Maximum Peak Overshoot Research Articles

LQR controller performance via particle swarm optimization and neural networks

AbstractThe inverted pendulum‐cart (IPC) control problem has long been a benchmark in the field of control systems due to its inherent instability and nonlinear dynamics. The linear quadratic regulator (LQR) control technique has been proven to be effective in stabilizing the inverted pendulum; however, the challenge lies in finding the optimal control gains that provide the best performance. This article presents a method to address the LQR control design problem for the IPC system which is compared against a particle swarm optimization based LQR and a neural network optimized LQR. The method provides a deterministic approach to finding the weighing matrices Q and R in accordance with the time domain characteristics chosen by the designer, such as settling time and maximum peak overshoot. Results from MATLAB simulations indicate that the suggested strategy has good performance.

Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT

The power system is mainly affected by transient situations caused by switching heavy loads. The system may become unstable when transient situations occur. The power system should be able to perform continuous operation to maintain its voltage within acceptable limits. To achieve more stability and increase its speed of response, an Automatic Voltage Regulator (AVR) system requires the inclusion of a controller. The AVR system in the generating station uses the PID controller to adjust the abnormal voltage caused by transient conditions. To maintain the nominal voltage level under all the load conditions in the system, a bio-inspired meta-heuristic algorithm called Artificial Rabbit Optimization (ARO) algorithm is proposed to tune the PID controller gain parameters and obtain the optimal gain, thereby the AVR system adjusts the generator terminal voltage to nominal levels. The ARO algorithm inspires natural survival techniques to improve the AVR performance by reducing errors. To maintain a stable voltage profile in a power system, this research mathematically models survival techniques using the Internet of Things (IoT) to obtain an optimal solution. As a result, all devices connected to the power network receive a stable voltage that ensures their voltage reliability. The effectiveness of the proposed algorithm for the AVR system is verified with the MATLAB R2022a model, and the statistics functions are implemented in the module of Pandas, Scipy and mathematical investigations done in Numpy. The proposed ARO algorithm achieves a better voltage profile with less than 12.63% maximum peak overshoot during the system's transient response. The proposed algorithm provides the fastest response and highest stability comparable to other optimisation algorithms.

Fractional-order PID controller for blood pressure regulation using genetic algorithm

In this paper, fractional order PID controller [FOPID] is designed for automatic blood pressure regulation. One of the most common problems faced by post-operative patients is abnormal blood pressure (BP). Infusion of sodium nitroprusside (SNP) into the patient reduces blood pressure by relaxing the peripheral vasculature's muscles. The patient’s BP must be continuously monitored by an expert and the correct dose has to be given to the patient. It will be difficult to give the desired dosage to a patient manually because drug sensitivity is different for different patients. So, an appropriate controller must be designed to regulate blood pressure and minimize clinical expenses. A fractional-order PID controller is proposed in this paper to regulate mean arterial pressure, and FOPID parameters are optimized as a function of minimizing integral absolute error (IAE), integral squared error (ISE), and integral time absolute error (ITAE) using genetic algorithm. The performance of the proposed system is improved in terms of undershoot, settling time, and maximum peak overshoot. A MATLAB environment was used for the simulation of the system and the implementation of the algorithm.

Improve power quality of charging station unit using African vulture optimization algorithm

In recent years, there is growth in acceptance to consume fewer fossil fuels globally and the manufacturing of electric vehicles (EVs) has become more popular. However, the increase in the number of systems connected to the grid that contain EVs with a huge power capacity leads to unstable working in the power system. To assess the stability of the electric charging station several control approaches in AC part and DC parts during charging mode and discharging modes are tested. African vulture optimization algorithm (AVOA) has been utilized to tune the system controllers (proportional integral derivative (PID)/tilt integral derivative (TID) controllers). The superiority of AVOA is confirmed by comparing the performance with the genetic algorithm (GA). Two objective functions have been used i.e. integral time absolute error (ITAE) and integral square time error (ISTE). AVOA-tuned TID controllers using ISTE were found to be the best to contain the frequency deviations. The results have shown of the AC part and DC part is within an acceptable limit recommended by IEEE standard. Further, maximum peak overshoot, undershoot, and settle time obtained by AVOA-tuned PID and TID controllers are found the best. Finally, the improvement of the performance index obtained by AVOA over its counterpart GA is confirmed.

A time-delayed proportional-derivative controller for a dielectric elastomer circular membrane

• In this paper, an internal resonance of a circular DE membrane is investigated within mechanical and electrical excitations in order to find internal resonances for improving its performance for future applications. • This work proposes a time-delayed proportional-derivative (TDPD) controller for decreasing the oscillations of a DE system with quadratic and cubic nonlinearities. • Averaging method is used to solve the controlled system to obtain the first-order approximation solutions and response equations for the DE system. • The TDPD controller is the best control strategy that can reduce the framework vibrations regardless of the excitation frequency. Due to the substantial deformation generated by voltage excitation, the dielectric elastomer (DE) is a new type of functional polymer that can be employed as a smart actuator. In this paper, an internal resonance of a circular DE membrane is investigated within mechanical and electrical excitations in order to find internal resonances for improving its performance for future applications. All programs that contain DE will perform flawlessly if DE is managed. This work proposes a time-delayed proportional-derivative (TDPD) controller for decreasing the oscillations of a DE system with quadratic and cubic nonlinearities. The system is researched and investigated when using a Proportional–derivative (PD) controller to apply time-delayed control on displacement and velocity. Stability is improved, maximum peak overshoot is reduced, settling time is reduced, and the framework transient response is recovered using this control method. The first-order approximation solutions for the DE system are calculated using the averaging method. Simultaneous resonance is regarded as the worst type of resonance. To obtain the dynamic solution of the device with TDPD controllers, analytical and numerical approaches are used. The Routh-Huriwitz approach is used to review and analyze the consistency of the steady state solution in the near-resonance case. The effects of various factors on the steady-state solution are identified and discussed. The effects of time delay are investigated in order to determine the most stable range of time delays for the best performance. The MATLAB software package is used to generate simulation effects. When the results are compared to the numerical simulations, they show that the approximate solution and the control algorithm used in this paper are well validated. At the end, there is a comparison with previously published work. The focus of this paper is on using a TDPD controller to manage the nonlinear dynamic response and probable internal resonances of a circular DE membrane under mechanical and electrical excitations. The approximate solution of a nonlinear system of differential equations is investigated using the averaging technique in the cases of primary and 1:2 internal resonance, which are the worst-case scenarios that should be avoided. To display and compare controller effects at various system settings, several response curves are used. The TDPD controller is used to research nonlinear dynamics control of the DE system with harmonic force. The stability analysis and the effects of parameter behavior are tested using MATLAB program. To examine the controller effect, several response curves are used. The collected findings demonstrated that the TDPD control efficiency in reducing the system's nonlinear oscillations. The frequency response curves of the system were studied at various uncontrolled and regulated coefficients. Before and after control, response curve diagrams employing frequency response equations are created. Finally, validation curves are shown to measure the degree of similarity between analytical and numerical simulation predictions. At the end, there is a comparison with previously published works.

Ocean Wave Energy Control Using Aquila Optimization Technique

This paper presents ocean wave energy control using the Aquila optimization (AO) technique. An oscillating water column (OWC)-type wave energy converter has been considered that is fitted with a Wells turbine and doubly fed induction generator (DFIG). To achieve maximum power point tracking (MPPT), the rotor speed of the DFIG must be controlled as per the MPPT law. The MPPT law is designed in such a way that the Wells turbine flow coefficient remains within the threshold limit. It avoids the turbine from stalling which generates the maximum power. The MPPT law provides the reference rotor speed which is followed by the actual rotor speed. For this, a backstepping controller (BSC)-based rotational speed control strategy has been designed using the Lyapunov stability theory. The BSC has unknown control parameters which should be selected such that tracking errors are minimum. Hence, the objective of this work is to find the unknown control parameters using an optimization approach. The optimization approach of selecting BSC control parameters for an OWC plant has not been explored yet. To achieve this, an integral square error (ISE)-type fitness function has been defined and minimized using the AO technique. The results achieved using the AO technique have been compared with particle swarm optimization (PSO) and a genetic algorithm (GA), validating its superior performance. The rotor speed error maximum peak overshoot is least for AO-BSC as compared to PSO-BSC and GA-BSC. The fitness function value for AO comes out to be least among all the optimization methods applied. However, all tested methods provide satisfactory results in terms of turbine flow coefficient, rotor speed and output power. The approach paves the way for future research on ocean wave energy control.

Centralised fractional order LQI controller design for quadruple tank process — An optimisation approach

In this paper, a centralised Fractional Order Linear Quadratic Integral (FOLQI) controller is proposed for highly interacting Multi Input Multi Output (MIMO) system to reject the load disturbance conditions. An optimisation problem to minimise the control effort as an objective function is formulated in addition to meet the desired time domain inequality constraints. The parameters of the FOLQI controller are obtained by using sequential quadratic programming optimisation approach. For illustration, the quadruple tank process is considered as a case study which exhibits both minimum and non-minimum phase characteristics. To show the performance of the FOLQI controller, the simulation is performed under disturbance and parameter uncertainty conditions. The performance indices such as maximum peak overshoot, settling time and steady state error are computed for FOLQI controller. To show the superiority of FOLQI controller, these indices are compared with the responses obtained using the existing Integer Order Linear Quadratic Integral (IOLQI) and Linear Active Disturbance Rejection (LADR) controllers.

Improved Clamped Diode Based Z-Source Network for Three Phase Induction Motor

The 3Φ induction motor is a broadly used electric machine in industrial applications, which plays a vital role in industries because of having plenty of beneficial impacts like low cost and easiness but the problems like decrease in motor speed due to load, high consumption of current and high ripple occurrence of ripples have reduced its preferences. The ultimate objective of this study is to control change in motor speed due to load variations. An improved Trans Z Source Inverter (ΓZSI) with a clamping diode is employed to maintain constant input voltage, reduce ripples and voltage overshoot. To operate induction motor at rated speed, different controllers are used. The conventional Proportional-Integral (PI) controller suffers from high settling time and maximum peak overshoot. To overcome these limitations, Fractional Order Proportional Integral Derivative (FOPID) controller optimized by Gray Wolf Optimization (GWO) technique is employed to provide better performance by eliminating maximum peak overshoot problems. The proposed speed controller provides good dynamic response and controls the induction motor more effectively. The complete setup is implemented in MATLAB Simulation to verify the simulation results. The proposed approach provides optimal performance with high torque and speed along with less steady state error.

Harmonics Extraction Scheme for Power Quality Improvement Using Chbmli-Dstatcom Module

In recent day’s power distribution system is distress from acute power quality issues. In this work, for compensating Power Quality (PQ) disturbances a seven level cascaded H-bridge inverter is implemented in distribution static compensator which protects power quality problems in currents. Distribution Static Compensator (DSTATCOM) aid to enhances power factor and removes total harmonic distortion which is drawn from non-linear load. The D–Q reference theory based hysteresis current controller is employed to generate reference current for compensation of harmonics and reactive power, additionally Probabilistic Neural Network (PNN) classifier is used which easily separates exact harmonics. In the meantime fuzzy logic controller is also used to maintain capacitor DC-link potential. When comparing to PI controller it decreases steady state time and reduces maximum peak overshoot. Cascaded H-bridge multilevel inverter converts direct current to Alternating current, through inductor opposite harmonics are injected in Power Control Centre reduces source current harmonics and reactive power. The implementation of CHBMLI in distribution STATic COMpensator simulation model is simulated by means of MATLAB.

Model predictive control based autonomous DC microgrid integrated with solar photovoltaic system and composite energy storage

In this paper, a model predictive controller (MPC) is developed along with a simplified power management algorithm (PMA) for the autonomous DC microgrid. The autonomous DC microgrid includes a solar photovoltaic (SPV) unit integrated with composite energy storage (CES). The CES unit is composed of lithium-ion battery storage and supercapacitor storage units. Compared to conventional DC-microgrids with the SPV source alone, the integrated CES unit effectively handles the long-term and short-term power fluctuations due to the abrupt load variations and uncertainty in power generation by the SPV. This results in providing sustainable and reliable power supply to the remote accessing loads in rural electrification and improving the flexible operation of the microgrid. In addition, the integration of the supercapacitor with battery storage regulates the common dc-link voltage smoothly and is maintained at its reference value. Furthermore, with the proposed control strategy, the performance indices such as maximum peak overshoot, settling time, and steady-state error of the dc-link voltage have been improved as compared to the conventional control techniques. Besides that, the rate of change in battery current responds quickly to the step load variation. The efficacy and performance of the proposed system are verified and validated through MATLAB/Simulink and real-time simulation results using OPAL-RT OP4510 real-time simulator under various dynamic operating conditions.

LSTM Recurrent Neural Network-Based Frequency Control Enhancement of the Power System with Electric Vehicles and Demand Management

Due to the unpredictable and stochastic nature of renewables, current power networks confront operational issues as renewable energy sources are more widely used. Frequency stability of modern power systems has been considerably harmed by fast and unpredictable power variations generated by intermittent power generation sources and flexible loads. The main objective of the power system frequency control is to ensure the generation demand balance at all times. In reality, obtaining precise estimates of the imbalance of power in both transmission and distribution systems is challenging, especially when renewable energy penetration is high. Electric vehicles have become a viable tool to reduce the occasional impact of renewable energy sources engaged in frequency regulation mainly because of vehicle-to-grid technologies and the quick output power management of EV batteries. The rapid response of EVs enhances the effectiveness of the LFC system significantly. This research work investigates a deep learning strategy based on a long short-term memory recurrent neural network to identify active power fluctuations in real-time. The new approach assesses power fluctuations from a real-time observed frequency signal precisely and quickly. The observed power fluctuations can be used as a control reference, allowing automatic generation control to maintain better system frequency and ensure optimum generation cost with the use of demand management techniques. To validate the suggested method and compare it with several classical methods, a realistic model of the Indian power system integrated with distributed generation technology is used. The simulation results clearly indicate the importance of power fluctuation identification as well as the benefits of the proposed strategy. The results clearly show a considerable improvement in response performance indices, as the maximum peak overshoot was decreased by 21.25% to 51.2%, and settling time was lowered by about 23.34% to 65.40% for the suggested control technique compared to other controllers.

Operation of Grid-Connected PV-Battery-Wind Driven DFIG Based System

This article presents a photovoltaic (PV)-battery and wind driven doubly fed induction generator (DFIG) based grid-connected system with an improved multifunctional control scheme for grid-side converter (GSC). A three-stage improved reduced-order multiple integrator control is used to maintain the reactive power into the grid as well as it regulates the dc-link voltage across the GSC. The grid side control improves power quality in different abnormal conditions. Moreover, it behaves in such a way that it reduces the rise time, the maximum peak overshoot, as well as the settling time during the transients. The rotor side converter is used to provide the required amount of reactive power using the field-oriented control, for the wind power generator (WPG). A DFIG is used as a WPG. The single-stage PV array and a battery with bidirectional converter are connected to the common dc link of the GSC. The battery helps to extract the maximum wind power in light load conditions. The charging and discharging of the battery depend on the renewable energy generation and load demand. The dynamic behavior is improved by adding a PV feedforward term with the total active load current component. Here, the stator current total harmonic distortion (THD) and grid current THD are maintained as per the IEEE standard. Simulated and test results show the performance of the developed system in different dynamic conditions, such as load unbalancing, changes in PV insolation, and change in speed from the cut-in to cut-out speeds of the wind turbine. Moreover, these results show the battery behavior during different dynamic conditions.

Optimal Energy Management Scheme of Battery Supercapacitor-Based Bidirectional Converter for DC Microgrid Applications

Because of the splendid front of sustainable energy reassets in a DC Microgrid, it is profoundly willing to variances in energy age. A hybrid energy storage system (HESS) which includes a battery and a supercapacitor (SC) is used to decrease in-built fluctuations. The two different characteristics of the battery and supercapacitor make it a great match for HESS applications. The HESS is connected to the DC Microgrid through a bidirectional converter, which allows energy to be exchanged between the battery and supercapacitor. This paper discusses a converter presenting an approach for a double-input bidirectional converter. Related to this, a regulator was designed for use as a voltage regulation in a DC Microgrid. The designed controllers accelerated PV generation and load disturbance DC link voltage restoration, in addition to effective power balancing among the battery and the SC. The conventional PI, proposed PI, and predictive PI control techniques are effectively validated using MATLAB Simulink. Experimental findings with low power have been used to validate the operation of the predictive PI control technique. The DC grid voltage profile showed substantial improvement while using the predictive PI control in comparison with the proposed and conventional PI control techniques in terms of setting time and maximum peak overshoot.

Optimized cascade fractional‐order 3DOF‐controller for frequency regulation of a hybrid power system using marine predators algorithm

AbstractThis article investigates the competence of a cascade fractional‐order three‐degree‐of‐freedom (CC‐FO‐3DOF) controller for power‐frequency regulation of a hybrid wind‐diesel (hyWD) power system subjected to continuous load variation and unknown/uncertain wind power perturbation. The proposed cascade controller utilizes fractional‐order 3DOF‐PID and tilt‐integral‐derivative controllers as slave and master controllers, respectively, to ensure fast disturbance rejection and high damping of power‐frequency oscillations. A disturbance observer is designed to estimate system states and unknown/uncertain wind velocity. Subsequently, an improved control law is designed by augmenting the disturbance estimation, which ensures a higher degree of robustness of the closed‐loop system against disturbances. Marine Predators algorithm (MPA) is applied to search near‐optimum settings of the proposed controller, exercising an integral error‐based objective function. The effectiveness of the proposed controller is demonstrated by comparing results with the outputs of other prevalent controllers reported in the state‐of‐art. Extensive analysis of the presented results establishes the superiority of the MPA‐tuned CC‐FO‐3DOF controller in terms of maximum peak overshoot, frequency nadir, speed of response, and steady‐state accuracy. Finally, the robust closed‐loop stability of the investigated hyWD system has been affirmed applying Kharitonov's stability method, considering dynamic variation in system parameters.

A novel Green Leaf‐hopper Flame optimization algorithm for competent frequency regulation in hybrid microgrids

AbstractA proficient frequency regulation scheme has been explored in this work for two interconnected hybrid microgrids () comprised of multiple renewable energy sources, like solar‐thermal units, wind turbine generator units, solar photovoltaic, biogas turbine, biodiesel generator, and storage units. A new optimization algorithm has been formulated to replicate the photo‐tactic swarming behavior of green leafhoppers named Green Leaf‐hopper Flame optimization algorithm (GLFOA) to tune the system controllers (proportional integral (PI)/proportional derivative (PD)/proportional integral derivative (PID) controllers). The superiority of GLFOA is confirmed by comparing the performance with several popular optimization algorithms. To assess the stability of the several rigorous tests have been conducted considering availability/nonavailability of renewable resources, gusty wind, real‐time variation of solar radiation and wind velocities along with the random load disturbances. GLFOA tuned PID controllers were found to be the best to contain the frequency deviations. The maximum peak overshoot, undershoot, and settling time obtained by GLFOA tuned PID controllers are 0.01051 Hz, −0.00764 Hz, and 14.15 s, respectively, which are within an acceptable limit. Further, there is an improvement of 0.15% in the performance index obtained by GLFOA over its counterpart obtained by grey wolf optimization, confirming the superiority of the GLFOA tuned controller.

Steam Turbine Governor (STG) Controller Design Based on Particle Swarm Optimization (PSO)

A reheat and non-reheat steam turbines governor is a component of the turbine control system that regulates rotational speed in response to changing load conditions. The governor output signal manipulates the position of a steam inlet valve or nozzles which in turn regulates the steam flow to the turbine. The application of PID (proportional integral derivative (controller has been widely used from small industry to high technology industry. Tuning the parameters of a PID controller is very important in PID control, Ziegler-Nichols method also used to tune the coefficients of a PID controller. The compensation has been proposed to be 4%, 6%, 8%, 10% and 15% of the actual load.This paper evaluates the feasibility of using the Particle Swarm Optimization (PSO) method for determining the optimal PID controller parameters for steam turbine speed control. The PSO optimization technique is compared with Ziegler-Nichols method and (PID) controllers. It is validated that PSO based controller is more efficient in reducing the steady-states error, settling time, rising time, and overshooting limit in speed control of the steam turbine control. In which the maximum peak overshoot has been reduced from 44.8% in case of the original system without any controller to 3.45% for the case of PSO based controller and that for 15% load change.

An adaptive fractional fuzzy sliding mode controlled PSS for transient stability improvement under different system uncertainties

An adaptive fractional fuzzy sliding mode controller (AFFSMC) based power system stabilizer (PSS) is proposed for damping out low-frequency oscillations in single machine infinite bus and multimachine power systems under various operational contingencies. The proposed stabilizer is a combination of adaptive fuzzy control with a fractional order proportional integral derivative controller and sliding mode controller. A fractional sliding surface is adopted which enables the system to remain in the proposed robust sliding surface stably to eliminate the effects of chattering at any initial conditions. In addition, fractional adaptive rules based on fuzzy sets were used for optimal selection of parameters of the controller. Here, the stability issue is addressed through Lyapunov synthesis. Speed deviation and acceleration are considered as input signals to the proposed controller. A comparative analysis of performance indices such as settling time, maximum peak overshoot is presented to demonstrate the advantages of the proposed approach. In addition, stability analysis using Eigen value, integral time absolute error and shape of demerit were also presented to augment the stability study. The simulation results confirm that the proposed AFFSMC-PSS shows superior robust damping performance as compared to other approaches.

Performance analysis of boost converter with cascaded inner loop fuzzy SMC

PurposeThis paper intent to design, develop, and fabricate a robust cascaded controller based on the dual loop concept i.e. Fuzzy Sliding Mode concept in the inner loop and traditional Proportional Integral controller in the outer loop to reduce the unknown dynamics and disturbances that occur in the DC-DC Converter.Design/methodology/approachThe proposed Fuzzy sliding mode approach combines the merits of both SMC and Fuzzy logic control. FSMC approach reduces the chattering phenomena that commonly occurs in the sliding mode control and speed up the response of the controller.FindingsIn most of the research work, the inner current loop of cascaded controller was designed by sliding mode control. In this paper FSMC is proposed and its efficacy is confirmed with SMC -PI. In most uncertainties, FSMC-PI produces null maximum peak overshoot and a very less settling time of 0.0005 sec.Originality/valueThe presence of Fuzzy SMC in the inner loop ensure satisfactory response against all uncertainties such as steady state, circuit parameter variations and sudden line and load disturbances.

Act of nonlinear proportional derivative controller for MFC laminated shell

This manuscript presents connect a Proportional-Derivative (PD) controller with a macro-fiber composite (MFC) laminated shell system. Nonlinear equations of the MFC laminated shell have been established. The simultaneous resonance case considered as worst resonance. The main motivation of this research is applying a suitable vibration control to reduce or diminish the high vibration caused by the model. So, we used here PD controller in controlling the behavior MFC laminated shell structure. The goal of using the PD controller is to increase the system stability by improving control since it has an ability to predict the future error of the framework response. This control method makes stability increased, maximum peak overshoot decreased, settling time decreases. This controller is applied to recover the framework transient response. A second-order approximate solution has gained via applying multiple time scale perturbation (MTSP) technique. Every expected resonance case at this approximation order is extracted. After adding the PD-controller of the current framework, it eliminates the vibration amplitudes of the considered structure. Frequency (angular speed) response curves examined at special uncontrolled and controlled coefficients of the system. Finally, the conclusion shows a high efficiency of the measured control algorithm for eliminating vibrations of the modified system. A comparison between other approaches control algorithms had been prepared. The results are compared with the numerical simulations, and this shows a good verification for the approximate solution and for the control algorithm used in this manuscript. A comparison with earlier available papers is involved at the end.

Robust Control Design using Discrete Sliding Mode Control for Higher Order Uncertain Systems

This paper considers a tracking problem on discrete-time higher-order linear time-delay systems. The improved observer-model following sliding mode controller (OMF-SMC) is proposed. The combination uses a classical Luyenberger observer based controller to achieve predefined process output and sliding mode controller is added to assure the robustness despite of uncertainty and external disturbances. To show the effectiveness of proposed method, four error performance indices, maximum peak overshoot and settling time are considered rigorously. The simulations results on the non-oscillatory, moderate oscillatory, integrating, unstable and non-minimum phase system demonstrates that the proposed approach performs better compared with classical PID controller, continuous and discrete sliding mode controllers.