Proportional-integral-derivative Controller Design Research Articles

Intelligent Proportional Integral Derivative Control Design for Sampled and Delayed Output Measurements Systems

This paper considers the problem of control systems with sampled and delayed output measurements. Moreover, the considered system is Continuous Time Linear systems (CTLS) which is X-Y robot. The proposed control system design to solve this problem is called Intelligent Proportional Integral Derivative controller (IPID) which has a simple structure. Furthermore, the intelligent technique which is Genetic Algorithm is used to optimize parameters of the proposed controller. In order to restate the continuous state with non-time delay the piecewise estimator observer is utilized. Moreover, an example on MATLAB Simulink is presented to validate the proposed control approach with comparisons of the conventional Proportional Integral Derivative controller and the simulation results are provided to demonstrate its effectiveness.

Design of Mixed-Mode Analog PID Controller with CFOAs.

The design of a mixed-mode proportional-integral-derivative (PID) controller circuit using current-feedback operational amplifiers (CFOAs) as active components is proposed. With the same circuit topology, the proposed configuration of three CFOAs, four resistors, and two capacitors is capable of performing the PID controller in each of the following four modes: voltage mode, trans-admittance mode, current mode, and trans-impedance mode. Numerous mathematical analyses are conducted to determine the controller's performance under both ideal and non-ideal conditions. Additionally, the mixed-mode second-order lowpass filter is suggested and also used to examine the workability of the proposed mixed-mode PID controller in a feedback control structure. The proposed PID controller is implemented with the commercially available IC-type CFOA AD844, and the simulation results are presented to illustrate the functionality of the controller and its closed-loop control system. According to the findings, the total power consumption of the proposed PID controller is 0.348 W, with symmetrical supply voltages of ±9 V. It also has a temperature variation of less than 0.2% over the AD844's usable range. Monte Carlo statistical analysis results revealed that the gain responses of the controller exhibited a deviation of no more than 7.72% from the theoretical value. The controlled filter in a closed-loop control system has a 43% faster rise time and peak time than the uncontrolled filter in all four modes of operation. It also has a steady-state error less than 0.2 mV for voltage responses and 0.72 µA for current responses.

A Cascade PID Controller Design with Cuckoo Optimization Algorithm (COA) and Input Shaping (IS)

From past to present, Proportional-Integral-Derivative (PID) controllers stand out as the most widely used types of controllers. Due to the high-performance requirements, experimentally determined controller coefficients necessitate the application of modern optimization techniques. In this study, Ziegler-Nichols, Chien-Hrones-Reswick, and Cohen-Coon methods, which allow parameter calculation through the open-loop system's step response method, were compared with the Cuckoo Optimization Algorithm for PID controllers designed for a brush-commutated DC motor with unknown parameters in the Matlab environment. The comparison was based on Integral of Absolute Error (IAE), Integral of Square Error (ISE), Integral of Time-weighted Absolute Error criterion. Similarly, the performance of the Cuckoo Algorithm was discussed in terms of stability margins and stability peaks. In this comparison, it was observed that the PID controller optimized with the Cuckoo Algorithm operated with high proportional and integral coefficients to minimize the cost function, resulting in overshoot in the system response. Input shaping, a commonly used method in open-loop control of both brushed and brushless DC motor systems, was integrated into the system to mitigate this overshoot. The hybrid controller achieved the best performance in terms of IAE, ISE and ITAE in the system response, with less overshoot compared to the other mentioned methods.

Elitism-crossover barnacle mating optimization and its application to PID controller design for a buck converter

This paper presents an elitism-crossover barnacle mating optimization (ECBMO). It is an improvement of barnacle mating optimization (BMO). The original BMO suffers from local optima problem leading to a low accurate solution. A new method of offspring generation is adopted into the original BMO structure. Some features of the best-so-far solution are incorporated into the generated offspring. The accuracy performance of the proposed algorithm is tested on several IEEE functions. A statistical analysis is conducted to compare its performance over the original BMO. It is also applied to optimize proportional integral derivative (PID) parameters for controlling output voltage of a buck converter. Result of benchmark functions test shows the proposed algorithm has attained higher accuracy for all functions compared to BMO algorithm. Application on the real problem shows both algorithms control the converter voltage satisfactorily. However, the ECBMO has achieved more optimal PID parameters and leading to a better output voltage response.

Analytically derived disturbance rejection‐based PID controller tuning for second‐order plus dead time plants

AbstractThis letter proposes a proportional‐integral‐derivative (PID) design method for second‐order plus dead time models based on a desired closed‐loop transfer function for load disturbance changes. Also, tuning rules for single integrating first‐order plus dead time models are provided. Using a two‐degree‐of‐freedom control scheme, the design problem considers load disturbance rejection, robustness to model uncertainties, and setpoint tracking. Three examples are used to assess the performance of the proposed method. Finally, the results are compared to two popular PID control design methodologies, skogestad internal model control or skogestad IMC (SIMC) and improved SIMC (iSIMC).

Reaction Curve-Assisted Rule-Based PID Control Design for Islanded Microgrid

In a renewable energy-based islanded microgrid system, frequency control is one of the major challenges. In general, frequency oscillations occur in islanded microgrids due to the stochastic nature of load and variable output power of distributed generating units (DGUs). In the presented research proposal, frequency oscillations are suppressed by implementing the proportional integral derivative (PID) controller-based control design strategy for an islanded microgrid. The modeling of the islanded microgrid is firstly presented in the form of a linearized transfer function. Further, the derived transfer function is approximated into its equivalent first-order plus dead time (FOPDT) form. The approximated FOPDT transfer function is obtained by employing the reaction curve method to calculate the parameters of the FOPDT transfer function. Furthermore, the desired frequency regulation is achieved for the manifested FOPDT transfer function by incorporating PID control design. For PID controller tuning, different rule-based methods are implemented. Additionally, comparative analysis is also performed to ensure the applicability of the comparatively better rule-based tuning method. The Wang–Chan–Juang (WCJ) method is found effective over other rule-based tuning methods. The efficacy of the WCJ method is proved in terms of transient response and frequency deviation. The tabulated data of tuning parameters, time domain specifications, and error indices along with responses are provided in support of the presented control strategy.

Design of Multivariable PID Control Using Iterative Linear Programming and Decoupling

The design of multivariable process control systems is specially complicated when there are strong interactions between the different control loops, and even more with multiple time delays. This paper proposes an iterative design method of centralized proportional-integral-derivative (PID) controllers for stable linear systems. The methodology is based on the linear parameterization of equivalent loop transfer functions (ELTFs) for centralized control. These functions capture the dynamics of the other loops and, from a prior design, allow solving the design problem at each iteration with linear programming that shapes the Nyquist plot of the ELTFs in the frequency domain, which also avoids the need for approximations. Two optimizations are proposed: (I) maximizing integral gains by fulfilling linear robustness margins in each ELTF and (II) maximizing linear robustness margins by fulfilling minimum bandwidths in each loop. In both optimizations, static decoupling and decoupling at a frequency close to the bandwidth of each loop are included as constraints, which improves the decoupling performance and the procedure convergence. The effectiveness of the method is verified in three simulation examples (square and non-square) and a lab experimental process. The proposed designs achieve a similar or better response when compared to that achieved by other authors.

Optimal Fuzzy Logic Proportional Integral Derivative Controller Design by Bee Algorithm for Hydro-Thermal System

This paper proposes a robustness and an optimum of the fuzzy logic-proportional integral derivative (FLPID) controllers for automatic generation control (AGC) of a two area interconnected hydro-thermal system and an application of bee algorithm (BA) in order to design FLPID controllers. Traditionally, membership functions (MF) and control rules (CR) of the fuzzy logic controller (FLC) were achieved by trial and error methods of designers. To solve this problem, BA was proposed to concurrently tune PID gains, MF and CR of the FLPID controllers to minimize frequency deviations and tie-line power deviations of power system. Simulation results clearly showed that the performance and the robustness of the optimal FLPID controllers were superior to the conventional PID and the FLPID controllers in terms of settling time and overshoot.

Modeling and Tuning of Electronic Throttle Control System in Formula Student Car

The Lookprabida formula student team uses an electronic throttle control (ETC) system in the racecar. Unfortunately, the ETC system cannot regulate the throttle valve to closely pursue angular setpoints. Thus, this paper endeavors to improve the response of the ETC system in racing cars. The ETC system is first examined using frequency response analysis. Parameter identification is carried out to obtain a mathematical model for the electronic throttle body. The Ziegler-Nichols’ second method is then applied to preliminarily select the proportional integral derivative (PID) gains using MATLAB/Simulink®. Then, the ETC system fine-tuning on the experimental set is carried out. The final PID controller design results in a reduction in the settling time from 0.35 to 0.20 s, overshoot from 29.6 to 19.33%, and steady-state error from 2.00 to 0.22%. The racecar with the finally designed PID gains is tested on a training track; the root mean square error (RMSE) shows that the throttle valve approaches much closer to angular setpoints.

Frequency-based multivariable control design with stability margin constraints: A linear matrix inequality approach

In this paper, a proportional–integral–derivative controller design problem for stable multivariable process is considered. A frequency-based control technique is formulated as a convex optimization problem with linear matrix inequality constraint. The multivariable proportional–integral–derivative controller is designed so that H∞ norm of the difference between the designed loop gain function and a desired one is minimized. Linear matrix inequality constraints on the stability margin guarantees designed loop stability. Process frequency domain data is used to solve the proposed optimization problem. Knowledge of the process parametric model is not necessary. The desired loop gain transfer function is defined from the reference complementary sensitivity function. Simulation results show the comparison between the proposed technique and technique from the literature. In these examples, the proposed technique resulted in stable closed-loops different from the others and in a reduction of the performance indices.

Complex-order PID controller design for enhanced blood-glucose regulation in Type-I diabetes patients

Type-I Diabetes (TID) is a chronic autoimmune disease that elevates the glucose levels in the patient’s bloodstream. This paper formulates a fractional complex-order Proportional-Integral-Derivative (PID) control strategy for robust Blood Glucose (BG) regulation in TID patients. The glucose-insulin dynamics in blood plasma are modeled via the Bergman-Minimal-Model. The proposed control procedure employs the ubiquitous fractional order PID controller as the baseline BG regulator. The design flexibility of the baseline regulator to effectively normalize the BG levels is enhanced by assigning complex orders to the integral and differential operators instead. The resulting Complex Order PID (CO-PID) regulator strengthens the controller’s robustness against abrupt variations in the patient’s BG levels caused by meal disturbances or sensor noise. The controller parameters are numerically optimized offline. The aforesaid propositions are justified by performing credible simulations in which the proposed controller is tasked to effectively track a set point value of 80 mg/dL from an initial state of hyperglycemia under various disturbance factors. As compared to the FO-PID controller, the CO-PID controller improves the reference tracking-error, transient recovery-time, and control expenditure by 13.1%, 33.4%, and 28.1%, respectively. The simulation results validate the superior reference-tracking accuracy of the proposed CO-PID controller for BG regulation.

Ters Sarkaç Sisteminde Referans Model Kullanarak Kesir Dereceli PID Denetleyici Tasarımı

The proportional Integral Derivative (PID) controller has three basic parameters: Proportional gain (Kp), Integral gain (Ki) and Derivative gain (Kd). In a conventional PID controller, integral and derivative operators are integer order. The researchers proposed a fractional order PID (PIλDµ) controller by using the fractional integral and derivative operators instead of the integer order integral and derivative operators in the traditional PID controller because it improves the control performance. The PIλDµ controller has an additional fractional integrator degree (λ) and fractional derivative degree (µ). In this study, the focus is on the design of a fractional-order PID controller according to a reference model in the time domain. Bode's ideal transfer function was used as the reference model. It is aimed to obtain PIλDµ parameters by minimizing the error between the time domain response of Bode's ideal transfer function model and the output of the system to be controlled by using the optimization method. Genetic Algorithm (GA) optimization was used as the optimization method. The study was carried out as a simulation study on an inverted pendulum system with a single-input multiple-output (SIMO) structure.

Fractional-order PID controller design via optimal selection strategy of frequency domain specifications

In this paper, a novel fractional-order Proportional-Integral Derivative (PID) controller design depending on optimal selection of frequency domain specifications is proposed for time delay systems. The frequency domain specification sets, namely, (i) phase margin and gain crossover frequency and (ii) phase margin and gain margin are determined so that the reference model is optimal according to three time domain performance indices, i.e. Integral Square Error (ISE), Integral Time Square Error (ITSE) and Integral Absolute Error (IAE). Here, the delayed Bode's ideal transfer function is employed in the reference model. Moreover, the stability region of the reference model is given via a theorem. In simulation studies, the proposed methodology is compared with other two different methods using the same frequency domain specifications. It is observed that the proposed optimal fractional-order PID controllers outperform according to the mentioned performance indices, and they also possess considerably acceptable performance in terms of other time domain specifications such as overshoot, settling time, etc.

Design of Sliding Mode Control Strategy for DC Motor

Abstract: This research paper presents a comparative study ofsliding mode control (SMC) strategies and proportional- integralderivative (PID) control for DC motor applications. The project involves the design and implementation of PID and SMC controllers, as well as the evaluation of their respective response characteristics. Furthermore, the SMCcontroller is designed using three different sliding surfaces to analyze their impact on the control system's performance. A model is developed and simulated usingMATLAB/SIMULINK. Overall, the simulation results show that sliding mode controller is superior than PID for speed control of dc motor.

VDIBA-Based Current-Mode PID Controller Design

This paper aims to bring a voltage differencing inverting buffered amplifier (VDIBA)-based current-mode (CM) proportional integral derivative (PID) controller circuit. This CM PID controller is designed with a single VDIBA, three resistors, and two grounded capacitors. The proposed circuit is easy to design, and the control parameters can be tuned without changing the design configuration. A sensitivity analysis of the control parameters to electronic components has been conducted. The Simulation Program with Integrated Circuit Emphasis (SPICE) simulation has been performed using Taiwan Semiconductor Manufacturing Company (TSMC) [Formula: see text]m complementary metal-oxide semiconductor (CMOS) technology parameters. An application circuit example is given to demonstrate the reliability of the proposed PID design. A comparison table of the PID controllers previously reported in the literature is also presented.

A new MOPSO algorithm for solving mathematical test functions and control engineering problems

In this article, a new multiobjective particle swarm optimization (MOPSO) algorithm is introduced to improve the performance of a sliding mode based robust fuzzy proportional–integral–derivative (PID) controller. In this regard, the non-dominated solution having minimum number of neighbors is considered as the global best position, while the sigma values of the members are employed to determine the personal best position. A modified multiple-crossover operator is combined with the operators of the particle swarm optimization to significantly increase the convergence speed of the algorithm. To limit the size of the archive, a dynamical elimination scheme defined in the Euclidean space is introduced. Besides, iteration-based linear relations are implemented to adaptively compute the inertia weight and learning coefficients. To evaluate the effectiveness of the introduced MOPSO algorithm, the requirements are conducted by means of three benchmark functions with regard to generational distance, spacing, and maximum spread metrics. This analysis demonstrates that the proposed algorithm operates better through comparison with well-known elitist multiobjective evolutionary algorithms. Moreover, the MOPSO algorithm is applied for optimal design of a hybrid robust fuzzy PID controller for a pneumatic system with two bellows. Conflicting objective functions are considered as the normalized values of overshoot and settling time of the displacement between the bellows that should be simultaneously minimized. The feasibility and efficiency of the strategy are assessed in comparison with the conventional controllers.

Fuzzy Fractional Order Proportional Integral Derivative Controller Design for Higherorder Time Delay Processes

Process control is the interested domain of interest as the industrial high-order applications require an effective control mechanism with higher robustness. Since the conventional method of proportional integral derivative (PID) controller remains inadequate for the higher-order processes, this research concentrates on the fuzzy fractional-order controllers, that more and more attention nowadays in various research areas of science and engineering specifically, on the areas of tuning, design, implementation, and analysis of these controllers. Accordingly, this research marks a milestone for the control industry through introducing a robust controller, fuzzy fractional-order (FO) PI[Formula: see text]D[Formula: see text] (fuzzy FOPID) for higher-order applications with dead time. The fractional-order controller requires the fractional orders for the derivative term [Formula: see text] and integral term [Formula: see text]. The results of the proposed fuzzy FOPID controller is analyzed and compared with the comparative methods, such as a proportional integral derivative (PID) controller and fractional order PID controller (FOPID). Accordingly, the time-domain specifications are evaluated in the presence/absence of the disturbances is analyzed and in addition, the time domain optimal integral metrics are determined. Simulations are carried out using MATLAB/SIMULINK.

PID Controller Design for Heating Furnace Temperature Based on Bat Algorithm (BA)

A furnace is a tool for heating materials, oil, and others. Furnaces use gas, coal, and oil as fuel. Temperature is the main parameter that needs to be controlled to remain stable, and precise and improve fuel efficiency. As technology develops, several methods can be used to control temperatures that are more reliable than conventional controls. The technology is a Proportional Integral Derivative (PID) controller. PID controllers have been proven and widely used in the industry, but determining the gain of the PID value is still not accurate. This can affect temperature stability, and slow response to reach the desired set point. Therefore, it is necessary to optimize the control system. Optimizing by looking for a better PID gain value with the artificial intelligence tuning method. The Artificial Intelligence method is Bat Algorithm(BA). The simulation results and discussion show that the best design is PID-BA with an overshoot of 0.0429, non undershoot, and the fastest settling time of 16.9 seconds

Robust H[formula omitted]-PID control Stability of fractional-order linear systems with Polytopic and two-norm bounded uncertainties subject to input saturation

This paper deals a type of H∞ proportional–integral–derivative (PID) control mechanism for a type of structural uncertain fractional order linear systems by convex Polytopic and two-norm bounded uncertainties subject to input saturation which mainly focuses on the case of a fractional order α such that 0<α<1. The Gronwall–Bellman lemma and the sector condition of the saturation function are investigated for system stability analysis and stabilization. The main strategy of the presented strategy is to restore fractional order PID controller design under input saturation problem from static output feedback controller design. Unlike existing strategies, non-iterative strategy is used to get optimal output feedback based on the LMI. On the premise of a linear matrix inequality algorithm, the SOF control laws can be obtained. After that, the fractional-order PID controller is recovered from the SOF controller. A numerical example is provided in order to show the validity and superiority of the proposed method.

Design of Optimal PID Controller for Electric Vehicle Based on Particle Swarm and Multi-Verse Optimization algorithms

The automotive industry is moving toward more environmentally friendly automobiles with greater range and performance than traditional vehicles as the effects of global warming worsen. Because of the positive impact, electric vehicles can have on reducing harmful emissions from the transportation sector, scientists have grown increasingly interested in the possibility of analyzing and simulating electric vehicles. In this study, we develop a non-linear dynamic concept of an electric vehicle by fusing kinetic and electrical components. Then we create a proportional Integral derivative (PID) controller to help it stay on course. To obtain optimal parameters for this controller by minimizing the error between the desired and actual output, Particle Swarm Optimization (PSO), and Multi-Verse Optimization (MVO) algorithm are used. The proposed controllers tested with linear and nonlinear trajectories to represent the electric vehicle's speed. The computation findings show that the proposed controller works perfectly, keeping up with the electric vehicle's speed quickly and precisely. In particular, the MVO-based proportional-integral-derivative (PID) controller is superior to the proportional-integral-derivative (PID) -based PSO method in terms of no steady-state error and smallest overshoot (0.05% with MVO while 0.25% with PSO) prevention for electric vehicle (EV) speed despite the better results of settling time and rising time obtained in PSO(0.767 And 0.211 s) respectively while these values were (0.807 and 0.215 s), respectively, in MVO. All works are performed in MATLAB (R2020a) /Simulink environment.