Settling Time Research Articles

Development of DC Motor Speed Control Using PID Based on Arduino and Matlab For Laboratory Trainer

DC motors are widely used as propulsions, including in electric bicycles. The problem faced by students in the DC motor control laboratory working using software simulation is that they do not have practical learning experience using digital instruments. This article aims to develop a DC motor speed control that can be used to learn practical Proportional Integral Derivative (PID) control in the laboratory. The DC motor speed control was developed using a combination of Arduino UNO microcontroller and Matlab software. The PID method was used because it is still broadly studied and applied in industries. The test results showed that the developed trainer can work well with PID variable values that can be entered via the keypad, and DC motor transient responses can be displayed in Matlab. From the experimental results, it was found that the optimal PID variable values were Kp=0.04, Ki=0.05, and Kd=0.004, where the controller produced a low overshoot value, i.e., 0.73% of its set point and a settling time of 10.66 seconds. The test results of using the developed trainer in the Fundamental of Control Engineering laboratory work showed that the developed trainer gave students practical learning experience in designing PID control for DC motor speed by using digital equipment, i.e., microcontroller and actual DC motor as well as analyzing its corresponding transient response in Matlab software environment

Transient Stability Enhancement Using Adaptive Neuro-Fuzzy-Based Unified Power Flow Controller

A power system is a complex, non-linear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia’s high-voltage transmission power grid in the North-West area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, an adaptive neuro-fuzzy-based unified power flow controller (ANFIS-based UPFC) is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The particle swarm optimization (PSO) algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. 70% of the retrieved data is used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, several disturbances such as three-phase with ground fault and single-phase with ground fault are used. When a three-phase with ground fault occurs at the mid-point of the Beles to Bahir Dar transmission line, for example, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement.

Prescribed time partial component lag consensus of multi-agent systems with switching topologies and external bounded disturbances

This paper investigates the problem of prescribed time partial component lag consensus (PTPCLC) and the problem of prescribed time partial component consensus (PTPCC) for nonlinear multi-agent systems (MASs) with switching topologies and external bounded disturbances. Some novel control protocols are designed by introducing a new time-varying function which depends on the prescribed settling time. Then, making full use of the permutation matrix, the original error system about the same agent is transformed into the new error system about the same component. Furthermore, the control parameters are designed according to the minimum eigenvalue of the Laplacian matrix and minimum non-zero values of the inner coupling matrix of the MASs to guarantee that the MASs can achieve PTPCLC and PTPCC, respectively. Finally, some simulation examples are given to verify the correctness of theoretical results.

Optimising coagulation/flocculation using response surface methodology and application of floc in biofertilisation

Abstract Aquaculture effluent generation, an environmentally crucial process requires effective treatment. Simple methods like coagulation and flocculation are considered effective. In this study, chitosan from Black tiger shrimp, Penaeus monodon, was used to remove ammonia (NH3) and turbidity. Response surface methodology-optimised dosages (5.00, 12.50, and 20.00 mg·L−1), pH (5.00, 6.25, and 7.50), and settling times (5.00, 7.50, and 30.00 min) were applied. Water spinach, Ipomea aquatica, received an application for organic fertiliser after the nutrient status of the recovered floc was later determined. Results showed that at a dose of 16.25 mg·L−1, a pH of 7.5, and a settling time of 17.5 min, chitosan cleared 92.16% of the turbidity. After 30 min of settling, 84.42% of the NH3 was eliminated at 5 mg·L−1 and 7.5 pH. Both macronutrients (P, K, N, Mg, and Ca) and micronutrients (Co, Cu, Fe, Mn, Ni, Se, and Zn) are present in the newly discovered floc. Floc had safe low Pb and Ni levels. Plants treated with floc showed superior growth compared to those with commercial liquid fertiliser, confirming nutrient absorbability. Conclusively, this study provides an eco-friendly wastewater treatment method.

Prescribed time formation synchronous tracking of unmanned aerial vehicles with denial of service attacks

This paper addresses the issue of robust prescribed time synchronous tracking control of unmanned aerial vehicles (UAVs) formation with denial of service attacks. A novel prescribed time synchronous tracking control scheme is proposed within a denial of service attack conversion mechanism. Firstly, on account of the UAV formation in the presence of aerodynamic damping and denial of service attacks, a formation cooperative control model is established under a second-order system. Secondly, to perform formation synchronous tracking within a settling time, a time-varying auxiliary function on account of synchronous state errors is designed for the convergence of prescribed time tracking. The control parameters are designed using a key eigenvalue scaling technique under the effect of denial of service attacks on network topology. The proposed scheme is also extended to UAV formation with a detailed-balanced graph. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy.

A hybrid optimization algorithm for improving load frequency control in interconnected power systems

The objective of this study is to develop an algorithm named Modified Artificial Bee Colony and Particle Swarm Optimization (MHABC-PSO) to address load frequency control (LFC) challenges in a two-area interconnected power system. The proposed MHABC-PSO algorithm is designed with two key modifications to enhance global exploration capability and improve convergence speed. Hence, a decision block is introduced in the employed bee (EB) phase incorporating a control parameter “limit” to allow each candidate solution (CS) to explore itself up to the “limit” value and boost local exploration. In addition, an introduction of a novel selection mechanism utilizing heuristic information (η) in EBs phase guides the onlooker bee (OB) phase to select better solutions based on success and failure history, thus promoting exploitation and reducing biased exploration. To address the efficacy of the proposed algorithm at the system level, three different two-area power systems are studied, incorporating various complexities, linearity, and non-linearity such as thermal-hydro, reheat thermal, and thermal-hydro-gas turbine configurations with HVDC link, SSSC, and CES. The algorithm is applied to optimize four objective functions (i.e. ITAE, IAE, ISAE, and ITE). The fitness function maximizes controller gains by utilizing the integral time multiplied absolute error (ITAE). Other objective functions like IAE, ISAE, and ITE are employed for a comprehensive analysis. Evaluation of MHABC-PSO effectiveness is conducted through ITAE values, peak deviations, and settling times of frequency and power deviations in different two-area systems. Results demonstrate that MHABC-PSO settles the system more quickly with zero steady-state error under step load perturbations (SLPs) of 1% and 2%. Comparative analysis with ABC, PSO, SFLA-TLBO, and OHABC-PSO using ITAE index and controller settling times shows the superiority of MHABC-PSO for LFC analysis. In conclusion, the proposed MHABC-PSO algorithm proves to be an efficient and effective solution for LFC, outperforming other algorithms in terms of the specified objective functions and exhibiting rapid convergence and optimal gains for controllers, effectively addressing LFC issues by combining exploration and exploitation techniques.

Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization

This research introduces a unified power quality conditioner (UPQC) that integrates solar photovoltaic (PV) system and battery energy systems (SBES) to address power quality (PQ) issues. The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. Moreover, the optimal choice of parameters for the adaptive neuro-fuzzy inference system (ANFIS) was achieved through the integration of the enhanced harmony search algorithm (EHSA) and the predator-prey-based firefly algorithm (PPFA) in the form of the hybrid metaheuristic algorithm (PPF-EHSA). In addition, the algorithm is employed to optimize the selection of resistance and inductance values for the filters in UPQC. The primary objective of the ANNC with predator-prey-based firefly algorithm and enhanced harmony search algorithm (PPF-EHSA) is to enhance the stability of the DC-link capacitor voltage (DLCV) with reduced settling time amid changes in load, solar irradiation (G), and temperature (T). Moreover, the algorithm seeks to achieve a reduction in total harmonic distortion (THD) and enhance power factor (PF). The method also focuses on mitigating fluctuations such as swell, harmonics, and sag and also unbalances at the grid voltage. The proposed approach is examined through four distinct cases involving various permutations of loads and sun irradiation (G). However, in order to demonstrate the performance of the suggested approach, a comparison is conducted with the ant colony and genetic algorithms, i.e., (ACA) (GA), as well as the standard methods of synchronous reference frame (SRF) and instantaneous active and reactive power theory (p-q). The results clearly demonstrate that the proposed method exhibits a reduced mean square error (MSE) of 0.02107 and a lower total harmonic distortion (THD) of 2.06% compared to alternative methods.

Optimized Proportional Integral Derivative Based Power System Stabilizer Using Jaya Algorithm for Angular Stability Enhancement

This study presents an optimized Proportional Integral Derivative Based Power System Stabilizer (PIDPSS) using Jaya Algorithm for angular stability enhancement. Jaya algorithm introduced by Rao, is an optimization technique with few control parameters which is used to minimize the objective function F(K). The modeling and simulation were done using Matlab /Simulink software version R2021b on IEEE 14-Bus system and Single Machine Infinite Bus (SMIB). A three-phase fault was introduced into the network at system runtime of 5s with a fault clearing time of 0.1s. The result of the simulation of the IEEE 14 Bus system showed a 74% and 24% reduction in overshoot time of speed deviation for generators 1 and 2, with settling times of 2.5s and 4s, respectively, in the presence of PIDPSS. The load angle experienced a 14% and 19% reduction in overshoot with settling times of 2s and 2.5s, respectively in the presence of PIDPSS for generators 1 and 2, respectively. The Electrical Power result showed 27% and 6% reduction in overshoot time as well as settling times of 2.5s and 4s, respectively, for generator 1 and 2 in the presence of PIDPSS. The result of the simulation of SMIB system also showed a 25% reduction in overshoot time in relation to deviation speed at a settling time of 4s in the presence of PIDPSS. The Load Angle showed- a 13% decrease in overshoot time at 2s settling time in the presence of PIDPSS. Also, the Electrical Power result highlighted a 15% drop-in overshoot time and settles within 2s. These results affirms that the PIDPSS introduced improved overall system stability.

Influence of Using ACO Algorithm in STATCOM Performance

Flexible alternating current transmission systems (FACTS) are technologies that rely on the use of high-power electronic equipment and advanced control methods to increase the ability of electrical transmission lines to transmit the largest amount of electrical power.Static_ synchronous _compensator (STATCOM) is one of FACTS_ family It has a rapid ability to supply or absorb reactive power from the system, traditional controller in STATCOM is (PID) or (PI). The controller is used to control the currents and voltages. In (STATCOM) there are two control loop, the inner loop which is represented by controlling the source current, and the outer loop which is used to control the voltage of the DC-link energy –storage- capacitor (vdc). Many algorithms are used in optimization to predict a specific behavior and to find the best solution for it. Two optimization methods were used Ant Colony Algorithm (ACO) and Genetic Algorithm (GA) to adjust the parameters of the outer PI controller. This unit is responsible for adjusting (vdc). The capacitor voltage is converted by the STATCOM inverter into a three-phase voltage, which is used to supply the load with required reactive power. Through the simulation and control process, the results demonstrated the effectiveness of the compensator in supplying the reactive power to the linear or non-linear loads, The results also showed that using the ACO technique that the integrated time absolute error ( ITAE) was reduced which gives more precise in adjusting the PI control parameters Which is achieved best Vd.c response ,compared with the GA and ziklur-nichlos method , by measuring the response of the lowest settling time (TS) and minimal peak overshoot (P-ov) .

Impact of stepwisely reducing settling time on the formation and performance of aerobic granular sludge

The impact of gradually reducing the settling time strategy on the formation, performance, and microbial succession of aerobic granular sludge (AGS) was investigated in a sequencing batch reactor (SBR). The results showed that the short settling time accelerated the granulation of AGS and the granulation was completed within 35 d. When the settling time was 10 min, the granular sludge had good settling performance and significant biological activity, and the average particle size and integrity coefficient increased to 1.017 mm and 93.3 %. The shortening of settling time stimulated bacteria to secrete more extracellular polymeric substances (EPS). Three-dimensional fluorescence spectrum analysis showed that aromatic proteins had high fluorescence intensity. According to the analysis of high-throughput sequencing, Rhodanobacter and Klebsiella were the dominant bacteria, and carbohydrate metabolism and amino acid metabolism were significantly enhanced, which may play an important role in maintaining the stability of AGS.

Effect of DC Link Capacitor on the Performance of Wind-Driven Double-Fed Induction Generator

In this paper, the starting performance of a double-fed induction generator (DFIG) operating at super-synchronous speed is investigated when varying the values of the DC link capacitor. The starting transients, the settling time, and the harmonics of the stator and rotor currents and voltages are investigated as a function of the DC link capacitor Cd. Curve fitting is applied to derive mathematical equations (polynomial) relating the variation of the stator current THD with the value of the DC capacitor. Similarly, polynomials are deduced to relate the THD of the rotor current and rotor voltage with the value of the DC link capacitor. These polynomials help to design the DC link parameters that lead to minimum current and voltage harmonics of the grid-connected DFIG. The minimum THD values of the stator current, rotor current, and rotor voltage are presented. These results are repeated at different rotor speeds to deduce the optimum Cd value within a wide speed range. Also, the effect of Cd on the DC link current and voltage ripples is demonstrated at different speeds.

Practical finite-time synchronization of delayed fuzzy cellular neural networks with fractional-order

The practical finite-time (PFT) synchronization of fractional-order delayed fuzzy cellular neural networks (FODFCNNs) is presented in this article. Initially, a useful practical finite time (FT) stable lemma is developed, serving as an efficient instrument for the PFT synchronization of fractional-order systems. Subsequently, a new PFT synchronization criterion for FODFCNNs is derived using the designed controller and the aforementioned lemma. Simultaneously, the settling time for PFT synchronization is determined, relying on specific controller parameters and the initial conditions of the considered systems. Ultimately, the accuracy of the derived outcomes is confirmed through a numerical simulation.

Tracking and Disturbance Suppression of the Radio Telescope Servo System Based on the Equivalent-Input-Disturbance Approach

This paper presents a composite control algorithm for the radio telescope servo control system to address the target tracking and matched/mismatched disturbance suppression problems. The algorithm consists of the equivalent-input-disturbance (EID) approach and the optimal control method. An EID estimator is developed using the difference between the estimated output of the state observer and the measurement output and then the estimate of the EID is fed forward into the control input to reject the disturbance. A cost function with clear physical meaning is selected and the weighting parameters are adjusted for the optimal controller to improve tracking performance. Considering the nonminimum phase characteristics of the radio telescope system, the state observer gain is computed using the linear matrix inequality (LMI) method. The system stability is analyzed using the small gain theorem, and the linear quadratic regulator (LQR) control method is utilized to determine the state feedback gain. Finally, the composite controller is designed for an identified telescope model. Simulation results show that for the tracking performance, the settling time of proposed method is 1.13 s and reduces by about 0.32 s and 0.87 s than that of the ADRC controller and PI controller, respectively. For the antidisturbance performance, the RMS value and the maximum error of the proposed method are 0.0039 and 0.0128, which are 42.86% and 40.38% of the ADRC controller and 30.71% and 27.77% of the PI controller, respectively, which indicates that the proposed method has better control performance. In addition, the proposed controller has certain robustness to systematic parameter perturbations.

Fixed-time synchronization of Inertial Cohen-Grossberg Neural Networks with state dependent delayed impulse control and its application to multi-image encryption

In this paper, we discussed about fixed-time synchronization (FXTS) of Inertial Cohen-Grossberg Neural Networks (ICGNNs) with state-dependent delayed impulses. The Lyapunov stability theory and several useful criteria are utilized to make sure that the control parameters are selected in sync with the intended settling time. Two types of the controller are developed in order to guarantee that error-delayed ICGNNs can be synchronized. A sufficient condition for ensuring FXTS for delayed ICGNNs with desynchronization impulses is investigated. In FXTS, the settling time of ICGNNs will have the smallest upper bound and the settling time of desynchronization will have the largest upper bound. We subsequently conducted numerical simulations to substantiate the validity of the proposed discoveries. Finally, we proposed a multi-image encryption algorithm with the help of ICGNNs and presented the statistical analysis to test its efficacy.

Advanced golden jackal optimization-based fractional-order integral terminal sliding-mode (FO-ITSM) controller for level control of the conical tank system

This article examines a conical tank nonlinear system's level control. A crucial component of facilities like milk processing plants and sugarcane mills is the conical tank. The fluid drains entirely due to the conical shape of the tank. The conical form of the system causes nonlinear dynamics. The conical tank nonlinear system transfer mechanism is not easily accessible, and different operating heights require different transfer mechanisms, making controller design difficult. Developing the Fractional-Order Integral Terminal Sliding-Mode (FO-ITSM) Controller system for Advanced Golden Jackal Optimisation (GJO) based conical tank systems is the main objective of this paper. This is utilised to deliver real-time liquid level management in non-linear conical systems. The dynamics of these models make it possible to identify the conical tank system that produces control signals from liquid samples taken at reference levels with greater accuracy. This system, which is employed on multiple platforms, maintains and controls the liquid level while meeting all design specifications, including time constant, no overshoot, less settling, and rise time. For research simulation work, MATLAB software's FOMCON toolbox is utilised.

A sequential excitation and simplified ant colony optimization based global extreme seeking control method for performance improvement

To date, it is lacking of an effective global extreme seeking control (GESC) approach to improve transient responses. Motivated by this engineering problem, a novel and general method of global ESC is proposed theoretically and technically in three steps: the step of a simplified ant colony optimization (SACO) algorithm for the task of global extreme seeking, the step of a sequential excitation (SE) algorithm for the improvement of both transient and steady state responses, and the step of discrete to continuous control conversion to control the real world dynamical systems. Both numerical simulations and physical experiments have been conducted to verify the effectiveness of the proposed controller with SE-SACO on the overall performance improvement in terms of transient responses, steady state responses and global extreme seeking. In comparison with the cutting-edge methods such as IABC-SHTS, SSA, BA and GA-ACO, the settling time and oscillations due to the proposed method are reduced significantly when keeping the global extreme seeking ability. It alleviates the conflict of global extreme seeking and good transient responses of the dynamical systems, an unsolved issue in the literature.

Experimental and Simulative Evaluation of a Reinforcement Learning Based Cold Gas Thrust Chamber Pressure Controller

At DLR neural networks, as potential future controller for rocket engines, are studied. A neural network-based chamber pressure controller for a simplified cold gas thruster is presented and analyzed in simulation and experiment. The goal of the controller is twofold: It can track a trajectory with different changes of setpoints and it allows to set and control a wide variety of steady state chamber pressures. The neural network gets feeding line pressure measurement data as input and calculates valve positions as output values. The training phase of the controller is done with a reinforcement learning algorithm in an EcosimPro/ESPSS simulation, that is validated with data from the corresponding experimental set up. To increase the robustness and to allow a transfer from the simulation directly to the test facility domain randomization is applied. The controller is evaluated in simulations and experiment. It was found that – in the range of physically possible operation points – the controller achieves a constantly high reward which corresponds to a low error and a good control performance. In the simulation the controller was able to adjust all required set points with a steady state error of less than 0.1bar while retaining a small overshoot and an optimal settling time. It is found that the controller is also able to regulate all desired set points in the real experiment. A reference trajectory with different steps, linear and sinus changes in target pressure is tested in simulation and experiment. The controller was in both cases able to successfully follow the given trajectory.

Implementation of ARM STM32F4 microcontroller for speed control of BLDC motor based on bat algorithm

Brushless direct current (BLDC) motor speed performance is an essential component that must be considered in the electric vehicle drive system. This research aims to obtain the transfer function from the mathematical modeling of the BLDC motor and optimization of the BLDC motor so that a high transient response is received. The method used in this study is to identify system parameters and then model them using the structure of the autoregressive exogenous (ARX) polynomial model. Meanwhile, to get a high transient response performance, optimization is done with a hybrid intelligent controller. The experimental findings indicate that the optimization process utilizing the bat method yielded the most favorable transient response. The resulting values for the proportional gain (Kp), integral gain (Ki), and derivative gain (Kd) were determined to be 31.2685, 7.7375, and -1.1934, respectively. In the current investigation, the minimum frequency value is 6381934.619. Also determined are the transient response characteristics, which include a rise time of 0.289 seconds, a settling time of 10.9 seconds, an overshoot of 3.4%, and a peak time of 1.04 seconds. Furthermore, the closed-loop system demonstrates stable behavior in terms of stability.

Dynamic Event-Triggered-Based Adaptive Finite-Time Neural Control for Active Suspension Systems With Displacement Constraint.

In this article, we give an event-based control algorithm for nonlinear active suspension systems (ASSs) with the vertical displacement constraint. For in-vehicle communication networks, the controller area network (CAN) is a widely used standard for interconnecting electronic control units (ECUs). The main task in this work is, thus, to reduce the communication burden on the CAN. To this end, we develop a dynamic event-triggered communication mechanism in ASSs. Meanwhile, in practice, the vehicle safety is degraded when the body vibration exceeds the allowable maximum. Thus, the vertical displacement of ASSs should be constrained within a reliable range. For this purpose, we present a novel finite-time integral barrier Lyapunov function (FTIBLF), which not only guarantees that the vertical displacement constraint bounds are not violated, but also enables the position of the suspension to be stabilized in the neighborhood of a desired position in finite settling time. Furthermore, neural networks (NNs) are utilized to identify the unknown nonlinear characteristics in ASSs subjected to the modeling error and unknown mass. As a result, we propose an adaptive neural control technique based on the dynamic event-triggered condition, which helps to improve the ride comfort while ensuring the driving safety and handling stability. Finally, simulation results are provided to verify the validity of the presented methodology.

Developing and Simulating a Model using Proportional Integral Derivative (PID) on Matlab/Simulink for Speed Control of DC Motor

This study uses MATLAB/SIMULINK to perform a thorough inquiry of the use of Proportional Integral Derivative (PID) control for accurate speed control of a DC motor. To increase steady-state error, decrease settling time, and improve transient response, the suggested model uses sophisticated PID tuning techniques. The efficacy of the PID controller in preserving the intended speed setpoints is demonstrated by means of comprehensive simulation experiments that assess its performance in a range of operational scenarios. This work creates a useful foundation for DC motor speed control system optimization in addition to offering insightful information on PID-based control techniques.