Integral Time Absolute Error Criterion Research Articles

Virtual synchronous generator based superconducting magnetic energy storage unit for load frequency control of micro-grid using African vulture optimization algorithm

An isolated microgrid has significant frequency stability issues due to the erratic nature of renewable energy sources, stochastic load behaviour, and low system inertia. Consequently, this paper presents a Virtual Synchronous Generator (VSG) based Superconducting Magnetic Energy Storage (SMES) unit for frequency stability enhancement of microgrid. A newly developed metaheuristic algorithm named African Vulture Optimization Algorithm (AVOA) is used to optimize the proportional-integral controller parameters with the help of the Integral Time Absolute Error criterion. The proposed Load Frequency Control (LFC) model of an isolated microgrid is evaluated against numerous scenarios and well-known algorithms like Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimization, and Harris Hawks Optimization. The MATLAB simulation results of the proposed system demonstrate a significant improvement in the dynamic response pertaining to maximum overshoot, settling duration, and frequency oscillations when contrasted to the other algorithms. Also, observed that the LFC system with VSG-based SMES unit improves the frequency response of 77.37 % and 86.56 % in peak overshoot and settling time respectively as compared to the system without energy storage units. Finally, real-time simulations are carried out in OPAL-RT (OP-4500) to validate the proposed system.

Voltage control for membrane capacitive de-ionization cell for higher energy efficiency in salt removal

Membrane Capacitive DeIonization (MCDI) cells have proven to be advantageous in water desalination and ions removal. Therefore, the time has come to introduce an alternative water purification technique to reduce the global water shortage. MCDI is known to be environmentally friendly, energy efficient and economical. Besides its reduced energy footprint, recent applications underline the regenerated energy during the desorption phase, which makes the MCDI as a potential cleaner energy source. Thus, a large number of scientific publications addressing problems and enhancing the performance of an MCDI have been published. In this paper, we have developed a simple and inexpensive method to control the adsorption voltage of the cells. So, the ion adsorption/desorption mechanisms of the MCDI will be controlled by a variable charging voltage applied to the cell.The entire response of controlled MCDI integrated model was created and he simulated results were compared with the experimental ones in order to validate the results. Accordingly, the controller parameters were tuned using the genetic algorithm optimization technique, based on the integral time absolute error criterion. Furthermore, the experimental results reveal that the control of the cell had increased the salt retention by 50%, the quantity of removed salt by the energy unit was improved by 10%, and the cell energy ratio from 28% to 32%.

Pressurized Water Reactor Core Power Control Using BAS-RBF-PID Approach During Transient Operation

A pressurized water reactor (PWR) is a system of several integrated components such as the core, steam generator, hot leg, cold leg, and plenums. The subsystems consist of critical parameters and malfunctions that cause potential accidents. Therefore, a PWR requires a control system for safe and stable operation over its lifetime. In this study, the state-space model of the PWR core is established and validated with published work. Then, a beetle antenna search (BAS) algorithm–optimized radial basis function (RBF) neural network proportional-integral-derivative (PID) control (BAS-RBF-PID) strategy is proposed to regulate the core power. The BAS-RBF-PID control approach computes the control input to optimize the PWR core output power to track the reference command. The integral absolute error and integral time absolute error criterion functions are used to measure the control performance. The sensitivity of the control input on the PWR output is examined through the Jacobian, and the stability is analyzed by using the Lyapunov approach and Nichols chart. The simulation results verified that the PWR core output power chased the reference command smoothly as compared with the BAS-PID and PID strategies with good performance. This confirms that the control signal optimizes the core power effectively. This study gives the benefit to apply the BAS-RBF-PID algorithm in other nuclear engineering fields for control purposes.

Marine predator algorithm based PD‐(1+PI) controller for frequency regulation in multi‐microgrid system

Renewable generation uncertainty, dynamic load change, and system parameter variation play a significant role in the performance degradation of non-linear multi-microgrid (MMG) systems. As a result, intelligent control becomes the need of the hour for assisting superlative attribute- based consistent electric power. The application of marine predator algorithm (MPA)-based cascaded PD-(1+PI) controller for Automatic Generation Control (AGC) of MMG system is a novel work. A maiden attempt of the MPA is proposed to optimize the parameters of the cascaded PD-(1+PI) controller using the integral time absolute error criterion. To demonstrate its superiority, the proposed algorithm is compared to the genetic algorithm, differential evolution, and grey wolf optimization. MPA then applied to conventional controller PID, cascaded PD-PI controller and proposed PD-(1+PI) controller for frequency control in multi-microgrid system. The robustness of the suggested controller is verified over PID and PD-PI controller by taking step and random load perturbation and integrating the renewable sources like solar and wind with their uncertain nature. The simulation of the investigated interconnected microgrid is carried out in MATLAB/SIMULINK environment. Finally, detailed simulation and hardware in the loop experimental results are presented to confirm the practicality of the proposed approach.

Gradient descent-particle swarm optimization based deep neural network predictive control of pressurized water reactor power

A pressurized water reactor (PWR) is an integrated system of various interdependent subsystems that show highly nonlinear behavior, and each component is prone to uncertainties and malfunctions that initiate potential accidents. In contrast, PWR needs to be continuously monitored for stable and safe operation over a service time. In this study, a gradient descent-particle swarm optimization hybrid algorithm-based deep neural network (GD-PSO-based DNN) approach is proposed to monitor the PWR core power and outlet temperature. The Gaussian noise is introduced to the input signal, and the PWR core stability conditions are examined by using Lyapunov analysis. The simulation results verified under different conditions, in which the proposed control approach tracks the reference inputs successfully and enhances the stability as compared with the sliding mode control, linear quadratic regulator, and proportional-integral-derivative methods. The controller is validated by using the data from RELAP5 system codes, and the performance is examined using the mean square error, integral square error, integral absolute error, integral time square error, and integral time absolute error criterion functions. This study gives the benefit to apply the GD-PSO-based DNN control method for other nuclear engineering fields for system identification, prediction, and control.

PID Controllers Design for a Quadrotor System Using Teaching Learning Based Optimization

Optimized Proportional Integral Derivative controllers are designed to control the translational and rotational motions of a quadrotor system with six degrees of freedom. The teaching learning based optimization algorithm is used to obtain the proportional, integral and derivative gains of six PID controllers so that the integral time absolute error criterion is minimized. The control objective, is to enforce the horizontal position, altitude and yaw angle of the quadrotor to track their desired reference trajectories while stabilizing its roll and pitch angles. The efficiency and the control performance of the proposed scheme are demonstrated through numerical simulation and compared with those of the PID controllers designed using genetic algorithm, the sliding mode control and other control techniques proposed in the literature. The simulation study shows the good performance of the proposed control scheme in terms of transient response characteristics, tracking accuracy and disturbance rejection.

Optimization of sliding mode control with PID surface for robot manipulator by Evolutionary Algorithms

AbstractIn this paper, a sliding mode controller (SMC) with PID surface is designed for the trajectory tracking control of a robot manipulator using different optimization algorithms such as, Antlion Optimization Algorithm (ALO) Sine Cosine Algorithm (SCA) Grey Wolf Optimizer (GWO) and Whale Optimizer Algorithm (WOA). The aim of this work is to introduce a novel SMC-PID-ALO to control nonlinear systems, especially the position of two of the joints of a 2DOF robot manipulator. The basic idea is to determinate four optimal parameters (Kp, Ki, Kd and lamda) ensuring the best performance of a robot manipulator system, minimizing the integral time absolute error criterion (ITAE) and the integral time square error criterion (ISTE). The robot manipulator is modeled in Simulink and the control is implemented using the MATLAB environment. The obtained simulation results prove the robustness of ALO in comparison with other algorithms.

An Integral Tilt Derivative Control Strategy for Frequency Control in Multimicrogrid System

The multimicrogrid system is a complicated nonlinear system, which brings performance degradation due to deficient damping under the unexpected fluctuation in power generation due to the presence of renewable sources, dynamically changing loading conditions, and parameter variations. Owing to this, to provide consistent electric power with superlative attribute, sturdy and intelligent control techniques are amazingly imperative in the automatic generation control of microgrid (MG). The application of imperialist competitive algorithm (ICA)-based fractional-order integral proportional derivative with filter (IPDF), i.e., integral tilt derivative with filter controller (ITDF) controller in frequency control in two areas interconnected MG (isolation mode) with renewable penetration, is a novel work. A maiden attempt of the ICA is proposed to optimize the gains of the ITDF controller utilizing the integral time absolute error criterion. To demonstrate the supremacy of ICA, its outcomes are contrasted with two existing optimization strategies, namely the genetic algorithm and the particle swarm optimization. The effectiveness of the proposed controller is revealed by contrasting the dynamic responses of multi microgrid (MMG) with proportional integral derivative with filter (PIDF) and tilt integral derivative with filter (TIDF) controllers. At last, a sensitivity investigation is performed to exhibit the power of the studied strategy to wide variations in the MG parameters, magnitude as well as the location of step/random load disturbance. The proposed MMG is simulated in MATLAB/Simulink environment.

Investigation and performance comparison of ride comfort on the created human vehicle road integrated model adopting genetic algorithm optimized proportional integral derivative control technique

This context exhaustively investigates the ride comfort performance index on the proposed active suspension vehicle system. Ride comfort in terms of occupants (includes driver and passenger) head acceleration, sprung mass vertical and pitching accelerations is considered. For this examination, a 14-degree-of-freedom human vehicle road integrated system model was extensively developed. Then, an active suspension system composed of a hydraulic actuator and proportional-integral-derivative controller is incorporated into the developed vehicle model to enhance the ride comfort. Besides, the designed controller needs to satisfy other vehicle performance indices like vehicle stability and ride safety. Accordingly, the controller parameters were optimally tunned with the help of genetic algorithm technique, on the basis of integral time absolute error criterion. The objective function was created on the basis of minimizing the integral time absolute error of sprung mass displacement, suspension working space and tire deflection responses. The entire response of human vehicle road integrated model, with the proposed active suspension system and passive suspension system on various random road surfaces (A, B, C, D and E with respect to ISO 8608) with five constant speeds (20, 40, 60, 80 and 100 kmph), was compared via surficial presentation. Furthermore, the comfort measures such as root mean square and vibration dose value from ISO 2631-1 were adopted to evaluate the severity between the occupants via head acceleration response. The simulation results showed that the suggested active suspension system significantly improved the ride comfort with guaranteed vehicle stability and ride safety.

Robust fractional PID controller synthesis approach for the permanent magnetic synchronous motor

In this work, we propose the synthesis method of different robust fractional order PID controllers, which are synthesized from solving the constrained optimization algorithm for the permanent magnet synchronous motor (PMSM) speed control problem. This problem is formulated through the time and frequency-domain specifications where the obtained fractional controller should simultaneously satisfy some conflicted goals, such as good tracking dynamic behavior of the imposed set-point reference, good attenuation of the plant uncertainties, good suppression of the sensor noise effect and ensure an enough trade-off between the nominal performances and the robust stability of the feedback control system. The previous properties are satisfied not only for the nominal plant-model used in synthesis controller step, but also for a set of the neighboring plant uncertainties. Moreover, the integral time absolute error criterion should be minimized to satisfy the imposed time-domain requirements. However, the weighted-mixed sensitivity problem based upon the different proposed adjustable weights is minimized to ensure the imposed frequency-domain specifications where the load disturbances, sensor noises and the neglected nonlinear and fast PMSM dynamics are considered. The PMSM speed drive is controlled where its dynamic behavior is modeled by the unstructured-multiplicative uncertainty. The obtained simulation results are compared in time and frequency domains by those given with the conventional robust \(\mathcal {H}_\infty\) controller to demonstrate the effectiveness of the proposed fractional controllers.

Improving the pneumatic control valve performance using a PID controller

Pneumatic control valves are still the most used in process industries due to their low cost and simplicity. This paper presents a design procedure of a PID controller for a pneumatic control valve. For comparison, P and PI controllers are also utilized for the control valve. The bond graph method is used to model the control valve, in order to compare the response characteristics of the valve. Simulation results are found for three controllers of the valve. The integral time absolute error criterion is used to calculate the controller parameters. The results show excellent closed-loop performance and the PID controller is more robust than the P and PI controller.

An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process

This paper shows a strategy for the optimal tuning of a fuzzy controller in a networked control system using an offline simulated annealing approach. The optimal tuning of the fuzzy controller using a maximum known delay is based on the integral time absolute error ( ITAE) performance index. The goal is to obtain the optimal tuning parameters for the input scaling factors where the ITAE performance index is minimized. In this study, a step change in the force reference signal is considered a disturbance, and the goal is to assess how well the system follows set-point changes using the ITAE criterion. In order to improve the efficiency of high-performance drilling processes while preserving tool life, the current study focuses on the design and implementation of an optimal fuzzy-control system for drilling force. Simulation results demonstrate good convergence properties of the proposed strategy. Experimental tests of the drilling of two materials (GGG40 and 17-4 PH) corroborate the excellent transient response and the minimum overshoot predicted by the simulation results. Thus, the optimal fuzzy control system reduces the influence of the increase in cutting force that occurs at larger drill depths, eliminating the risk of rapid drill wear and catastrophic drill breakage.

Networked Fuzzy Control System for a High-Performance Drilling Process

The high-performance drilling (HPD) process has a significant impact on production in many industries, such as the automotive, die/mold and aerospace industries. However, cutting conditions for drilling are generally chosen from a machining-data handbook, requiring operator experience and skill. In order to improve drilling efficiency while preserving tool life, the current study focuses on the design and implementation of a simple, optimal fuzzy-control system for drilling force. The main topic of this study is the design and implementation of a networked fuzzy controller. The control system consists of a two-input (force error and change of error), single-output (feed-rate increment) fuzzy controller with nine control rules, the sup-product compositional operator for the compositional rule of inference, and the center of area as the defuzzification method. The control algorithm is connected to the process through a multipoint interface (MPI) bus, a proprietary programming, and communication interface for peer-to-peer networking that resembles the PROFIBUS protocol. The output (i.e., feed-rate) signal is transmitted through the MPI; therefore, network-induced delay is unavoidable. The optimal tuning of the fuzzy controller using a maximum known delay is based on the integral time absolute error (ITAE) criterion. The goal is to obtain the optimal tuning parameters for the input scaling factors while minimizing the ITAE performance index. In this study, a step in the force reference signal is considered a disturbance, and the goal is to assess how well the system follows set-point changes using the ITAE criterion. The optimization is performed using the Nelder–Mead simplex (direct search) method. The main advantage of the approach presented herein is the design of a simple fuzzy controller using a known maximum allowable delay to deal with uncertainties and nonlinearities in the drilling process and delays in the network-based application. The results demonstrate that the proposed control strategy provides an excellent transient response without overshoot and a slightly higher drilling time than the CNC working alone (uncontrolled). A major issue in high performance drilling is the increase in cutting force and torque that occurs as the drill depth increases. Therefore, the fuzzy-control system reduces the influence of these factors, thus eliminating the risk of rapid drill wear and catastrophic drill breakage.