Terms Of Overshoot Research Articles

SSA-based Fractional Order PI-PD Cascade Controller Design for DC Motor Speed Control

Bu çalışmada doğru akım (DA) motoru hız kontrolünü sağlamak için eklemeli ve kesir dereceli (FOPI-FOPD) denetleyici tasarımı önerilmiştir. Önerilen denetleyici parametreleri son yıllarda geliştirilen Serçe Arama Algoritması (SAA) ile optimize edilmiştir. Denetleyici parametrelerinin aranması için çeşitli amaç fonksiyonları kullanılmıştır. Bu amaç fonksiyonları zamanla çarpılan mutlak hatanın integrali (ITAE), mutlak hatanın integrali (IAE), zamanla çarpılan hatanın karesinin integrali (ITSE), hatanın karesinin integrali (ISE) ve Zwe-Lee Gaing (ZLG) fonksiyonudur. Zaman bölgesinde aşma (M_p), oturma süresi (t_s) ve yükselme süresi (t_r) bakımından kullanılan amaç fonksiyonlarından elde edilen sonuçlar karşılaştırılmıştır. Ayrıca elde edilen sonuçlar frekans bölgesinde kazanç marjı, faz marjı ve bant genişliği açısından incelenmiştir. Zaman ve frekans bölgesinde farklı amaç fonksiyonlarından elde edilen en iyi sonuç literatürdeki çalışmalarla karşılaştırılmıştır. Ayrıca önerilen denetleyicide elde edilen en iyi sonuç için gürbüzlük, bozucu yük ve referans hız değişimi analizleri incelenmiştir.

Thermal management inside a discretely heated rectangular cuboid using P, PI and PID controllers

This study investigates the viability of three different continuous (P, PI, and PID) controllers to meet specific thermal requirements at a desired location in a cooling system with discrete heat sources. The system is a rectangular cuboid with three discrete heat sources placed on the bottom surface at periodic intervals, and the rest of the walls are insulated. A temperature probe is installed in the system's center to monitor the temperature and provide feedback to the flow controllers in a continuous manner. The velocity of air entering from the inlet port varies in response to the controller feedback and discharge through the outlet port at atmospheric condition. The Galerkin finite element technique solves the governing Navier-Stokes and energy equations and the appropriate initial and boundary conditions. The simulations consist of testing the system's response at the probe point using different combinations of proportional (P), integral (I), and derivative (D) controllers with varied gains to analyze and compare the system's steady-state error and transient behavior in terms of overshoot, oscillation, and settling time. The results indicate that the P controller cannot eliminate steady-state error, while the PI controller achieves zero steady-state error and faster settling. However, if appropriately tuned, the PID controller enhances oscillation control and response time.

Genetic Algorithm based PID Tuning Software Design and Implementation for a DC Motor Control System

This study presents the software and implementation for proportional-integral-derivative (PID) tuning of a DC motor control system using genetic algorithm (GA). The PID parameters for a specific control structure are optimized using GA in the proposed tuning procedure. Also, integral time absolute error (ITAE) is used as a fitness function to optimize the parameters. The robustness of the control system is compared with conventional mathematical method. Simulations are carried out in MATLAB/Simulink to compare the results of a DC motor control system. Simulation results show that in terms of overshoot, steady-state error, and settling time, GA-based PID tuning approach performed better than conventional method. Additionally, a sensitivity analysis is performed to evaluate how robust the proposed approach is to parameter variations. The analysis shows that compared to the conventional method, the GA-based PID tuning algorithm is more adaptable to variations in system parameters.

Dynamic ensemble multi-strategy based bald eagle search optimization algorithm: A controller parameters tuning approach

To address the problems of bald eagle search algorithm (BES), easy fall into local optimums, limited diversity and slow convergence, a dynamic ensemble multi-strategy bald eagle search (DMBES) algorithm is proposed. Firstly, a nonlinear control factor is constructed to explore the potential range of population search, which helps to explore potential solutions. Next, a Levy flight strategy is introduced to better smooth the transition and explore the solution space through individual information interaction. Then, a dynamic selection strategy is proposed to help individuals obtain optimal solutions and better balance the algorithm's exploration and exploitation capabilities. Finally, in numerical experiments, classical benchmark functions and CEC 2022 test functions are selected to compare the performance of DMBES with other reliable and recent algorithms. In addition, box plots, Wilcoxon signed-rank test and Friedman rank test are used to confirm the significance of the results. In the controller parameter tuning experiments, the results show that the optimized controller of the DMBES algorithm outperforms the six comparison algorithms in terms of overshoot and rise time.

Tunicate Swarm Algorithm based Optimized PID controller for automatic generation control of two area hybrid power system

In the modern power system, the use of renewable energy sources is increasing rapidly, which makes the system more sensitive. Therefore, it requires effective controllers to operate within the allowable ranges. The existing techniques based on cascaded controllers implemented so far for load frequency control have the advantage of improving the system response. However, this makes the system a more complex and time-consuming process. This makes the system more straightforward, makes it easy to optimize PID parameters, and provides results in acceptable ranges. This paper attempts to solve the load frequency control (LFC) problem in an interconnected hybrid power system with a classical PID controller employing the tunicate swarm algorithm (TSA). This algorithm is used for two areas of an interconnected hybrid power system: thermal, hydro, nuclear, and wind. The PID controller parameters are optimized by tunicate swarm algorithm using integral time absolute error (ITAE) based objective function. To show the robustness of the proposed TSA algorithm, a sensitivity analysis is performed for four case studies ranging from 20% to 30% load increments and decrements. The performance of the proposed TSA algorithm has been compared with the well-known optimization algorithms, particle swarm optimization (PSO), artificial bee colony (ABC), and arithmetic optimization algorithm (AOA) in terms of overshoot, undershoot, and settling time. The simulation results show that the proposed TSA has better optimization capability than PSO, ABC, and AOA in terms of overshoot, undershoot, and settling time.

Fractional Order Internal Model PID Control for Pulp Batch Cooking Process

Pulp batch cooking is performed in a sealed high-temperature and high-pressure digester and is a complex black-box process. Based on the analysis of the mechanism of batch cooking, a model with nonlinear characteristics was established. To optimize the nonlinear part of the model, function fitting and numerical approximation were used for the simple cooking process. A novel fractional-order model is proposed to solve the problems of model inaccuracy and uncertain system parameters during batch cooking. For fractional-order systems, a two-degree-of-freedom fractional-order PID (FO-PID) controller based on internal model control is designed. The internal model control simplifies the number of parameter settings of the fractional-order controller and allows accurate adjustment of the adjustable parameters of the FO-PID controller based on the maximum sensitivity and stability margin. The simulation results show that the fractional-order internal model control in the batch cooking process is superior to the integer-order control in terms of overshoot, response speed, and robustness. Compared to internal model control, it provides better setpoint tracking dynamics and better robustness to system parameter disturbances. The practical application results in paper mills are given to illustrate the effectiveness of the method.

Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle

In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and heading angle of autonomous underwater vehicle. The neural network is trained using the back-propagation algorithm. In the presence of noise and parameter variation, the proposed adaptive controller’s performance is compared with that of the self-tuning fuzzy-PID and fuzzy logic controller. Simulations are conducted to obtain the performance of both controller models in terms of overshoot, and the rise time and the result of the proposed adaptive controller exhibit superior control performance and can eliminate the effect of uncertainty.

Experimental Design of Fast Terminal Sliding Mode Control for Valve Regulation under Water Load Uncertainty for Precision Irrigation

The application of control systems in precision irrigation is critical to ensure the accurate distribution of water in crops under various uncertainties. Shifts in the loading of the water supply on the control valve can be a significant uncertainty. Changes in weather and the uncertainty of the water level in the reservoir are also challenging issues. Sliding Mode Control (SMC) is a robust control technique that is simple to apply to deal with uncertainty, while Fast Terminal Sliding Mode Control (FTSMC) has the benefit of the rapid convergence. The DC electric motor, which is a common component of electric control valves, can be employed in designing control techniques for precision irrigation applications. This study aims to design a proposed experimental-based method, namely FTSMC for valve regulation under water load uncertainty for precision irrigation application. Modification of the signum function should be used to eliminate the chattering effect in real experiments.The results of experiments showed that the proposed method was superior to the conventional Proportional Integral Derivative (PID) and traditional SMC techniques in terms of overshoot, convergence rate and error. Because of those reasons, the FTSMC approach should be implemented on control valves against load uncertainty in precision irrigation applications.

Thrust Vector Controller Comparison for a Finless Rocket

The paper focuses on comparing applicability, tuning, and performance of different controllers implemented and tested on a finless rocket during its boost phase. The objective was to evaluate the advantages and disadvantages of each controller, such that the most appropriate one would then be developed and implemented in real-time in the finless rocket. The compared controllers were Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and Proportional Integral Derivative (PID). To control the attitude of the rocket, emphasis is given to the Thrust Vector Control (TVC) component (sub-system) through the gimballing of the rocket engine. The launcher is commanded through the control input thrust gimbal angle δ, while the output parameter is expressed in terms of the pitch angle θ. After deriving a linearized state–space model, rocket stability is addressed before controller implementation and testing. The comparative study showed that both LQR and LQG track pitch angle changes rapidly, thus providing efficient closed-loop dynamic tracking. Tuning of the LQR controller, through the Q and R weighting matrices, illustrates how variations directly affect performance of the closed-loop system by varying the values of the feedback gain (K). The LQG controller provides a more realistic profile because, in general, not all variables are measurable and available for feedback. However, disturbances affecting the system are better handled and reduced with the PID controller, thus overcoming steady-state errors due to aerodynamic and model uncertainty. Overall controller performance is evaluated in terms of overshoot, settling and rise time, and steady-state error.

Improved Dynamic Performance of the Fuel Cell-Fed Boost Converter Using Super Twisting Sliding Mode Control Strategy

Nonlinear controllers are useful if the equations governing the system dynamics are nonlinear. The fuel cell (FC)-fed boost converter is a nonlinear system as the VI characteristics of the fuel cell and the output voltage to duty cycle transfer function of the boost converter are nonlinear. The choice of a nonlinear controller depends on the dynamic performance of the FC-fed boost converter for wide load variations. This paper compares the performance of the system using conventional sliding mode control (CSMC) and super twisting sliding mode control (STSMC) strategies for tight voltage regulation for the load disturbance and reference voltage tracking. Simulation and experimental results of the fuel cell-fed 600 W boost converter prove the supremacy of STSMC strategy over CSMC strategy in terms of overshoot, undershoot, settling time and chattering reduction.

The Development of an Energy Efficient Temperature Controller for Residential Use and Its Generalization Based on LSTM.

Thermostats operate alongside intelligent home automation systems for ensuring both the comfort of the occupants as well as the responsible use of energy. The effectiveness of such solutions relies on the ability of the adopted control methodology to respond to changes in the surrounding environment. In this regard, process disturbances such as severe wind or fluctuating ambient temperatures must be taken into account. The present paper proposes a new approach for estimating the heat transfer of residential buildings by employing a lumped parameter thermal analysis model. Various control strategies are adopted and tuned into a virtual environment. The knowledge gained is generalized by means of a long short-term memory (LSTM) neural network. Laboratory scale experiments are provided to prove the given concepts. The results achieved highlight the efficiency of the implemented temperature controller in terms of overshoot and energy consumption.

Online and Real-Time Trajectory Generation Method for Unforeseen Events Using a Modified Spline Approach

This letter focuses on the problem of online and instantaneous generation of smooth trajectories and their analytical time derivatives, where future reference signals provided by a trajectory planner or a user are a-priori unknown. For this purpose, we propose a modified cubic spline polynomial which transforms the instantaneous piecewise references into smooth and continuous functions for digital control systems. The resulting reference signals are not only smooth and continuous in time but also analytically differentiable. The inferred derivative values could then be used in a variety of linear and non-linear controllers such as, Proportional-Integral-Derivative (PID), sliding mode, and backstepping. We also provide optimum and boundary values for the parameters used in the proposed technique. In addition, we present a lightweight implementation (a few lines of code) of the algorithm by which real-time computations are rapidly performed using a microcontroller. Both simulation and real experiments demonstrate that for the plants having different degrees of complexities, the proposed approach with a PD / PID controller outperforms the conventional filtered derivative-based one in terms of overshoot in the step response, robustness against noise, and trajectory tracking error.

Dynamic voltage restorer based on particle swarm optimization algorithm and adaptive neuro-fuzzy inference system

This article uses a dynamic voltage restorer to tackle a wide range of power quality issues, such as voltage drooping and swelling, spikes, distortions, and so on. The proportional controller, integrated controller (PI), and adaptive neuro-fuzzy inference system (ANFIS) are proposed dynamic voltage restorer (DVR) controllers. The control strategy's goal is to employ an injection transformer to mitigate for the needed voltage and keep the load voltage fixed. The settings of the PI controller are fine-tuned using two methods: trial and error and intelligent optimum. Particle swarm optimization (PSO) is now the most effective method. In terms of settling time, overshoot, undershoot, and disturbances around the final value, the PSO-tuned PI controller outperforms the trial-and-error PI controller. The ANFIS controller is used to regulate the DVR's responsiveness through the PI-PSO controller. The PI-PSO data is used as training data by the ANFIS controller. The results show that a DVR with an ANFIS controller outperforms a PI-PSO controller in terms of overshoot, undershoot spike voltage, steady state time, and settling time. In the case of a failure voltage, the DVR with an ANFIS controller has a 27% undershoot spike voltage while the PI-PSO controller has a 30% undershoot spike voltage.

A Fractional Order Super Twisting Sliding Mode Controller for Energy Management in Smart Microgrid Using Dynamic Pricing Approach

A real-time energy management strategy using dynamic pricing mechanism by deploying a fractional order super twisting sliding mode controller (FOSTSMC) is proposed for correspondence between energy users and providers. This framework, which controls the energy demand of the smart grid’s users is managed by the pricing signal provided by the FOSTSMC, issued to the smart meters, and adjusts the users’ demand to remove the difference between energy demand and generation. For the implementation purpose, a scenario based in MATLAB/Simulink is constructed where a sample renewable energy–integrated smart microgrid is considered. For the validation of the framework, the results of FOSTSMC are compared with the benchmark PI controller’s response. The results of the benchmark PI controller are firstly compared in step response analysis, which is followed by the comparison in deploying in renewable energy–integrated smart grid scenario with multiple users. The results indicate that the FOSTSMC-based controller strategy outperformed the existing PI controller-based strategy in terms of overshoot, energy balance, and energy price regulation.

A hybrid technique to control superheater steam temperature in power plants using multi modeling and predictive sliding mode control

One of the most critical variables to control in a steam power plant is superheater steam temperature. It must be carefully managed within a narrow temperature range; too high temperatures will cause material degradation. Temperature variation impacts the plant's efficiency, causing unplanned disturbances, because to frequent and intense load fluctuations and stringent requirement for performance and safety. To control the temperature level and maintain stability, the proposed hybrid approach is applied with MM-PSMC in this research. Fuzzy Logic Controller (FLC) and Golden Eagle Optimization [GEO] are combined in the suggested hybrid approach. Using the GEO technique, the FLC aims to increase control quality and identify the optimum value. This technique enables the superheater to attain the best-fit settings for the steam temperature plant while also increasing the plant's stability. The primary goal of this paper is to lower the IAE index at the superheater temperature plant. Finally, the MATLAB/Simulink platform provides steady-state values of the control levels of the superheater steam temperature under various scenarios. The suggested technique is compared to existing methods such as RNN with EHO, RNN with PSO, and ANN. Simulation results shows the proposed method gives good results Maximum 160 MW of power is generated and when the set value intelligent optimization compensation method is employed, the SST's control quality is dramatically improved—by 90 % when compared to the initial control—in terms of overshoot, control error, and relation to risk management. The proposed method controls the temperature at 535°C compared to existing methods.

Image Based Visual Servoing for Floating Base Mobile Manipulator Systems with Prescribed Performance under Operational Constraints

This paper presents a novel Image-Based Visual Servoing (IBVS) control approach for Floating Base Mobile Manipulator Systems (FBMMSs) that imposes prescribed transient and steady-state response on the image feature coordinate errors while satisfying the visibility constraints that arise owing to the camera’s limited field of view. The proposed control strategy does not incorporate any knowledge on either the FBMMS dynamic model, the exogenous disturbances, or the inevitable camera calibration and depth measurement errors. More specifically, it guarantees: (i) predefined behavior in terms of overshoot, convergence rate, and maximum steady-state error value of the image features and system velocities tracking errors; (ii) satisfaction of camera field of view constraints; (iii) bounded closed-loop control signals, and (iv) reduced design and implementation complexity. Additionally, the performance of the developed scheme is solely determined by certain designer-specified performance functions/parameters, and it is fully decoupled by the control gains selection. The efficiency of the proposed scheme is demonstrated via a realistic simulation study, using an eye-in-hand Underwater Vehicle Manipulator System (UVMS) as a test-bed FBMMS platform.

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Considering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. This method can make the motor load system reach a steady state faster than simple PID control. But RSC-PID is also unsatisfactory in terms of overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. The simulation results show that RSC-EPID control improves the tracking effect of the output signal of the motor load system, ensures the stability of the system, and improves the performance of the system.

New algorithm based active method to eliminate stick-slip vibrations in drill string systems

The drill string is always exposed to various types of vibrations among which, stick slip is one of the most important types. It is a severe state of torsional vibrations. This phenomenon can decrease the rate of penetration of drilling, wear of expensive equipment prematurely and cause catastrophic failures. In this paper, a novel adaptive sliding mode (SM) controller is proposed to eliminate stick slip in drill string systems. This proposed algorithm has a more robust capacity than existing 1st-order SM schemes in the literature regarding the robustness to parametric uncertainties, variations in weight on bit (WOB), variations in reference velocity and measurement noise. Moreover, the proposed controller does not require a priori knowledge of the upper bounds of parametric uncertainties, external disturbances and can be easily applied for any operating mode of the drill rig. A proof of stability based on the Lyapunov criterion of the system is given. Simulation results show that the proposed algorithm suppresses the stick-slip while keeping good performances compared to other SM controllers. A comparative study between the proposed controller and classic SM controllers and other adaptive SM scheme is performed in order to assess the advantages of the proposed algorithm and illustrate the overall performance improvements. The obtained results show that the proposed controller succeeded to eliminate the stick-slip phenomenon with the best performance compared to the classic SM controllers. In fact, the proposed controller presented a reduction of nearly 26 in terms of overshoot and 1.6 times better settling time values while having the smoother input signal.

Design of proportional integral and derivative controller using particle swarm optimization technique for gimbal system

<span>This paper presents the development of an optimal <a name="_Hlk98139022"></a>proportional, integral and derivative (PID) controller for controlling camera gimbal on unmanned aerial systems (UAV). Three optimal controller improvements are obtained using the suggested <a name="_Hlk98139050"></a>particle swarm optimization (PSO) technique. The PSO algorithm is initially built and integrated with the PID controller to control the DC motor gimbal. Before comparing the performance of a DC motor with PSO-PID with a DC motor with Zeigler-Nichols controller, the impacts of iteration numbers are explored. Finally, bode analysis was conducted to validate the stability of the proposed PSO-PID controller. Simulation is conducted within the MATLAB environment to verify the system's performance in terms of settling time, steady-state error and overshoot. The simulation results show has a longer settling time (0.91656 sec) than the Ziegler-Nichols controller (0.14316 sec) but a shorter rising time (0.091686 sec) than the Ziegler-Nichols controller (0.00094 sec). Furthermore, the overshoot was lowered from 12.941% to 0.959% as a result. As a result, the suggested PSO-PID controller technique outperforms the Ziegler-Nichols controller in terms of overshoot and rise time. Further study will investigate the integration of other optimisation methodologies such as fuzzy logic for better performance.</span>

Performance Enhancement of the DPC Control Based on a VGPI Controller Applied to a Grid Connected PV System

Even though the PI controller is among the most widespread controllers in photovoltaic (PV) systems, it presents overshoots and undershoots in the tracking trajectory mode. This major disadvantage remains an open problem. In the current paper, a Direct Power Control (DPC) of a three-phase grid-connected PV system based on a Variable Gain PI (VGPI) controller is developed to remove the overshoots and undershoots in the classical PI controller. The proposed control system has been tested in the Matlab/Simulink environment. The simulation results demonstrate the feasibility and robustness of the proposed method in terms of overshoots, undershoots, and current quality.