Tilt Integral Derivative Research Articles

Performance analysis on an integrated three-area thermal AGC system with various wind velocities considering HCSA-optimized PI-TIDN Controller

This article investigates the impact of a high-voltage direct-current (HVDC) link and a wind turbine system (WTS) on the dynamics of a three-area thermal automatic generation control (AGC) system. A novel controller, the cascade of proportional-integral (PI) and tiltintegralderivative (TID) with filter coefficient (N) (PI-TIDN) controller is projected. The WTS units are subjected to various wind velocity scenarios, including fixed and random wind velocities. The controller parameters are concurrently enhanced using the hybrid crowsearch algorithm (HCSA). The system dynamics corresponding to the PI-TIDN controller are superior to those of PIDN and TIDN controllers. Additionally, studies with different wind velocities demonstrate that responses with fixed wind velocities are better than those with random wind velocities. Moreover, integrating WTS units with the thermal system improves dynamics compared to the thermal system alone. It is also apparent that the parallel AC-HVDC system enhances dynamics. Furthermore, sensitivity analysis exposes that the PI-TIDN controller values at nominal settings are vigorous and do not require retuning.

Cascaded Integral Minus Tilt Integral Derivative With Filter for Frequency Stabilization of V2G/G2V Enabled Hybrid Microgrid

ABSTRACTRenewable generation plays an important part in today's power system. With the inclusion of the inverter interfaced renewable energy sources (RESs) into a microgrid, the total system inertia decreases and it leads to increased frequency deviation in presence of a disturbance. This paper proposes a cascaded Integral Minus Tilt Integral Derivative with Filter (I–TDN)‐Proportional‐Integral (PI), [(I‐TDN)‐PI] controller for frequency stabilization of a hybrid microgrid in presence of electric vehicles (EV). The microgrid model includes reheated thermal power plant with high degree of non‐linear system such as inverter based RESs like photovoltaic and wind generation systems. A diesel engine generator is incorporated for load frequency control during perturbation in the system frequency. Virtual inertia controller (VIC) with inverter based energy storage system (ESS) is commonly used to improve system inertia and frequency stability of the microgrid. In addition to the ESS, this paper proposes inclusion of the EVs in this VIC. The optimal gains of the proposed cascaded I‐TDN‐PI controller are determined using Mountain Gazelle Optimizer (MGO), a modern metaheuristic optimization algorithm. Sensitivity of the proposed controller is investigated in presence of system nonlinearities, load perturbations, time delay, system parameter variation and RES power fluctuations. The simulation results justify the robustness of the proposed control structure for frequency stabilization of the microgrid.

Enhanced Frequency/Voltage Control in Multi-Source Power System Using CES and SSA-Optimized Cascade TID-FOPTID Controller

The stability and reliability of modern power systems, especially those with several renewable energy sources, strongly rely on load frequency control (LFC) and automatic voltage regulation (AVR) for frequency and voltage control under inconstant load demands. In this study, a new cascade tilt integral derivative-fractional order proportional tilt integral derivative (TID-FOPTID) controller is presented that is polished through the salp swarm algorithm (SSA) for LFC of a multi-area power system with AVR in all control areas. The system integrates capacitive energy storage (CES) and deals with governor dead-band and generation rate constraint nonlinear effects in a thermal-hydro-gas three-area multi-source power system (TAMSPS). The analysis of results, due to the SSA-based TID-FOPTID controller compared to TID-FOTID and TID-FOTID + 1 shows a positive outcome. Integrating CES brings additional improvement in the system performance, the overshoot/undershoot, and settling time are minimized. Finally, the sensitivity analysis reveals that the suggested controller will be effective and reliable for enhancing the frequency stability of the complex power system with a high RES integration level.

Controller design and optimal sizing of battery energy storage system for frequency regulation in a multi machine power system

Frequency regulation is one of the key components needed to keep the power grid stable and reliable in the case of an imbalance between generation and load. This study looks at several control techniques for Battery Energy Storage Systems (BESSs) to keep the frequency stable in the power system during generation/load disruptions. This research aims to build several BESS controllers, including the proportional-integral (PI), proportional integral derivative (PID), and Tilt-Integral Derivative (TID) controllers. Also, the BESS controller parameters are optimized and compared by using metaheuristics based particle swarm optimization (PSO) and the BAT algorithm. However, for practical power systems with high MVA ratings, the size of the battery energy storage systems has to be increased considerably to offset frequency deviations. Additionally, by utilizing PSO to reduce the frequency deviations within prescribed limits, this work presents the optimal BESS sizing for multimachine power systems. Furthermore, the research study examines the impact of BESS connected to various voltage levels, such as LV, MV, and HV subgrids. It also examines BESS's role in frequency management, which is an area of expertise that needs further investigation and is anticipated to have a big influence on the frequency stability of the system. Time domain simulations are carried out, which shows that the PSO based controller design is capable of stabilizing the system frequency with superior performance as well as the BESS's capability to participate effectively in frequency regulation. Additionally, this paper also implements the experimental verification through Hardware-in-the-Loop (HIL) simulations to validate the performance and robustness of the proposed control system. The WSCC 9-bus test system is used to demonstrate the BESS's efficacy. The system is modelled and simulated using the DIgSILENT PowerFactory software.

Frequency stabilization of interconnected diverse power systems with integration of renewable energies and energy storage systems

Recent improvements in renewable energy sources (RESs) and their extensive use in the power industry have created significant issues for their operation, security, and management. Due to the ongoing reduction of power system inertia, maintaining operational frequency at its nominal value and minimizing tie-line power variations constitute essential variables for these improvements. A novel improved frequency stabilization approach based on modified fractional order tilt controller is presented for interconnected diverse power systems with integration of sea wave energy, photovoltaic, wind, energy storage system and biodiesel generator. The fractional order tilt integral double derivative (FOTIDD2) is a novel controller that is developed to address the frequency stabilization problems of a hybrid power structure that is integrated with sources of clean energy and devices for storing energy. A recent optimization algorithm inspired by human behavior called mother optimization algorithm (MOA) is applied to adjust the coefficient of the proposed cascaded controller. The FOTIDD2 controller was compared with other control methods in terms of its transient performance, in order to determine its effectiveness. Further, the mother optimization algorithm results are compared with those of sine cosine algorithm (SCA), fox optimization algorithm (FOA), and grey wolf optimization (GWO). FOTIDD2 controller was found to be more effective than PID controller in respect of reducing overshoot (Osh) by 79.31%, 83.78% and 67.99%, improving time settlement by 33.22%, 29.87%, and 22.45% and reducing undershoot (Ush) by 79.13%, 89.34%, and 86.90% for the (∆F1), (∆F2), and (∆Ptie), respectively.

Performance evaluation of fuel cell based on a DC-DC boost converter through optimized MPPT controller

The current electric vehicle domain is increasingly focused on fuel cell technologies due to its flexibility, steady supply of power, low atmospheric pollution, increased startups, and rapid responses. Fuel cells exhibit nonlinear power versus current characteristics, making it challenging to extract maximum peak power from the fuel stack. To address this, this work introduces an adaptive Coati Optimization algorithm combined with a Tilt-integral-derivative (TID) controller (TID-ACOA) to find the maximum power point (MPP) of the fuel stack systems, ensuring maximum power extraction. The proposed MPPT controller is compared with other MPPT controller, including PI, TID, and TID-COA. Comprehensive evaluations are conducted on tracking current, voltage, maximum power extraction, MPPT controller efficiency, converter voltage settling time, and oscillations. The fuel stack’s low output voltages are enhanced using a boost DC-DC converter, and the entire fuel stack-fed boost converter systems is modeled using MATLAB/Simulink. Simulation result show that the TID-ACOA MPPT controller achieves higher MPP tracking efficiency compared to conventional controllers.

Improved Performance of the Permanent Magnet Synchronous Motor Sensorless Control System Based on Direct Torque Control Strategy and Sliding Mode Control Using Fractional Order and Fractal Dimension Calculus

This article starts from the premise that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) control strategy, is characterized by the fact that the internal flux and torque control loop usually uses ON–OFF controllers with hysteresis, which offer easy implementation and very short response times, but the oscillations introduced by them must be cancelled by the external speed loop controller. Typically, this is a PI speed controller, whose performance is good around global operating points and for relatively small variations in external parameters and disturbances, caused in particular by load torque variation. Exploiting the advantages of the DTC strategy, this article presents a way to improve the performance of the sensorless control system (SCS) of the PMSM using the Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead–Lag under constant flux conditions. Sliding Mode Control (SMC) and FOSMC are proposed under conditions where the flux is variable. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system performs better is verified. The article also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance for the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional-Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM-SCS based on the DTC strategy in a complete Hardware-In-the-Loop (HIL) implementation.

A deterministic design approach of tilt integral derivative controller for integer and fractional-order system with time delay

A tilt integral derivative (TID) controller modifies the proportional integral derivative (PID) controller in the fractional domain. It converts the proportional gain as a function of frequency and is thereby capable of achieving optimal system response. The usual practice for the parameter estimation of the TID controller is by minimization of the error-based objective functions using optimization techniques. Although precise results can be achieved, these nature-inspired algorithms are stochastic and hence produce different solutions during different iterations. Therefore, a comparative statistical study is usually necessary to validate the best possible result. This study shows a deterministic analytical procedure for the paramssseter estimation of TID controllers. The magnitude and phase angle criteria, along with the frequency-domain loop shaping specifications, are used for the explicit evaluation of the TID parameters. Because of its model-independent nature, this tuning strategy can be used for a variety of integral and nonintegral order systems with different plant structures. In this article, the authenticity of the applied procedure is demonstrated through suitable numerical examples. The complexity of the design problem is enhanced by using it for both integer and non-integer (fractional) order plus time-delay systems. Further, the robustness of the control system in the presence of a TID controller was examined under the influence of external parameters and input reference changes. Simulation studies validate the supremacy of TID controllers over PID controllers in terms of reference tracking and disturbance rejection capabilities.

Energy saving and speed control in autonomous electric vehicle using enhanced manta ray foraging algorithm optimized intelligent systems

The integration of autonomous vehicles (AVs) with hybrid electric vehicles (HEVs) aims to address the problem of optimizing energy economy while improving reliability and safety. By using intelligent driver support technologies and sophisticated control algorithms, this method aims to lower emissions and fuel consumption. Therefore, the HEVs develop an intelligent driver aid system that integrates an energy management system (EMS) with adaptive cruise control (ACC). To maximize energy use, the system integrates HEVs with autonomous vehicle technologies. In order to maintain safe distances from the lead vehicle, identify the desired acceleration, and switch between speed and distance control, the ACC uses a deep learning system in conjunction with a tilt integral derivative (TID) controller. This ensures stability. Based on ACC commands, the EMS controls energy usage. An enhanced manta ray foraging optimization (EMRFO) technique is used to significantly enhance speed control and energy economy. Simulink/MATLAB analysis of the suggested model shows notable gains in energy consumption control, particularly with the ACC with deep neural network (DNN) system, and considerable reductions in driving risks. In addition, the system precisely controls engine torque to maintain a 72.75 % State of Charge (SoC) and an average engine energy efficiency of 32.36 %. To validate the performance, the proposed approach is put into practice in real-time configuration. The configuration for the experiment produced an ideal efficiency of 31.99 % with minimal error using the proposed method.

Frequency Stabilization Based on a TFOID-Accelerated Fractional Controller for Intelligent Electrical Vehicles Integration in Low-Inertia Microgrid Systems

Microgrid systems face challenges in preserving frequency stability due to the fluctuating nature of renewable energy sources (RESs), underscoring the importance of advanced frequency stabilization strategies. To ensure power system stability in situations where renewable energy significantly contributes to the energy mix, it is essential to implement load frequency controllers (LFCs). Moreover, with the widespread use of electric vehicles (EVs), leveraging battery storage from EVs for microgrid frequency control is becoming increasingly crucial. This integration enhances grid stability and offers a sustainable solution by utilizing renewable energy more efficiently and reducing dependency on traditional power sources. Therefore, this paper proposes an innovative approach to LFCs, using fractional-order control techniques to boost the resilience of the interconnected microgrid systems. The approach centers on a centralized control scheme with a tilt fractional-order integral-derivative featuring an accelerated derivative (TFOID-Accelerated) controller. The accelerated derivative component of this controller is tailored to mitigate high-frequency disturbances, while its tilt feature and fractional integration effectively handle disturbances at lower frequencies. As a result, the proposed controller is expected to efficiently counteract disturbances caused by variability in RESs and/or load changes, achieving a high level of disturbance rejection. Additionally, this paper employs the recent growth optimizer (GO) method for the optimal design of the controller’s parameter set, avoiding the need for complex control theories, elaborate disturbance observers, filters, and precise power system modeling. The GO algorithm enhances fractional-order capabilities, offering a robust solution to the challenges of renewable energy variability and demand fluctuations. This is accomplished by optimizing parameters and simplifying the control system design across different microgrid scenarios. The proposed TFOID-Accelerated LFC demonstrates superior performance in enhancing frequency stability and minimizing oscillations compared to existing controllers, including traditional proportional-integral-derivative (PID), PID-Accelerated (PIDA), and tilt-integral-derivative (TID) controllers.

Optimization of the different controller parameters via OBL approaches based artificial ecosystem optimization involving fitness distance balance guiding mechanism for efficient motor speed regulation of DC motor

This study proposes a new optimization approach, which is called as artificial ecosystem optimization algorithm with fitness-distance balance guiding mechanism by using opposite based learning methods (FDBAEO_OBLs) for the speed regulation of direct current (DC) motor. The performance of the proposed FDBAEO_OBL algorithm is tested in two different experimental studies. In the first experimental study, the proposed approach is tested in the CEC2020 benchmark test functions and the FDBAEO algorithm, which included the best OBL approach, is determined using non-parametric Wilcoxon and Friedman statistical analysis methods. Second, the parameters of proportional integral derivative (PID), tilt integral derivative (TID), proportional integral derivative with filter (PIDF), tilt integral derivative with filter (TIDF), fractional-order proportional integral derivative (FOPID), fractional-order proportional integral derivative with filter (FOPIDF), proportional integral derivative with fractional-order filter (PIDFF) and fractional-order proportional integral derivative with fractional-order filter (FOPIDFF) controller structures to be used in DC motor closed loop speed control are determined with FDBAEO_OBL, and the performances of the controllers are investigated. Integral absolute error (IAE), integral time absolute error (ITAE), integral time squared error (ITSE) and integral squared error (ISE) performance indices are used as the objective function of the operation process in which the control parameters are determined. According to the comparative step response results of the controller structures, the four best controller structures for DC motor speed regulation are determined. The performances of these controllers are examined under different simulation conditions and according to the results obtained, it is seen that the best controller structure is FOPIDFF. The FDBAEO_OBL algorithm, which is used in both benchmark test functions and DC motor speed regulation, shows an effective, durable and superior performance in finding the optimal solution values during the optimization.

Augmenting the Stability of Automatic Voltage Regulators through Sophisticated Fractional-Order Controllers

The transition from traditional to renewable energy sources is a critical issue in current energy-generation systems, which aims to address climate change and the increased demand for energy. This shift, however, imposes additional burdens on control systems to maintain power system stability and quality within predefined limits. Addressing these challenges, this paper proposes an innovative Modified Hybrid Fractional-Order (MHFO) automatic voltage regulator (AVR) equipped with a fractional-order tilt integral and proportional derivative with a filter plus a second-order derivative with a filter FOTI-PDND2N2 controller. This advanced controller combines the benefits of a (FOTI) controller, known for enhancing dynamic performance and steady-state response, with a (PDND2N2) controller to improve system robustness and adaptability. The proposed MHFO controller stands out with its nine tunable parameters, providing more extensive control options than the conventional three-parameter PID controller and the five-parameter FOPID controller. Furthermore, a recent optimization approach using a growth optimizer (GO) has been formulated and applied to optimally adjust the MHFO controller’s parameters simultaneously. The performance of the proposed AVR based on the MHFO-GO controller is scrutinized by contrasting it with various established and developed optimization algorithms. The comparative study shows that the AVR based on the MHFO-GO controller surpasses other AVR controllers from the stability, robustness, and dynamic response speed points of view.

Optimizing power system stability: A hybrid approach using manta ray foraging and Salp swarm optimization algorithms for electromechanical oscillation mitigation in multi‐machine systems

AbstractThis paper emphasizes the significance of ensuring adequate damping of electromechanical oscillations in power systems to ensure stable operation. Power System Stabilizers (PSSs) are influential in enhancing system damping and refining dynamic characteristics during transient conditions. However, the efficacy of PSSs is notably contingent on parameter values, particularly in the case of lead‐lag PSSs. In response to this challenge, the paper introduces a Tilt‐Integral‐Derivative (TID)‐based PSS, optimized through a novel optimization algorithm called Hybrid Manta Ray Foraging and Salp Swarm Optimization Algorithms (MRFOSSA). The MRFOSSA algorithm demonstrates robustness and enhanced convergence, validated through benchmark function tests, and outperforms competing algorithms. These superior characteristics of MRFOSSA were employed in optimal tuning of TID‐PSSs to uphold the stability of multi‐machine power systems. The MRFOSSA algorithm demonstrates robustness and enhanced convergence, outperforms competing algorithms in the optimal tuning of TID‐PSS within the Western System Coordinating Council (WSCC)‐3‐machines 9‐bus test system. In summary, the proposed TID‐PSS, coupled with the MRFOSSA algorithm, presents a promising avenue for enhancing power system stability.

Modified Tilt-integral active disturbance rejection controller with inertia emulated direct current link for frequency control of marine microgrid

The increasing integration of intermittent renewable sources (RSs) poses a dynamic frequency stability challenge for modern marine vessel microgrids. To address this issue, this paper proposes a novel control approach, specifically targeting frequency and tie-line power stabilization in a diverse source marine microgrid (MμG) with two intertied areas featuring renewable (wind-wave) sources. The suggested approach introduces a modified tilt-integral active disturbance rejection (TI-ADRC) controller designed to ensure effective damping of power frequency oscillations. As the control scheme depends on the optimal setting of the proposed controller, a recently developed marine predator technique (MPT) has been adopted. The performance of the proposed controller is compared with other recent controllers viz. PID, tilt-integral derivative (TID), two-degree-of-freedom (2DOF)-PID, fuzzy-PI, and ADRC to validate its superiority. To further enhance the system dynamics, a precise modeling of inertia emulated direct current (IEPDC) tie link is incorporated in microgrid system. The impact assessments, considering time delays with pre/post IEPDC link, demonstrate a substantial 57.79% and 81.53% reduction in peak frequency overshoot compared to DC link (conventional model) and AC link, respectively. The analysis of the eigen plot confirms the stability of the control system. Sensitivity assessments of the controller against ± 30% parametric variations and load fluctuations are conducted, affirming its robustness. Finally, the result from OPAL-RT confirm the practicality of the proposed method. It is asserted that the suggested controller is reliable and functions well in n the presence of diverse disruptions and parametric variations.

A fast relay feedback auto-tuning tilt-integral-derivative (TID) controller method with the fractional-order Ziegler–Nichols approach

The relay feedback auto-tuning method, which was an early commercialized approach, has maintained its popularity due to its simplicity and robustness. However, the classical proportional integral derivative (PID) controller auto-tuning method often results in unacceptable overshoot, especially for integrating and higher-order processes. By integrating the TID controller with the relay feedback technique, we significantly enhance dynamic control performance without relying on prior knowledge of the system. However, the direct application of the classical auto-tuning method to the TID controller encounters challenges due to the additional fractional-order transfer function s−1n. Therefore, we have developed a fractional-order Ziegler–Nichols (FOZ-N) approach, specifically designed to adjust the parameters of the TID controller. The proposed FOZ-N method is fast, simple, and capable of achieving the desired performance. In contrast to the previous Ziegler–Nichols tuning method, the TID controller parameters Kt and Ki are determined to shift the critical point (−1/Ku,j0) to the FOZ-N point (−0.5,−j0.7) on the Nyquist curve, ensuring system robustness and dynamic performance. The impact of the fractional order parameter s−1n and ratio r is explored through time-domain analysis, where these parameters are determined to ensure optimal dynamic performance. Additionally, we provide a detailed tuning procedure along with a helpful example. To demonstrate the advantages of the proposed auto-tuning TID controller over the Ziegler–Nichols PID controller, Optimal PID controller, simple internal model control (SIMC) PID controller, and Ziegler–Nichols FOPID controller, we present a simulation illustration involving multiple different systems. To validate the practical outcomes of this paper, we present experimental results on the temperature control of a Peltier cell.

LFC performance advancement of two-area RES penetrated multi-source power system utilizing CES and a new CFOTID controller

This study addresses the challenges of load fluctuations and the assimilation of renewable energy sources (RES) causing undesirable frequency deviations. To avoid widespread deviations in frequency, an intelligent, competent and robust load frequency control (LFC) scheme and energy storage system are obligatory for disturbance rejection in RES penetrated realistic interconnected multi-source power system (IMSPS). Hence, a new cascade fractional order tilt integral derivative (CFOTID) controller is suggested with capacitive energy storage (CES) for a pragmatic RES based thermal-hydro-gas IMSPS having governor dead-band and generation rate constraint nonlinearities. The salp swarm algorithm (SSA) is utilized successfully for fine-tuning of the controller parameters. Scrutiny of the outcome results reveals the sovereignty of the CFOTID controller over several existing well proven controllers and SSA-based FOTID. It is observed that power generation due to CES further advance the system results in terms of minimized overshoot, undershoot, and settling time. Lastly, a sensitivity analysis is conducted to confirm the robustness of the controller.

Load frequency and voltage control of two area interconnected power system using covariance matrix adaptation evolution strategy based Aquila optimization optimized fast fuzzy-fractional order tilt integral derivative controller

In this paper, an optimal tuning method is given for a Fast Fuzzy-Fractional Order Tilt Integral Derivative Controller (HFF-FOTIDC) used in the Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR) systems of a two-area interconnected power system. LFC is in charge of managing the frequency and real power flows in the system. AVR is in charge of maintaining the voltage profile and managing the flow of reactive power. In Area 1, a step disturbance is used to study how the system works in a dynamic way. By looking at the system's voltage, power flow through the tie lines, and frequency, the experts can figure out how the system will react. The proposed control techniques combine two well established control logic, which are fast fuzzy control logic and fractional order tilt integral derivative control logic. The main goal is to decrease voltage and frequency changes caused by step disturbances and return them to their normal values. The simulation results show how good the suggested system is by comparing it to systems that use traditional proportional integral controllers and traditional Integral controllers. By using the finest tuning method for the fast fuzzy-fractional order tilt integral derivative controller, the designed system has better performance than standard controllers at reducing fluctuations and getting the desired frequency and voltage responses. The parameters of the proposed controller are optimized by using Covariance Matrix Adaptation Evolution Strategy based Aquila Optimization (CMAESAO) optimization technique.

Stabilization and tracking control of underactuated ball and beam system using metaheuristic optimization based TID-F and PIDD2-PI control schemes.

In this paper, we propose two different control strategies for the position control of the ball of the ball and beam system (BBS). The first control strategy uses the proportional integral derivative-second derivative with a proportional integrator PIDD2-PI. The second control strategy uses the tilt integral derivative with filter (TID-F). The designed controllers employ two distinct metaheuristic computation techniques: grey wolf optimization (GWO) and whale optimization algorithm (WOA) for the parameter tuning. We evaluated the dynamic and steady-state performance of the proposed control strategies using four performance indices. In addition, to analyze the robustness of proposed control strategies, a comprehensive comparison has been performed with a variety of controllers, including tilt integral-derivative (TID), fractional order proportional integral derivative (FOPID), integral-proportional derivative (I-PD), proportional integral-derivative (PI-D), and proportional integral proportional derivative (PI-PD). By comparing different test cases, including the variation in the parameters of the BBS with disturbance, we examine step response, set point tracking, disturbance rejection analysis, and robustness of proposed control strategies. The comprehensive comparison of results shows that WOA-PIDD2-PI-ISE and GWO-TID-F- ISE perform superior. Moreover, the proposed control strategies yield oscillation-free, stable, and quick response, which confirms the robustness of the proposed control strategies to the disturbance, parameter variation of BBS, and tracking performance. The practical implementation of the proposed controllers can be in the field of under actuated mechanical systems (UMS), robotics and industrial automation. The proposed control strategies are successfully tested in MATLAB simulation.

Optimal Design of Fractional Order Tilt-Integral Derivative Controller for Automatic Generation of Power System Integrated with Photovoltaic System

Optimal Design of Fractional Order Tilt-Integral Derivative Controller for Automatic Generation of Power System Integrated with Photovoltaic System

Application of Tilt Integral Derivative for Efficient Speed Control and Operation of BLDC Motor Drive for Electric Vehicles

This study presents the tilt integral derivative (TID) controller technique for controlling the speed of BLDC motors in order to improve the real-time control of brushless direct current motors in electric vehicles. The TID controller is applied to the considered model to enhance its performance, e.g., torque and speed. This control system manages the torque output, speed, and position of the motor to ensure precise and efficient operation in EV applications. Brushless direct current motors are becoming more and more popular due to their excellent torque, power factor, efficiency, and controllability. The differences between PID, TID, and PI controllers are compared. The outcomes demonstrated that the TID control enhanced the torque and current stability in addition to the BLDC system’s capacity to regulate speed. TID controllers provide better input power for BLDC (brushless DC) drives than PI and PID controllers do. Better transient responsiveness and robustness to disturbances are features of TID controller design, which can lead to more effective use of input power. TID controllers are an advantageous choice for BLDC drive applications because of their increased performance, which can result in increased system responsiveness and overall efficiency. In an experimental lab, a BLDC motor drive prototype is implemented in this study. To fully enhance the power electronic subsystem and the brushless DC motor’s real-time performance, a test bench was also built.