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Due to improper selection of gradient update direction or learning rate, SGD optimization algorithms for deep learning suffer from oscillation and slow convergence. Although Adam algorithm can adaptively adjust the update direction and learning rate at the same time, it still has the overshoot phenomenon, and hence suffers from wasting computing resources and slow convergence. In this work, the PID controller from the feedback control area is borrowed to re‐express the adaptive optimization algorithm (the Adam optimization algorithm is derived into the integral I component form) of deep learning. In order to alleviate the overshoot phenomenon and hence speed up the convergence of Adam, a complete adaptive PID optimizer (adaptive‐PID) is proposed by incorporating the proportional P and derivative D component. Extensive experiments on standard data sets verify that the proposed adaptive‐PID algorithm significantly outperforms Adam algorithm in terms of convergence rate and accuracy.

Linear quadratic regulator (LQR) and proportional-integral-derivative (PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions. The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method.

Sustainable energy based hybrid microgrids are advantageous in meeting constantly increasing energy demands. Conversely, the intermittent nature of renewable sources represents the main challenge to achieving a reliable supply. Hence, load frequency regulation by adjusting the amount of power shared between subsystems is considered as a promising research field. Therefore, this paper presents a new stratagem for frequency regulation by developing a novel two stage integral-proportional-derivative with one plus integral (IPD-(1+I)) controller for multi sources islanded microgrid system (MS-IμGS). The proposed stratagem has been tested in an MS-IμGS comprising of a wind turbine, parabolic trough, biodiesel generators, solid-oxide fuel cell, and electric water heater. The proposed model under different scenarios is simulated in MATLAB environment considering the real-time recorded wind data. A recently developed sine-cosine algorithmic technique (SCA) has been leveraged for optimal regulation of frequency in the considered microgrid. To identify the supremacy of the proposed technique, comparative studies with other classical controllers with different optimization techniques have been performed. From the comparison, it is clearly evident that, SCA-(IPD-(1+I)) controller gives better performance over other considered stratagems in terms of various time domain specific parameters, such as peak deviations (overshoot, undershoot) and settling time. Finally, the robustness of the proposed stratagem is evaluated by conducting sensitivity analysis under ±30% parametric variations and +30% load demand. The lab tests results validate the operation of the proposed system and show that it can be used to regulate the frequency in stand-alone microgrids with a high penetration of renewable energy.

For regulating the Synchronous Generator (SG) output voltage, the Automatic Voltage Regulator (AVR) system is a significant device. This work propounds a survey on Optimization Algorithms (OAs) utilized for tuning the controller parameters on the AVR system. A device wielded for adjusting the SG’s Terminal Voltage (TV) is named AVR. A Controller is utilized for improving stability and getting a superior response by mitigating maximum Over Shoot (OS), reducing Rise Time (RT), reducing Settling Time (ST), and enhancing Steady State Error (SSE) since output voltage has a slower response and instability. The controllers utilized here are Proportional-Integral-Derivative (PID), Intelligent Controller (IC), along with Fraction Order PID (FOPID). Owing to the occurrence of time delays, nonlinear loads, variable operating points, and others, OAs are wielded for tuning the controller. (a) Particle Swarm Optimization (PSO), (b) Genetic Algorithm (GA), (c) Gray Wolf Optimizer (GWO), (d) Harmony Search Algorithm (HSA), (e) Artificial Bee Colony (ABC), (f) Teaching Learned Based Optimization (TLBO), et cetera are the various sorts of OA. For enhancing the TV response along with stability, various OAs were tried by researchers.

This paper presents the design and implementation of an LED driver utilizing an interleaved Watkins–Johnson converter (ILWJC). ILWJC is proposed for the control of LED system. The converter's architecture, incorporating multiple phases with diodes, switches, and coupled inductors, allows for efficient voltage transformation and precise current control. Its interleaved configuration minimizes current ripple and enhances performance efficiency, ideal for applications demanding strict current regulation. This research, employing MATLAB's SIMSCAPE for simulations, focuses on a 12-V, 2.4-W chip-on-board injection type LED module. The study assesses the performances of proportional–integral–derivative (PID) and fractional order PID (FOPID) control systems in managing the ILWJC, with the FOPID controller showing superior outcomes in terms of faster response times, reduced steady-state error, lower peak overshoot, and improved voltage and current ripple control. These results underline the FOPID controller’s potential to enhance responsiveness, accuracy, and stability in LED lighting systems. The research also identifies an optimal phase shift for interleaving at 240 degrees, achieving the lowest current ripple at 63 mA, thereby enhancing conversion efficiency. A 12-W LED driver was successfully implemented in hardware, demonstrating the practical viability of the ILWJC for real-world applications.

In this experimental study, a new technique is designed and presented for controlling the rotor side converter of an induction generator (IG) for multi‐rotor wind turbine (MRWT) systems. The direct power command (DPC) strategy is used to regulate the reactive and active power (Qs and Ps). DPC is characterized by several drawbacks, the most prominent of which are low durability, low current/power quality, and the use of power estimation. Therefore, a new PDPI (proportional‐derivative proportional‐integral) regulator is used as a suitable solution to overcome these shortcomings while maintaining simplicity, achieving a rapid dynamic response, and obtaining gains that characterize the DPC. The suggested DPC for controlling the IG inverter of an MRWT system uses two PDPI regulators and pulse width modulation (PWM) to create and generate the pulses necessary to run and regulate the IG inverter. First, the DPC‐PDPI‐PWM is verified in a MATLAB using different tests, and the characteristics of the DPC‐PDPI‐PWM is compared to that of DPC under different working conditions for a 1500 kW IG. Second, the validity of the simulated results is verified using the Hardware‐in‐the loop (HIL) test for the DPC‐PDPI‐PWM, and dSPACE 1104 is used for this purpose. The results demonstrate the effectiveness of the DPC‐PDPI‐PWM approach over DPC, as the harmonic distortion of the stream is minimized by 36.66%, 22.68%, and 33.33% in the three proposed tests. Also, the overshoot value of Ps was reduced compared to DPC by ratios estimated at 70.96%, 71.42%, and 70.31% in all tests. DPC‐PDPI‐PWM also reduces the steady‐state error of Qs compared to DPC by 68.33%, 58.82%, 67.90% in all tests performed. The experimental results confirm the numerical results, suggesting that the DPC‐PDPI‐PWM is a suitable solution in the field of command in the future. This research proposes an effective and simple solution to reduce electrical energy ripples from an energy system based on multi‐rotor wind turbines. The mathematical model of the proposed solution is given, mentioning the pros and cons. The contribution of the proposed solution in increasing the quality of energy and the durability of the energy system is confirmed using experimental work and simulation, with results highlighting the extent of its superiority and high performance compared to several research works.

Multi-area power systems (MAPSs) are highly complex non-linear systems facing a fundamental issue in real-world engineering problems called frequency stability problems (FSP). This paper develops an enhanced slime mold optimization algorithm (ESMOA) to optimize the tuning parameters for a cascaded proportional derivative-proportional integral (PD-PI) controller. The novel ESMOA proposal includes a new system that combines basic SMO, chaotic dynamics, and an elite group. The motion update incorporates the chaotic technique, and the exploitation procedure is enhanced by searching for a select group rather than merely the best solution overall. The proposed cascaded PD-PI controller based on the ESMOA is employed for solving the FSP in MAPSs with two area non-reheat thermal systems to keep the balance between the electrical power load and the generation and provide power system security, reliability, and quality. The proposed cascaded PD-PI controller based on the ESMOA is evaluated using time domain simulation to minimize the integral time-multiplied absolute error (ITAE). It is evaluated in four different test situations with various sets of perturbations. For tuning the cascaded PD-PI controller, the proposed ESMOA is compared to the golden search optimizer (GSO) and circle optimizer (CO), where the proposed ESMOA provides the best performance. Furthermore, the findings of the proposed cascaded PD-PI controller based on the ESMOA outperform previous published PID and PI controllers adjusted using numerous contemporary techniques.

This study proposes a new optimization technique, known as the eagle strategy arithmetic optimization algorithm (ESAOA), to address the limitations of the original algorithm called arithmetic optimization algorithm (AOA). ESAOA is suggested to enhance the implementation of the original AOA. It includes an eagle strategy to avoid premature convergence and increase the populations’ efficacy to reach the optimum solution. The improved algorithm is utilized to fine-tune the parameters of the fractional-order proportional-integral-derivative (FOPID) and the PID controllers for supporting the frequency stability of a hybrid two-area multi-sources power system. Here, each area composites a combination of conventional power plants (i.e., thermal-hydro-gas) and renewable energy sources (i.e., wind farm and solar farm). Furthermore, the superiority of the proposed algorithm has been validated based on 23 benchmark functions. Then, the superiority of the proposed FOPID-based ESAOA algorithm is verified through a comparison of its performance with other controller performances (i.e., PID-based AOA, PID-based ESAOA, and PID-based teaching learning-based optimization TLBO) under different operating conditions. Furthermore, the system nonlinearities, system uncertainties, high renewable power penetration, and control time delay has been considered to ensure the effectiveness of the proposed FOPID based on the ES-AOA algorithm. All simulation results elucidate that the domination in favor of the proposed FOPID-based ES-AOA algorithm in enhancing the frequency stability effectually will guarantee a reliable performance.

The uncertainties associated with multi-area power systems comprising both thermal and distributed renewable generation (DRG) sources such as solar and wind necessitate the use of an efficient load frequency control (LFC) technique. Therefore, a hybrid version of two metaheuristic algorithms (arithmetic optimization and African vulture's optimization algorithm) is developed. It is called the ‘arithmetic optimized African vulture's optimization algorithm (AOAVOA)’. This algorithm is used to tune a novel type-2 fuzzy-based proportional–derivative branched with dual degree-of-freedom proportional–integral–derivative controller for the LFC of a three-area hybrid deregulated power system. Thermal, electric vehicle (EV), and DRG sources (including a solar panel and a wind turbine system) are connected in area-1. Area-2 involves thermal and gas-generating units (GUs), while thermal and geothermal units are linked in area-3. Practical restrictions such as thermo-boiler dynamics, thermal-governor dead-band, and generation rate constraints are also considered. The proposed LFC method is compared to other controllers and optimizers to demonstrate its superiority in rejecting step and random load disturbances. By functioning as energy storage elements, EVs and DRG units can enhance dynamic responses during peak demand. As a result, the effect of the aforementioned units on dynamic reactions is also investigated. To validate its effectiveness, the closed-loop system is subjected to robust stability analysis and is compared to various existing control schemes from the literature. It is determined that the suggested AOAVOA improves fitness by 40.20% over the arithmetic optimizer (AO), while frequency regulation is improved by 4.55% over an AO-tuned type-2 fuzzy-based branched controller.

Switched Reluctance Motors has become one of the best solutions for EV applications because of its numerous benefits over other electric drive systems. Its excellent qualities are the robust design, double saliency, fault tolerance, and ability to withstand the heat of SRM drives. In order to minimize torque ripple and provide an exact speed response in SRM, this article mainly presents a speed and current control technique. The accurate speed control and torque ripple reduction of a SRM is controlled using the particle swarm optimization technique (PSO) with speed and current control mechanisms. The PID and FOPID speed controllers in the outer loop and current controller in the inner loop, respectively, are regulated, as are the 3- ∅ , 6/4 SRM turn-on ( T O ), and turn-off ( T F ), angles. The results were compared with existing optimization methods such as the SHO, LUS, GA, Ant-Lion, NSGA-II, MOLGSA, GSA, Hybrid MOLGSA, and RGA-SBX algorithms, show that a cascaded Fractional order PID(FOPID) controller offers better speed, current, and torque responses, as well as smaller current and torque ripples, under numerous different load and speed conditions. Under all load conditions, it has been demonstrated that the PSO-FOPID controller has the best speed response and minimal torque ripples when compared to the PSO-PID controller.