Proportional-integral-derivative Coefficients Research Articles

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

This paper aims to design an accurate angular position control for DC motors using a proportional integral derivative (PID) controller. The estimated DC motor parameters have been calculated using the parameter estimator toolbox in MATLAB, Arduino Mega 2560 and speed sensor to build an accurate model in MATLAB. The optimized PID coefficients are found for the DC motor model using the firefly algorithm (FA), which aims to make the actual angle match the desired value without overshooting and oscillations. The PIC16F877A microcontroller was used to implement the code based on optimized PID coefficients found in MATLAB/Simulink to generate the suitable pulse width modulation (PWM) output. In this work, step input was tested to analyze the characteristics of the system response in terms of rise time, settling time and overshoot. It was found that the controller output response curve which is produced from FA-based-PID reached the desired position without overshoot and any oscillations. The findings established that closed-loop control of any system using a system identification toolbox and optimized PID technique can be applied in real applications using low-cost controllers and sensors such as PIC16F877A microcontroller and analog rotary position sensor, respectively.

Autonomous flight performance optimization of fixed-wing unmanned aerial vehicle with morphing wingtip

PurposeThe purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.Design/methodology/approachThe main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.FindingsLongitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.Originality/valueThis paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Designing an optimal PID controller for a PV-connected Zeta converter using genetic algorithm

This paper suggests a way of fixing problems of voltage fluctuations and peak overshoot in a PV-connected Zeta converter system. The Zeta converter in the proposed approach is controlled using proportional integral derivative (PID) while a genetic algorithm (GA) calculates the PID coefficients based on the control mechanism. The performance of the designed system was analyzed in a MATLAB/Simulink environment. The analysis showed that the proposed system reduced the output voltage ripple and peak overshoot during transient conditions by providing feedback to the converter through the PID controller, this is a significant improvement when compared to the results found without a PID controller.

Attitude control of a quadrotor using PID controller based on differential evolution algorithm

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency.

Simultaneous arm morphing quadcopter and autonomous flight system design

Purpose The purpose of this paper is to obtain values that stabilize the lateral and longitudinal flight of the quadrotor for which the morphing amount and the best Proportional-Integral-Derivative (PID) coefficients are determined by using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm. Design/methodology/approach Quadrotor consists of body and arms; there are propellers at the ends of the arms to take off and rotors that rotate them. By reducing the angle between mechanism 1 and the rotors with the horizontal plane, the angle between mechanism 2 and the arms, the rotors rise and different configurations are obtained. Conventional multi-rotor aircraft has a fixed fuselage and does not need a tail rotor to change course as helicopters do. The translational and rotational movements are provided by the rotation of the rotors of the aircraft at different speeds by creating moments about the geometric center in 6-degree-of-freedom (DOF) space. These commands sent from the ground are provided by the flight control board in the aircraft. The longitudinal and lateral flight stability and properties of different configurations evaluated by dynamic analysis and simulations in 6 DOF spaces are investigated. An algorithm and PID controller are being developed using SPSA to achieve in-flight position and attitude control of an active deformable aircraft. The results are compared with the results of the literature review and the results of the previous article. Findings With SPSA, the best PID coefficients were obtained in case of morphing. Research limitations/implications The effects of quadrotor arm height and hub angle changes affect flight stability. With the SPSA optimization method presented in this study, the attitude is quickly stabilized. Practical implications With the optimization method, the most suitable PID coefficients and angle values for the lateral and longitudinal flight stability of the quadrotor are obtained. Social implications The transition rate and PID coefficients are determined by using the optimization method, which is advantageous in terms of cost and practicality. Originality/value With the proposed method, the aircraft can change shape to adapt to different environments, and the parameters required for more stable flight for each situation will be calculated, and this will be obtained more quickly and safely with the SPSA optimization method.

Performance analysis of PID controller-based metaheuristic optimisation algorithms for BLDC motor

ABSTRACT Today, the use of permanent magnet brushless DC (PMBLDC) motors in vehicles is increasing due to the characteristics of sensorless operation. PMBLDC motor controllers can control the speed and position in the closed-loop feedback system without the need for a position sensor mounted on the shaft. Proportional – Integral – Derivative (PID) controller is one of the most common feedback control algorithms used in PMBLDC motor controllers. Optimizing problems using deterministic methods such as Lagrange and simplex methods requires basic information and complex calculations. Meta-heuristic algorithms are a type of stochastic algorithm that is used to find the optimal solution. Meta-heuristic algorithms are divided into three general categories: evolutionary algorithms, swarm intelligence algorithms, and stochastic algorithms. In this paper, using 14 metaheuristic optimisation algorithms, PID control parameters including settling time, time rise, overshoot, and stability of step response of the mentioned system are optimised. In this paper, 14 meta-heuristic algorithms are simulated and evaluated to optimise the PID coefficients of the controller, including settling time, rising time, excessive increase, and step response stability. The simulation result shows that the genetic algorithm (GA) has the best performance in terms of cost function and biogeography-based optimisation (BBO) in terms of settling time and rising time parameters. Finally, the simulation results are confirmed using experimental results.

Numerical analysis of the performance of a PID-controlled air curtain for fire-induced smoke confinement in a tunnel configuration

In order to effectively meet the requirements of smoke confinement in different fire scenarios, this paper proposes an adaptive intelligent smoke control method combining PID (Proportional Integral Derivative) system with a traditional air curtain. By taking a tunnel fire configuration as an example, the feasibility of PID-controlled air curtains is explored and the calculation method of PID control parameters is clarified. The stability and reliability of the PID-controlled air curtain in realizing the smoke control effect in different complex fire scenarios are studied. The method of determining optimal PID coefficients is first proposed. Then, the robustness of the PID-controlled air curtain has been assessed by discussing the sensitivity of these parameters with different fire scenarios. The results show that the PID-controlled air curtains can effectively meet the requirements of smoke control in different complex fire scenarios. The impact of different fire scenarios on the PID-controlled air curtain system lies in the pre-control period, such as the system features of delay time, response time, overshoot, etc.

Application of PID controller in chemical process system using COMSOL

Process control covers the analysis, design, and application of control systems to achieve specific objectives in process safety, production capacity, and product quality. Control of any system is significant, especially in chemical process systems. For this, it is necessary to use a control that can meet the system's demands and work harmoniously. In this study, a system that can be adapted to the chemical process industry has been designed and modelled in the COMSOL program. The concentration level and velocity behaviour of the same type of fluid applied from two channels over this model were kept at the desired level with the Proportional Integral Derivative (PID) control algorithm. The flow rate of the fluids can be changed with the controller to achieve the desired concentration. As a result of the simulation with different PID coefficients, the best response was obtained when the high proportional gain coefficient was applied. It has been determined that the best response of the process in both concentration and velocity data is achieved when kp= –1 m4/(mol·s). A high overshoot and steady-state error occurred at low proportional gain values in the system response.

Strategy Optimization in a Robot Race Using PID

In this paper, the proportional-integral-derivative (PID) Controller is optimized to complete a robot challenge, The Race, by the University of Texas at Austin’s Robotics Academy. Optimization is done through three stages: (A) optimizing the PID coefficients of both wheels; (B) optimizing the constant speed, and (C) setting the constant speed to its maximum. Finally, the analysis proves that Method C, which yields the fastest time, approaches the theoretical bound.

A PID controller for synchronization between master-slave neurons in fractional-order of neocortical network model

Modeling of the biological neurons is a way to understand the architecture of neural networks of the brain. A complex brain network includes the synchronization between some groups of neurons. The dynamic behavior of interactions between groups of slave-master neurons in the neocortical network is unpredictable and challenging. The purpose of synchronizing a neural interaction is to reduce the synchronization error between the chaotic slave-master neurons. This paper uses a proportional–integral–derivative (PID) controller to synchronize master–slave neurons in the fractional-order of the neocortical network model based on dendritic spike frequency adaptation (DSFA) uncertainties and unknown disturbance effects. The purpose of this article is in two parts: First, we implemented the effect of previous states of the neuron conditions by fractional-order of the differential equations in the neocortical network model. Second, by synchronizing the FO neocortical master–slave model by PID controller, we investigated the connection strength of the complex network in chaotic point of view. The optimized PID coefficients and fractional-order were calculated using root mean square error (RMSE) criteria to control the membrane voltage synchronization. The chaotic behavior of the system was evaluated by numerical techniques such as attractor analysis and time series diagrams. The optimal RMSE value for master–slave neurons occurred at fractional-orders 0.89. It is shown that the synchronization of master–slave neurons improves over time, and eventually they are fully synchronized while the controller error is reduced.

Combined Quadrotor Autopilot System and Differential Morphing System Design

The aim of this article is to design and model a quadrotor with autopilot system and differential morphing using the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm. Along with differential morphing, quadrotor modelling and control was also done. Although it is simple in structure, it has a complex structure in terms of model and control. Newton-Euler method was used for the dynamic model. Non-linear motion equations have been converted to linear motion equations. The full quadrotor model was drawn in the Solidworks program. Mass and moment of inertia information was obtained from this model. Simulation model was created by using state space model approach in Matlab / Simulink environment. Proportional integral derivative (PID) algorithm was used as the control structure. Differential morphing and PID coefficients rates were determined with SPSA. With the optimization method, determining the differential morphing rate, PID coefficients and applying it to the quadrotor provides a very innovative method.

A novel data-driven controller for solid oxide fuel cell via deep reinforcement learning

Solid oxide fuel cells (SOFC) are complex nonlinear and time-varying systems with operational constraints. How to effectively control and stabilize the output voltage while preventing constraint violations becomes a main challenge to its wide application. To improve the efficiency of its operation and power tracking control and prevent constraint violations, this paper designs a data-driven adaptive proportional integral derivative (PID) controller, which maintains the output voltage at the reference value via the optimal control of the hydrogen flow. Moreover, a novel large-scale deep reinforcement learning (DRL) algorithm, called the two-stage training strategy large-scale twin delayed deep determination policy gradient (TGSL-TD3PG), is adopted to adaptively adjust the baseline coefficients of the designed controller scaffolded by the high adaptability and model-free features of reinforcement learning. In the training of TGSL-TD3PG, multiple agents are simultaneously employed to acquire the best policy, whereby the training of optimal agents in practice is structured upon the principles of imitation learning and curriculum learning. This method solves the common problem of low robustness in conventional deep reinforcement learning and can be applied to the field of control. Moreover, the TGSL-TD3PG algorithm incorporates the baseline PID coefficients into the design objective and offers the controller with the online coefficient-adjusting ability through learning. Therefore, such limitations as no adaptability, low robustness and neglect of constraints associated with conventional PID control are significantly addressed. Simulation results show that the proposed controller can promote the SOFC to track the load power demand effectively while maintaining the fuel utilization ratio constant. The proposed algorithm reduces 45.2% of the output voltage setting time and 30% of the voltage overshoot to the maximum extent, while generating the constraint violation time of fuel utilization at 0.

Hexarotor Longitudinal Flight Control with Deep Neural Network, PID Algorithm and Morphing

Unmanned Aerial Vehicles (UAV) have become an integral part of life, from military operations to entertainment. With this popularity, the interest of researchers on these UAVs has gradually increased. The use of UAVs in many areas and the researches carried out by researchers have revealed the limitations of these devices. At the beginning of these restrictions is the passage through the narrow space. In this study, longitudinal flight control of a hexarotor UAV is discussed with morphing. The limitation of this study is that it is difficult to estimate the moment of inertia and proportional integral derivative (PID) coefficient values according to the arm length, since there is a change in the hexarotor arm lengths with the morphing and the rigid body model changes accordingly. In this study, it is aimed to obtain these parameters with the Deep Neural Network(Artificial Neural Network(ANN) with two or more hidden layers) to overcome this problem. 15 drawings of hexarotor morphing states were drawn in Solidworks program. A training set was created by obtaining the PID coefficients for the longitudinal flight of these drawings from the Matlab/Simulink program. The training set was taught to the Deep Neural Network and moments of inertia and PID coefficients were obtained according to the arbitrarily estimated arm extension or shortening rates. In addition, the hexarotor dynamic model was derived according to the Newton-Euler approach and modeled using the state space model. The longitudinal flight of the hexarotor is simulated with the state space model. The moment of inertia and PID coefficients were estimated by Deep Neural Network according to the values determined by the program randomly together with the initial state. Simulations were made with these parameters and the results were given in graphics.

Effective Control of Voltage and Frequency in Microgrid Using Adjustment of PID Coefficients by Metaheuristic Algorithms

Controlling of voltage and frequency in the microgrid is a challenging issue and many controllers are suggested for this purpose. Proportional-Integral-Derivative (PID) controller is simple and efficient when its coefficients are optimally adjusted. Recently, evolutionary algorithms are used to optimally adjust the PID coefficients. In this paper, four evolutionary algorithms, particle swarm optimization, genetic algorithm, imperialist competitive algorithm and selfish herd optimization, are applied to set the PID controller coefficients and tested in the frequency and voltage control of the microgrid. Settling time, overshoot and stability margin are three main factors in the control response of PID which are considered for generating the fitness function to be optimized in terms of the PID coefficients. The proposed microgrid includes inverter-based distributed generators and different loads. Load change and different faults may cause the degradation of frequency and voltage where using of the PID controller leads to better control of these parameters. Simulation results show the efficiency of all four algorithms where particle swarm optimization performance is better than other three algorithms. Experimental results also verify the simulation results discussed in the paper.

PID++: A Computationally Lightweight Humanoid Motion Control Algorithm.

Currently robotic motion control algorithms are tedious at best to implement, are lacking in automatic situational adaptability, and tend to be static in nature. Humanoid (human-like) control is little more than a dream, for all, but the fastest computers. The main idea of the work presented in this paper is to define a radically new, simple, and computationally lightweight approach to humanoid motion control. A new Proportional-Integral-Derivative (PID) controller algorithm called PID++ is proposed in this work that uses minor adjustments with basic arithmetic, based on the real-time encoder position input, to achieve a stable, precise, controlled, dynamic, adaptive control system, for linear motion control, in any direction regardless of load. With no PID coefficients initially specified, the proposed PID++ algorithm dynamically adjusts and updates the PID coefficients , and periodically. No database of values is required to be stored as only the current and previous values of the sensed position with an accurate time base are used in the computations and overwritten in each read interval, eliminating the need of deploying much memory for storing and using vectors or matrices. Complete in its implementation, and truly dynamic and adaptive by design, engineers will be able to use this algorithm in commercial, industrial, biomedical, and space applications alike. With characteristics that are unmistakably human, motion control can be feasibly implemented on even the smallest microcontrollers (MCU) using a single command and without the need of reprogramming or reconfiguration.

Performance prediction modeling of a premature baby incubator having modular thermoelectric heat pump system

In this study, a comprehensive mathematical model of a thermoelectric infant incubator operating with modular thermoelectric heat pump system (MTEHPS) was developed. All heat and mass exchange processes of the thermoelectric infant incubator system with both the premature infant and the surrounding atmosphere were determined and modeled. For that, the system was divided into 8 sub-compartments. Mass and energy conservation equations were applied to these sub-systems explaining the interactions among them. Moreover, the PID (Proportional integral derivative) coefficients (KP, KI and KD) required for the experimental automatic control of the proposed system were found out by simulating the created mathematical model in MATLABR/Simulink environment. By integrating the proposed MTEHPS into a commercial neonatal incubator, experimental results were obtained. Accuracy of the model developed was analyzed by comparing its results with the experimental results and the data from literature. The results clearly demonstrated that the data obtained from the model are in good agreement with the data obtained from the real system and literature.

Simultaneous quadrotor autopilot system and collective morphing system design

PurposeThe purpose of this paper is to design a quadrotor with collective morphing using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm.Design/methodology/approachQuadrotor design is made by using Solidworks drawing program and some mathematical performance relations. Modelling and simulation are performed in Matlab/Simulink program by using the state space model approaches with the parameters mostly taken from Solidworks. Proportional integral derivative (PID) approach is used as control technique. Morphing amount and the best PID coefficients are determined by using SPSA algorithm.FindingsBy using SPSA algorithm, the amount of morphing and the best PID coefficients are determined, and the quadrotor longitudinal and lateral flights are made most stable via morphing.Research limitations/implicationsIt takes quite a long time to model the quadrotor in Solidworks and Matlab/Simulink with the state space model and using the SPSA algorithm. However, this situation is overcome with the proposed model.Practical implicationsOptimization with SPSA is very useful in determining the amount of morphing and PID coefficients for quadrotors.Social implicationsSPSA optimization method is useful in terms of cost, time and practicality.Originality/valueIt is released to improve performance with morphing, to determine morphing rate with SPSA algorithm and to determine PID coefficients accordingly.

Quadrotorlarda PID Algoritması Kullanarak Diferansiyel Morphingin Yanlamasına Uçuş Üzerine Etkisinin İncelenmesi

Quadrotor is unmanned aerial vehicles that are widely used in military and civil fields. Quadrotors differ from conventional helicopters, which use rotors that are able to vary the pitch of their blades dynamically as they move around the rotor hub. Quadrotors generally use two pairs of identical fixed pitch propellers; two clockwise (CW) and two counterclockwise (CCW). These use independent variation of the speed of each rotor to achieve control. In this study, the quadrotor lateral flight and the effect of morphing on this flight were investigated. Morphing is called the change of unmanned aerial vehicles in their geometry. Newton-Euler method is used to create the quadrotor model. However, since the quadrotor is a system with non-linear dynamics, motion equations are converted to linear equations. The quadrotor full model was drawn in the Solidworks program, including batteries, controllers and propellers, in accordance with its actual dimensions. State space model approach was used in simulations in Matlab / Simulink environment. The value of mass and inertia moments from the Solidworks program is used in the state space model. Proportional integral derivative (PID) was used as the control algorithm. PID is an algorithm widely used in industrial applications. PID is preferred because it is strong, fast and simple. The system to which PID will be applied must be linear. The PID coefficients Kp, Ki and Kd are important for the stable operation of the system. Appropriate values should be chosen. In this study, lateral flight performance was monitored using differential morphing and PID algorithm and the results were presented.

Effects of Temperatures and Viscosity of the Hydraulic Oils on the Proportional Valve for a Rice Transplanter Based on PID Control Algorithm

This study was conducted to develop a proportional-integral-derivative (PID) control algorithm considering viscosity for the planting depth control system of a rice transplanter using various hydraulic oils at different temperatures and to evaluate the performance of the control algorithm, and compare the performance of the PID control algorithm without considering viscosity and considering viscosity. In this study, the simulation model of the planting depth control system and a PID control algorithm were developed based on the power flow of the rice transplanter (ERP60DS). The primary PID coefficients were determined using the Ziegler-Nichols (Z-N) second method. Routh’s stability criteria were applied to optimize the coefficients. The pole and double zero points of the PID controller were also applied to minimize the sustained oscillations of the responses. The performance of the PID control algorithm was evaluated for three ISO (The International Organization for Standardization) standard viscosity grade (VG) hydraulic oils (VG 32, 46, and 68). The response characteristics were analyzed using statistical method (ANOVA) and Duncan’s multiple range test (DMRT) at a significant level of 0.05 were performed through the statistical software SPSS. The results show that the control algorithm considering viscosity is able to control the pressure of the proportional valve, which is associated with the actuator displacement for various types of hydraulic oils. It was noticed that the maximum pressure was 15.405 bars at 0, 20, 40, 60, 80, and 100 °C for all of the hydraulic oils. The settling time and steady-state errors were 0.45 s at 100 °C for VG 32 and 0% for all of the conditions. The maximum overshoots were found to be 17.50% at 100 °C for VG 32. On the other hand, the PID control algorithm without considering viscosity could not control the planting depth, because the response was slow and did not satisfy the boundary conditions. The PID control algorithm considering viscosity could sufficiently compensate for the nonlinearity of the hydraulic system and was able to perform for any of temperature-dependent viscosity of the hydraulic oils. In addition, the rice transplanter requires a faster response for accurately controlling and maintaining the planting depth. Planting depth is highly associated with actuator displacement. Finally, this control algorithm considering viscosity could be helpful in minimizing the tilting of the seedlings planted using the rice transplanter. Ultimately, it would improve the transplanter performance.

Proportional integral derivative, modeling and ways of stabilization for the spark plasma sintering process

The stability of the proportional–integral–derivative (PID) control of temperature in the spark plasma sintering (SPS) process is investigated. The PID regulations of this process are tested for different SPS tooling dimensions, physical parameters conditions, and areas of temperature control. It is shown that the PID regulation quality strongly depends on the heating time lag between the area of heat generation and the area of the temperature control. Tooling temperature rate maps are studied to reveal potential areas for highly efficient PID control. The convergence of the model and experiment indicates that even with non-optimal initial PID coefficients, it is possible to reduce the temperature regulation inaccuracy to less than 4K by positioning the temperature control location in highly responsive areas revealed by the finite-element calculations of the temperature spatial distribution.