An Interactive PID Learning Module for Educational Purposes

Tuning control parameters of a fuzzy controller depends on trial-and-error. How this can be accomplished efficiently is an important subject in fuzzy control that should be investigated. We propose a method in which membership functions of a fuzzy controller can be simply set up using the knowledge of tuning parameters in a conventional PID controller. In this method, fuzzy control is realized as follows: Piecewise linear membership functions determined using the knowledge of tuning parameters in a PID controller are set up in antecedent parts of four fuzzy control rules having simple structures. Then the simplified inference method that enables high-speed inference is applied to fuzzy control rules.

Parameter tuning of PID controller for liquid level control of beer filling machine was studied in this paper, which can meet the demand of accurate controlling in beer production and improve the rapidity under the same conditions. Firstly, an improved genetic algorithm was proposed which has been verified by eight kinds of test functions. Simulation results revealed that, in comparation with other modified particle swarm optimization algorithm and modified genetic algorithm, the algorithm proposed in this work is not only capable to improve the convergence speed and precision under the same experimental conditions but also to improve the probability to converge to the optimal value. Finally, the proposed algorithm was applied to the parameter tuning of the PID controller of beer filling machine for liquid level control. Superior property had been obtained, which implied an effective improvement in the rapidity with the premise of steady-state error exclusion.

The new approach for determining and imposing the maximum stability degree of the system in function of the control object parameters and the number of tuning parameters of PID controller is proposed in this work. The elaborated method of synthesizing the typical controllers represents a simple analytical procedure, that requires the reduced volume of calculation and not impose the restrictions on the complexity of control object. The control systems synthesized by this criterion can be characterized by the high speed, a low overshot and good robustness.

Objectives: This paper provides solution for home automation, in terms of an energy efficient system. The objective is to design a device in the form of an intelligent remote control, to control the ambient condition of a house. Methods: An intelligent RF based remote control is developed which is capable of controlling in two modes- autonomous and semiautonomous. The dimming levels of appliances are tuned with optimized PID controller. A real hardware prototype is developed to control two parameters- light intensity and humidity level of the room. The tuning parameters of PID controller are calculated with the help of overall transfer function of the system, which includes the feedback signal from sensor, objective function of optimization algorithm and transfer function of the appliance (which is used to control the ambient conditions of room). Particle Swarm Optimization algorithm is used to optimize the tuning parameters for PID and results shows more energy efficiency with proposed system, when compared to conventional systems. Findings: The experimental results show prominent saving of energy by using proposed PSO-PID algorithm for designed system. It is calculated as 37.49% for light intensity control system and for humidity control saving comes out 36.9%. Novelty of the proposed system is new approach in terms of intelligent hybrid remote control. Keywords: Energy Efficient, Home automation, intelligent remote control, PID controller, PSO-PID

The main drive system using for four-high mill is usually a double closed loop DC motor speed regulating system. Generally, the speed controller and current controller are PID controllers, and the parameters of controllers are determined by the engineering design method. Once disturbance occur, the control effects are often just passable. In this paper, fuzzy logic is used to set the parameters of the speed PID controller, so as to improve the disturbance rejection ability. Simulation results show that the fuzzy tuning PID controller can give better control effect than the regular control.

Control quality of the controller depends on correct tuning of control parameter, and it is directly related to the control effect of whole control system. Aimed at the puzzle that the parameter of controller has been difficult to tune, the paper proposed a sort of optimization model based on immune mechanism for tuning of controller parameters. Firstly it defined the antibody, antigen and affinity of tuning parameter, and secondly explored the process of parameter tuning based on immune mechanism in detail, then explained the tuning method by means of optimizing parameters of PID controller as well as the seven parameters of HSIC controller. Finally it took a high-order process control as the example, and made the simulation to a large time delay process, and to a highly non-minimum phase process as well as HSIC controller. The simulation experiment results demonstrated that it is better in comparison with some other tuning methods for dynamic and steady performance. The research result shows that the proposed method is more effective for controller parameter tuning.

This paper presents a hybrid model of particle swarm optimization and genetic algorithm for tuning of PID controller parameters for a concentration control of isothermal continuously stirred tank reactor which is generally used to carry out chemical reactions in an industry on a large scale. The time domain study of different methods for tuning of PID controller parameters were observed and it was found that PSO(Particle Swarm Optimization)performance was better than GA(Genetic Algorithm) but the hybrid of GA-PSO gave the best output results in the observation to control the concentration of isothermal continuous stirred tank reactor.

In a Packed U Cell Multilevel Inverter, by optimizing the fractional order PID controller can improve performance by minimizing rise time, settling time, and steady-state error for capacitor balancing. Therefore, the Animated Oat Optimization Algorithm was used to optimize gain parameters of Fractional-order PID controllers for capacitor balancing. Based on the natural dispersal and hygroscopic motion of oat seeds, the Animated Oat Optimization algorithm provides a powerful global search capability and robust convergence behaviour. Animated Oat Optimization based Fractional order PID controllers are compared to conventional PID, Fractional order PID, Particle Swarm Optimization based Fractional order PID, etc., under identical operating conditions to validate their effectiveness. Some key transient performance measures are analyzed, including rise time, settling time, peak overshoot, steady-state error, and total harmonic distortion of the inverter output voltage. Additionally, control precision and system efficiency are evaluated using standard performance indices such as Integral Square Error, Integral Absolute Error,Integral Time Absolute Error, and Integral Time Square Error .Compared with other control techniques, the proposed Animated Oat Optimization based Fractional order PID controller achieves superior performance, with faster transient response, reduced steady-state error, and minimal total harmonic distortion, thereby improving capacitor voltage balancing and power quality in Packed U Cell Multilevel Inverter-based renewable energy systems. Also, the proposed Animated Oat Optimization based Fractional order PID controller is superior in terms of the convergence curve, as its value of the objective function reduces by 0.027 than the fitness value of Particle swarm optimization based FOPID , which is 0.047.

In this paper, design approach of PID controller with multivariable system is proposed using bacterial foraging based optimal algorithm. To tune PID controller for multivariable system, disturbance rejection conditions based on H ∞ are illustrated and the performance of response based on the bacterial foraging is computed for the designed PID controller as ITSE (Integral of time weighted squared error). Hence, parameters of PID controller are selected by bacterial foraging based optimal algorithm to obtain the required response.

The current trend is E mobilitythe reason for it is to protect or save the environment. The success of $\mathbf{E}$ mobility depends on the two important components. First one is a battery and the second is motor. Among these two, battery plays a very vital role in the $\mathbf{E}$ mobility. The cost of battery is also too high. The life span of any electric vehicle depends on the life of a battery. Battery charging and discharging cycles decide the life a battery. To have a longer life for a battery it is essential to control the charging and discharging cycles of the battery. Therefore, it becomes necessary to have a controller for controlling the charging and discharging of the battery. The Proportional Integral Derivative (PID) Controllersare in use in the industries since many years. The PID controller is simple, flexible and efficient. Proper tuning of the PID controllers, results in accurate and effective controlling actions. Many researchers have developed and discussed the tuning methods of PID controllers. To cope up with the complexities of the constantly evolving industries designers developing evolutionary algorithms like Genetic Algorithms, Simulated Annealing etc. In this paper, a simulation study is carried out by using MATLAB Simulinkto analyze the effect of battery model component on the controlling action by PID controller employing Genetic algorithm. It is found that the components of a battery model have significant effect on the performance of the battery. The Genetic algorithm helps to tune the PID controller to improve the performance.

Positioning control of liquid crystal elastomer actuator based on double closed-loop system structure

PID control is the most mature control method in classical control theory. PID control is widely used in the electromagnetic bearing control system. However, how to design PID control parameters to reduce the adjustment time and overshoot of electromagnetic bearings after disturbance is still an issue that needs to be further investigated. In this paper, the influence of "natural" stiffness and "natural" damping on system stability is evaluated to select the value range of P and D control parameters, and the value range of the I parameter is determined by using the Rouse criterion. Based on the genetic algorithm with a novel fitness function considering both the system phase margin and closed-loop bandwidth, the PID controller parameters are optimized. The simulation results show that the overshoot and the adjustment time of the genetic algorithm optimized PID controller are much smaller than that of the conventional method.

Introduction. In recent years, the use of renewable energy has become essential to preserve the climate from pollution and global warming. To utilize renewable energy more effectively, the microgrid system has emerged, which is a combination of renewable energies such as wind and solar power. However, due to sudden and random climate fluctuations, energy deviation and instability problems have arisen. To address this, storage systems and diesel engines have been incorporated. Nevertheless, this approach has led to another issue: frequency deviation in the microgrid system. Therefore, most recent studies have focused on finding ways to reduce frequency deviation. The goal of this work is to study and compare various improvement methods in terms of frequency deviation. Methodology. We first simulated the microgrid system using the PID controller based on the following algorithms: krill herd algorithm (KHA) and cuckoo search algorithm (CSA). In the second phase, we replaced the PID controller with the multi-stage PID controller and optimized its parameters using the KHA and the CSA. In the final phase, we tested the response of the microgrid system to these methods under a range of influencing factors. Results. The results initially showed the superiority of the KHA over the other algorithms in improving the parameters of the PID controller. In the second phase, the results showed a significant advantage of the multi-stage PID controller in terms of speed and stabilization time, as well as in reducing the frequency deviation compared to the PID controller. Practical value. Based on the tests conducted on the microgrid system, we can conclude that the multi-stage PID controller based on the KHA can be relied upon to solve these types of problems within the microgrid system. References 36, tables 4, figures 10.

This paper proposes the voltage/current double-loop control PWM method to depress the harmonic. After that, the PID controller's parameter design method is researched to depress the harmonic better. At last, the output current of the inverter is analyzed through the GCPV model accomplished in MATLAB considering the change of the light intensity. The result is good(the harmonic content is within the scope of the standard) and the dynamic performance of the GCPV is well when adjusting the PID controller's parameters. The simulation results prove the effectiveness and value of this paper's harmonic depression method.

— Load frequency control (LFC) plays a critical role in maintaining the stability and reliability of power systems, particularly in multi-area configurations. The primary objective of LFC is to regulate the system frequency around its nominal value while ensuring a balance between power supply and demand across interconnected areas. This objective becomes increasingly challenging as power grids grow more complex and interconnected, especially with the integration of renewable energy sources that introduce greater variability and uncertainty in generation. In this study, a PID controller is employed to stabilize system frequency during load fluctuations. The proportional (P), integral (I), and derivative (D) parameters of the controller are optimized using the Ant Colony Optimization (ACO) and Brain Storm Optimization (BSO) algorithms. Each algorithm was executed ten times within a 50-iteration framework. The results demonstrate the superior performance of the BSO algorithm in enhancing frequency stability.