition to an experimental hardware-in-the-loop implementation using the FPGA. The obtained results demonstrate that the proposed VCS-FGS-PID techniques offer better performance as regards good tracking and high accuracy and good robustness against stator resistance variability compared to VCS-PI. [ABSTRACT FROM AUTHOR]

• Parameter optimization improves system performance. • Average structural responses were significantly reduced compared to an uncontrolled structure. • The BI system showed the highest reduction in peak acceleration and inter-storey drift. • The BI-AFTMD system achieved the greatest reduction in peak displacement with average optimal parameters. • The proposed PID controller effectively enhanced seismic resilience. This study presents an innovative approach to mitigating seismic responses in multi-storey buildings equipped with a base-isolation (BI) system and passive friction-tuned mass dampers (PFTMDs). The key innovation lies in the combined use of a BI system and a PFTMD system, as well as the activation of this mechanical system by controllers. Additionally, the research design optimizes the parameters of these devices specifically for each earthquake scenario and compares the results to the average of the optimal parameters, which has not been investigated in previous studies. In this study, a 10-storey structure is modeled, featuring a BI system beneath the first floor and a PFTMD system on the roof. The parameters for the BI, PFTMD, BI-PFTMD, and BI-active FTMD (BI-AFTMD) systems are independently optimized using a multi-objective particle swarm optimization (MOPSO) algorithm. To enhance the passive BI-PFTMD system, a proportional-integral-derivative (PID) controller is incorporated into the friction-tuned mass damper system, resulting in the BI-AFTMD hybrid control system that adjusts the final control force transmitted to the structure. The seismic performance of these systems is assessed for the 10-storey building under both far-field and near-field earthquakes. The findings reveal that these control systems significantly decrease average peak displacement, acceleration, and inter-storey drift as compared to an uncontrolled structure, especially when system parameters are optimized for the same earthquake scenario. Using average optimal parameters, the BI-AFTMD system achieves the most substantial reduction in average peak displacement, while the BI system offers the greatest reduction in average peak acceleration and inter-storey drift. [ABSTRACT FROM AUTHOR]

The load-frequency control is a matter of interest in power system operation as it addresses the problem of controlling the system in response to the disturbances. A well-known solution to this problem is feedback stabilization through Linear Quadratic Regulator. The observability condition has to be satisfied for the implementation of LQR, but for certain cases of multi-source power systems this criteria fails. Also, to obtain the desired response, proper selection of the weighting matricesQand R for LQR controller is inevitable. On a trial and error based choice of these matrices, the classical optimal controller may fail to achieve an optimum set point tracking, due to the absence of an integral control action. In this paper, a balanced reduction technique is proposed to obtain a minimal realization of the unobservable system, which makes the optimal control design possible for the control of change in frequency. Thus the paper also applies PID output feedback controller designed through LQR. The impact of reduction for minimal realization and the LQR based PID controller in the frequency stabilization of the considered systems are highlighted in this paper through pertinent cases. [ABSTRACT FROM AUTHOR]

The design and development of eco-friendly renewable energy sources is a critical process in the power generation system. Power generation of photovoltaic system depend on temperature and irradiation. Variation of atmospheric conditions need to find points for every instant on V-I characteristics of PV in which maximum power transfer from source to load is achieved. This work deals with Maximum Power Point Tracking (MPPT) method based on Adaptive Neuro Fuzzy Interference System (EANFIS) in standalone operation. The novelty is introduced in the design of inverter, motor selection, and maximum power point tracking. Quasi-Z-source inverter (qZSI) is designed with Z-shaped impedance network to continuously draw constant current from solar panel. MPPT enhance the efficiency of PV panel via load matching; however, it may be affected by environmental changes. Hence, an EANFIS-based MPPT technique is used in the proposed work to confirm maximum power delivery to current motor. The proposed method is the combination of ParticleSwarmOptimization (PSO) and Adaptive Neuro Fuzzy Inference System (ANFIS). Training stage of ANFIS is optimized by PSO to handle switching angle of Multi-Level Inverter (MLI) and generate harmonic-less control voltage, hence named Enhanced ANFIS (EANFIS). Voltage and current control of solar panel decide maximum power generation which is verified using Simulink and practical environment. Thus, EANFIS-based MPPT technique achieved the maximum tracking efficiency of 94% which is better than other comparison methods, namely P&O, RBFNN, ANN, and IDISMC. [ABSTRACT FROM AUTHOR]

Purpose: The present study is focused on designing an automated jet nebulizer that possesses the capability of dynamic flow regulation. In the case of existing equipment, 50% of the aerosol is lost to the atmosphere through the vent, during the exhalation phase of respiration. Desired effects of nebulization may not be achieved by neglecting this poor administration technique. There may be adverse effects like bronchospasm and exposure to high drug concentrations. Methods: The proposed nebulizer is composed of two modes as "Compressed Air" mode and the "Oxygen Therapy" mode. The automated triggering from one mode to another will be dependent upon the percentage of oxygen saturation of the patient, monitored from the SpO2 sensor. The compressed airflow will be delivered to the patient according to the minute ventilation, derived with the aid of a temperature sensor–based algorithm. The compressor controller circuitry ensures that the patient receives optimum level of compressed air as per the flow rate. At the end of the drug delivery, if the liquid level sensor detects the absence of medication within the nebulizer chamber, the nebulization process will be terminated. The dynamic regulation of the motor speed with respect to the minute ventilation was accomplished. Results: A laminar flow was obtained from the outlet of the compressor towards the nebulizer tubing, and a turbulent flow was obtained within the chamber. No excessive turbulent flows or rotational flow patterns were detected. Conclusion: Detecting the drug levels in the nebulizing chamber will prevent continuous workup and useful in situations where back-to-back nebulization is required. Oxygen therapy mode identifies the patient's desaturation and important where the patient can be already hypoxic or have a ventilation-perfusion mismatch, but may be disadvantageous in severe COPD patients. The aforesaid results could certainly lead to the improvements of the existing nebulizers. [ABSTRACT FROM AUTHOR]

Ship course-keeping plays a vital role in navigation safety, especially when a ship is operating under windy conditions. A method to control ship movements through rudder-system configuration is necessary to stabilize a ship’s course. This paper describes the twin-rudder-system configuration design’s impact on a ship’s course-keeping ability under windy conditions. A time-domain simulation using the MATLAB-Simulink program was developed for this purpose. A proportional integral derivative (PID) controller was used to adjust the ship‘s heading angle according to the desired path. Several parameters—such as relative wind velocity and directions—were accounted for in the simulation. The result shows that, at a wind direction of 88o, the ship’s course-keeping speed decreased; however, increasing wind velocity caused a large deviation in the ship’s heading angle. Meanwhile, the ship‘s course-keeping speed increased with rising windspeed directions of 219o. The ship’s course-keeping time, at around 219o under the simulation’s wind direction, was 11.84% lower than during a previous sea-trial. A possible reason for this difference is that the simulation excluded waves and currents.

CSTR (Continuous stirred tank reactor) is employed in process control and chemical industries to improve response characteristics and system efficiency. It has a highly nonlinear characteristic that includes complexities in its control and design. Dynamic performance is compassionate to change in system parameters which need more effort for planning a significant controller for CSTR. The reactor temperature changes in either direction from the defined reference value. It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature, and deviation from reference values may cause degradation of biomass quality. Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential. In this paper, a conventional Proportional Integral Derivative (PID) controller is designed. The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters. Hence, A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s limitation. In the proposed technique, PID parameters are tuned by Particle Swarm Optimization (PSO). It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response. In this article, a multi-objective function is proposed for PSO based controller design of CSTR. [ABSTRACT FROM AUTHOR]

Pharmaceutical therapy for certain respiratory diseases involves delivering aerosolized drugs directly to the respiratory tract through inhalation with nebulizers. This research is focused on designing an automated jet nebulizer that possesses the capability of dynamic flow regulation. The proposed nebulizer is composed of two modes, namely, the Compressed Air mode and the Oxygen Therapy mode. The automated triggering from one mode to another will be dependent upon the percentage of oxygen saturation of the patient, monitored from the SpO2 sensor. The compressed airflow will be delivered to the patient according to his or her volumetric breathing rate, derived with the aid of a temperature sensor-based algorithm. The compressor circuitry is incorporated with a PID control unit, which is a novel feature that acts as feedback as well as a safety mechanism in ensuring that the patient receives compressed air as per the flow rate decided by the system. At the end of the drug delivery, if the liquid level sensor detects the absence of medication within the nebulizer chamber, the nebulization process will be terminated. The results obtained from simulations showed that the PID unit functioned smoothly, with less overshoot and response time. Thus, the dynamic regulation of the motor speed with respect to the volumetric breathing rates was accomplished. A laminar flow was obtained from the outlet of the compressor towards the nebulizer tubing, and a turbulent flow was obtained within the chamber, as expected. No excessive turbulent flows or rotational flow patterns were detected. [ABSTRACT FROM AUTHOR]

The article compares the dynamic performances of the pump-controlled, valve-controlled and prime-mover controlled hydromotor drive systems for a wide range of operating conditions. The control strategy adopted for each of them varies the supply flow to the hydromotor that gives the regulated drive speed. In this respect, the Simscape models are developed for the said systems that are validated experimentally. On the basis of response time, overshoot, and steady-state error obtained from the simulation and test results, it is found that the valve controlled hydromotor drive system is more sensitive than the others; whereas, the load-sensing pump control requires more time to settle but has less overshoot. • Three different control experiments are carried out to achieve a constant speed of a hydromotor under varying load. • The system parameters are estimated and validated by using optimization techniques. • It is established that the valve controlled hydromotor drive is more sensitive than others. • The load-sensing hydromotor drive is found to have a more sluggish response compared to the others. [ABSTRACT FROM AUTHOR]

This paper proposed an improved structure of Proportional Integral Derivative (PID) controller called as Integral Proportional Derivative (I-PD), applied for Automatic Generation Control (AGC) of Multi-Source Interconnected Power System (IPS). The parameters of the proposed controller are optimized with a newly developed, powerful, nature-inspired meta-heuristic technique known as Fitness Dependent Optimizer (FDO). To show the efficacy of the proposed controller and the technique used, they have been tested on three different system models. Initially, a two-equal area of diverse source generation including reheat-thermal, gas, and hydro power system is considered. In the second scenario, the same power system model is used with addition of two non-linearities; Generation Rate Constraint (GRC) and Governor Dead Band (GDB). Lastly, multiple non-linearities including Governor Dead Band (GDB), Time Delay (TD), Generation Rate Constraint (GRC), and Boiler Dynamics (BD) have been considered to make the initial system more realistic and practical. The outcome from the proposed techniques is also compared with some recently meta-heuristic algorithms such as Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). From the results, it has been perceived that the proposed technique shows superior performance in respect of Overshoot (Osh), Undershoot (Ush) and Settling Time (Ts) of the system frequency.

This study employs an FPGA board to implement a robust control technique for wind energy conversion systems (WECS). This approach facilitates extensive testing and validation of the control system across diverse wind conditions, utilizing the FPGA's parallel processing capabilities and advanced control algorithms. This method ensures robustness against nonlinearities and system uncertainties. FPGA-in-the-loop (FIL) testing provides precise and effective simulation results, enabling rapid prototyping and iterative modifications of control algorithms. The effectiveness of the robust control strategy is confirmed by FIL findings, demonstrating significant improvements in WECS stability and efficiency. Furthermore, the study highlights the strategy's potential to enhance WECS reliability and efficiency in real-world applications., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Ship course-keeping plays a vital role in navigation safety, especially when a ship is operating under windy conditions. A method to control ship movements through rudder-system configuration is necessary to stabilize a ship's course. This paper describes the twin-rudder-system configuration design's impact on a ship's course-keeping ability under windy conditions. A timedomain simulation using the MATLAB-Simulink program was developed for this purpose. A proportional integral derivative (PID) controller was used to adjust the ship's heading angle according to the desired path. Several parameters--such as relative wind velocity and directions--were accounted for in the simulation. The result shows that, at a wind direction of 88o, the ship's course-keeping speed decreased; however, increasing wind velocity caused a large deviation in the ship's heading angle. Meanwhile, the ship's course-keeping speed increased with rising windspeed directions of 219o. The ship's course-keeping time, at around 219o under the simulation's wind direction, was 11.84% lower than during a previous sea-trial. A possible reason for this difference is that the simulation excluded waves and currents. [ABSTRACT FROM AUTHOR]

The performance of the robotic manipulator is negatively impacted by outside disturbances and uncertain parameters. The system's variables are also highly coupled, complex, and nonlinear, indicating that it is a multi-input, multi-output system. Therefore, it is necessary to develop a controller that can control the variables in the system in order to handle these complications. This work proposes six control structures based on neural networks (NNs) with proportional integral derivative (PID) and fractional-order PID (FOPID) controllers to operate a 2-link rigid robot manipulator (2-LRRM) for trajectory tracking. These are named as set-point-weighted PID (W-PID), set-point weighted FOPID (W-FOPID), recurrent neural network (RNN)-like PID (RNNPID), RNN-like FOPID (RNN-FOPID), NN+PID, and NN+FOPID controllers. The zebra optimization algorithm (ZOA) was used to adjust the parameters of the proposed controllers while reducing the integral-time-square error (ITSE). A new objective function was proposed for tuning to generate controllers with minimal chattering in the control signal. After implementing the proposed controller designs, a comparative robustness study was conducted among these controllers by altering the initial conditions, disturbances, and model uncertainties. The simulation results demonstrate that the NN+FOPID controller has the best trajectory tracking performance with the minimum ITSE and best robustness against changes in the initial states, external disturbances, and parameter uncertainties compared to the other controllers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Jasim Mohamed, Oleiwi, Azar and Mahlous.)

This article proposes the minimization of the frequency deviations in the three‐area hybrid power system. The application of thyristor controlled phase shifter (TCPS) and ultracapacitor (UC) is utilized on load frequency control (LFC). The quality of power supply is affected due to continuous and random changes in load during the operation of the power system. Hence, a power system control is required to maintain a continuous balance between power generation and load demand. LFC plays an important role in maintaining constant frequency and voltage in order to ensure the reliability of electric power. The proportional integral derivative (PID) is adopted for the LFC scheme. The three‐area system considered in this article is integrated with conventional and renewable energy sources such as solar, biomass, and fuel cell. TCPS connected in series with the tie‐line ensures the stabilization of the system and provides additional service like dynamic power flow control in the tie‐line and the power quality is sustained by the subordinate control of supercapacitor energy storage. The system variability is basically due to the frequency fluctuations when it is subject to the load deviations. Thus, the suppression of peak value of frequency deviation through power modulation control is the only way to reduce this problem. By using this method, the efficiency of proposed technique becomes better in deregulated environment which is accepted for the load frequency control scheme. By utilizing these technologies, the better results are obtainable than without the usage of TCPS and a supercapacitor. [ABSTRACT FROM AUTHOR]

• Microgrid is a small grid that uses renewable energy sources to generate power. • Performance of microgrid may deteriorate due to the uncertainty in load dynamics. • A controller is proposed to control the voltage, current and power of a microgrid. • The controller is designed for both single and three phase islanded microgrid. • Performance of the controller is measured and compared against various loads. Future grids will consist of many small and large microgrids that will require complex control of grid interaction as well as islanding operation. This paper addresses the control of islanded microgrids under dynamic system conditions. In this paper, a robust model reference modified adaptive proportional integral derivative (PID) controller is designed for voltage and current control augmentation of islanded microgrid against various load dynamics. The variation of load dynamics may cause the unsafe operation of the microgrid. The objective of the designed controller is to confirm the safe operation against voltage and current control under different load dynamics. The controller is designed based on selecting the desired reference model and controlling parameters. The effectiveness of the proposed controller is evaluated against the presence of load dynamics, harmonic sources, asynchronous machines, nonlinear and unknown loads. The results show that the proposed controller is capable of providing high tracking performance and safe operation for both the single and three phase islanded microgrid system. [ABSTRACT FROM AUTHOR]

In this work, the mechanical and electrical components are designed and realised for an octocopter. The designed system dynamic model is supported with Euler-Lagrangian model and Newton-Euler model respectively for the rotational and transnational movements of the drone. The prototype octocopter is also equipped with a proportional integral derivative controller to feedback both location and respond to the external environment. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to propose two variants of bio-inspired algorithm, Constriction Coefficient Particle Swarm Optimization (CCPSO) and Differential Evolution (DE), which determine optimal parameters of Proportional-Integral-Derivative (PID) controller. To achieve this purpose, the PID controller has been plugged to the Model Reference Adaptive Controller (MRAC) that balances Inverted Pendulum (IP) with nonlinear characteristic in vertical-upright position. For comparison purpose, efficiency of these intelligent approaches to adjust MRAC parameters has been evaluated in terms of time response performance. Finally, the overall simulation results demonstrate that both the algorithms yield acceptable response in controlling the nonlinear model of IP system. However, the performance of DE algorithm is better than CCPSO in terms of transient response characteristics. [ABSTRACT FROM AUTHOR]

The frequency deviation and power fluctuation need to be controlled in a wind-integrated power system (WIPS) for keeping the balance between system power generation and demand, which support the quality and stability of overall power system. The present paper addresses this problem while concerning the integration of intermittent wind power and load disturbance into the WIPS. With this intent, it proposes the compensated superconducting magnetic energy storage (CSMES) system with proportional integral derivative (PID) controller for improving the frequency and power deviation profile. A novel swarm intelligence-based artificial bee colony (ABC) algorithm is used for optimal design of PIDCSMES system. Robustness of the proposed ABC-based PID-CSMES control strategy is tested in WIPS under various disturbance patterns of load and wind power. To demonstrate the improved dynamic response, their simulation results are compared with particle swarm optimization-based PID-CSMES, PID with SMES, and only PID controller technique. The performance indices and transient response characteristics of frequency and power deviation are used to evaluate and compare the accuracy and efficiency of each controller. Stability of various system configurations is analyzed using eigenvalue location. Comparing the results of different controller in WIPS indicates a substantial improvement in the dynamic response of system frequency and power deviations by utilizing the proposed control strategy. [ABSTRACT FROM AUTHOR]