Optimal PID Research Articles

Optimal PID Controller Design Based on Proportional Gain for Quarter Vehicle Model

In this study, an effective and new design method was used to determine the parameters of the PID controller used in order to improve the performance of a vehicle's active suspension system and to suppress vibrations in the vehicle. In this method, the PID controller is designed based on the optimal proportional gain kp setting, taking into account the settling time and maximum overshoot of the quarter vehicle system. This method is based on obtaining the other parameters of the controller by adjusting the kp to minimize the settling time and maximum overshoot error in a stable cycle. The obtained simulation results were evaluated by comparing the uncontrolled suspension system and the suspension system in which the PID controller whose parameters were adjusted with the proposed effective design method. It suppressed the system responses of the PID controller more effectively than the passive suspension system.

Research on DC Electronic Load System Based on PSO-PID Algorithm

At present, the control part of the DC electronic load is mainly based on analog control, that is, the control loop is controlled by an operational amplifier. Therefore, it is easy to cause slow response speed and overshoot in the process of current rise, and the improvement of response speed is of great significance to the test link. Therefore, this paper studies the digital control of DC electronic load. The DC electronic load control system has the characteristics of nonlinearity, and it is difficult to complete the theoretical modeling. Therefore, the MATLAB system identification toolbox is used to obtain the mathematical model. The traditional PID tuning method has a long time and poor effect. This paper adopts a parameter self-tuning PID controller based on particle swarm optimization algorithm. Through MATLAB simulation, the traditional PID control and particle swarm optimization PID control are simulated and compared. The results show that the digital controller has faster response speed and smaller overshoot than the conventional controller, and the system performance is significantly improved.

Research on the Control Method of Force Servo System of Liquid Hydrostatic Guide Oil Film Thickness Test Bench for Machine Tools

The test bench was designed to solve the problem of measuring the oil film thickness of the liquid hydrostatic guideway of machine tools. And based on the fast overshoot of the classical PID control, introducing the self-antidisturbance control algorithm, this study established a mathematical model of the test bench electro-hydraulic servo control system. At the same time, this paper also designed a specific structure of the self-turbulence controller, which adopts optimal quadratic control, pole configuration, PID control, and self-turbulence control to study the control methods of the test bench electro-hydraulic servo system, respectively, and uses the SIMULINK module to establish the system simulation model of the above-mentioned four control methods of the test bench. Simulation results of the test bench control system show that the response speed of the self-turbulence control is 0.4 seconds, 0.2 seconds, and 4.4 seconds faster than PID control, pole configuration, and optimal quadratic control, respectively, and the overshoot is significantly reduced. Finally, the test bench was built to complete the experiments, and the experimental results showed that the followability of the loading force and thickness detection accuracy of the test bench were better after adding the self-turbulence control algorithm and the stability of the oil film thickness was significantly improved by the test bench electro-hydraulic servo control system.

Optimal PID plus second-order derivative controller design for AVR system using a modified Runge Kutta optimizer and Bode’s ideal reference model

Optimal PID plus second-order derivative controller design for AVR system using a modified Runge Kutta optimizer and Bode’s ideal reference model

Design and Simulation of Modified Type-2 Fuzzy Logic Controller for Power System

This Article exhibits the structure of a Modified Type-2 (MT2) Fuzzy Logic (FL) Controller (MT2FLC), direction line programming of development, also performance optimization for different power systems. The implementation of the MT2FLC for control of a power system. New participation capacities were considered in adjusting a domain for an Interval Type-2 (IT2) Fuzzy Logic (FL) System (IT2FLS). Another structure in graphic user interface (GUI) mimicked four controllers: an optimal PID controller, FLC, a Type-1(TIFLC), an Interval Type-2 ((IT2FLC), and the MIT2FLC. Their yields were analysed, different periods of the structure procedure for the fuzzy framework, from beginning depiction to conclusive execution, can be gotten from the altered tool compartment (whose capacity to create complex frameworks and adaptability in broadening the accessible usefulness into working with adjusted type2 fuzzy administrators, phonetic factors, IT2 participation capacities, and defuzzification strategies, just as in assessing the MIT2FLC are its best characteristics). Case study for this work, all the optimization controllers implemented for a Brushless DC (BLDC) Motor with MATLAB Ver.2012a was utilized in the recreation and plan of the entirety of the procedure GUIs. Satisfactory results are obtaining which improve the implementation of the using of MIT2FLC controller as practical solution of power system.

Optimal PID Control for Temperature Control of Chiller Equipment

Optimal PID Control for Temperature Control of Chiller Equipment

A New Design Identification and Control Based on GA Optimization for An Autonomous Wheelchair

The daily lifestyle of an average human has changed drastically. Robotics and AI systems are applied to many fields, including the medical field. An autonomous wheelchair that improves the degree of independence that a wheelchair user has can be a very useful contribution to society. This paper presents the design and implementation of an autonomous wheelchair that uses LIDAR to navigate and perform SLAM. It uses the ROS framework and allows the user to choose a goal position through a touchscreen or using deep learning-based voice recognition. It also presents a practical implementation of system identification and optimization of PID control gains, which are applied to the autonomous wheelchair robot. Input/output data were collected using Arduino, consisting of linear and angular speeds and wheel PWM signal commands, and several black-box models were developed to simulate the actual wheelchair setup. The best-identified model was the NLARX model, which had the highest square error (0.1259) among the other candidate models. In addition, using MATLAB, Optimal PID gains were obtained from the genetic algorithm. Performance on real hardware was evaluated and compared to the identified model response. The two responses were identical, except for some of the noise due to the encoder measurement errors and wheelchair vibration.

Fast frequency relocking for synchronization enhanced resonant accelerometer

Synchronization, as a unique phenomenon, has been extensively studied in biology, chaotic systems, nonlinear dynamics, quantum information, and other fields. Benefiting from the characteristics of frequency amplification, noise suppression, and stability improvement, synchronization has been gradually applied in sensing, communication, time keeping, and other applications. In the sensing field, synchronization provides a new strategy to improve the performance of sensors. However, the performance improvement is only effective within the synchronization range, and the narrow synchronization range has become a great challenge for the wide application of synchronization-enhanced sensing mechanism. Here, we propose a frequency automatic tracking system (FATS) to widen the synchronization range and track the periodic acceleration signals by adjusting the frequency of the readout oscillator in real time. In addition, a high-precision frequency measurement system and fast response control system based on FPGA (Field Programmable Gate Array) are built, and the tracking performance of the FATS for static and dynamic external signals is analyzed to obtain the optimal control parameters. Experimental results show that the proposed automatic tracking system is capable of static acceleration measurement, the synchronization range can be expanded to 975 Hz, and the relocking time is shortened to 93.4 ms at best. By selecting the optimal PID parameters, we achieve a faster relocking time to meet the requirements of low-frequency vibration measurements, such as seismic detection and tidal monitoring.

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic neural network with adaptive learning rates. Moreover, a FO rectified linear unit (RLU) is introduced to enable the AC neural network to define and optimize its own activation function. By the means of the Lyapunov theorem, the convergence and the stability analysis of the proposed algorithm are investigated. The FO operators for the FOAC learning algorithm are obtained using the gray wolf optimization (GWO) algorithm. The effectiveness of the proposed approach is proven by extensive simulations based on the tracking problem of the two degrees of freedom (2-DOF) helicopter system and the stabilization issue of the inverted pendulum (IP) system. Moreover, the performance of the proposed algorithm is compared against optimized FOPID control approaches in different system conditions, namely when the system is subjected to parameter uncertainties and external disturbances. The performance comparison is conducted in terms of two types of performance indices, the error performance indices, and the time response performance indices. The first one includes the integral absolute error (IAE), and the integral squared error (ISE), whereas the second type involves the rising time, the maximum overshoot (Max. OS), and the settling time. The simulation results explicitly indicate the high effectiveness of the proposed FOPID-FOAC controller in terms of the two types of performance measurements under different scenarios compared with the other control algorithms.

Study on new frequency conversion control system and power balance control method of dual-motor for shearer traction unit

The drive system of shearer traction unit is affected by complex load interference and system structure, which results in the problem of unbalanced power of dual-motor of shearer traction unit. A new type of dual-motor frequency conversion control system for shearer traction unit with “common rectification and dual inverters” is designed. And a dual-motor power balance control method based on particle swarm optimization torque cross-coupled control is proposed. The new frequency conversion control system adopts a parallel structure of two inverter circuits with a common DC bus. Particle swarm optimization is used to optimize the PID control parameters of torque cross-coupling control, which solves the problem that it is difficult to gain optimal PID parameters. The control model is established using Matlab/Simulink simulation software. The proposed control method and the master-slave control method are compared by simulation. The results show that the control effect of proposed control method is better synchromization and power balance effect than the master-slave control method. The proposed control method is applied to dual-motor control of shearer traction unit for experimental verification. The results indicate that the proposed power balance control method realizes that the output torque ratio of dual-motor is between 0.9 and 1.1.

An Optimal Quantitative PID Controller Design for Ball and Beam System

In this paper, the design of two degree of freedom controller for ball and beam system is proposed. The Quantitative Feedback Theory (QFT) constraints are used to design two PID controllers to stabilize and compensate the nonlinear system with robust stability, robust performance and more desirable time response specifications. A multi objective cost function is proposed in this work to represent the function of QFT. The Black Hole Optimization (BHO) technique is a useful method to automate the design procedure of the proposed controller. The results of simulation indicate that the suggested optimal quantitative PID controller can give a desirable performance in comparison to other controllers designed in previous work.

The Hovering Stability of the Egretta Tail-Sitter VTOL UAV

Vertical takeoff and landing (VTOL) capability has extended the application of unmanned aerial vehicle (UAV) significantly. In this paper, simulation modeling and flight test were employed to investigate the hovering stability of a tail-sitter UAV named Egretta. The hovering stability simulation model was developed based on a simplified rigid body flight dynamic and the time-averaged propeller slipstream flow distribution. Meanwhile, a testing vehicle with PID controllers was built and tested to verify the hovering stability model. It was found that the Egretta UAV can achieve stable hovering in the roll, pitch, and yaw directions. The simulation model has demonstrated accuracy in predicting the hovering stability and dynamic responses with large perturbations in both trend and magnitude. Moreover, the simulation model can be extended to analyze the hovering stability of tail-sitter UAVs with different sizes. The simulation model will be very useful for initial stability sizing and PID optimization investigation.

Salp swarm algorithm based optimal speed control for electric vehicles

<p>The paper is all about the implementation of a novel bio-inspired metaheuristic<br />salp swarm algorithm (SSA) for speed control of brushless DC<br />(BLDC) motor drive that is run in sensorless control mode. The angular<br />speed of the motor is evaluated using an extended kalman filter, in which the<br />dynamics of the motor are nonlinear. The error in speeds between actual and<br />estimated is fed to the PID controller. To achieve the good transient<br />operation of the motor drive, the parameters of the PID are tuned with the<br />SSA. The optimum PID gains are determined by the minimization of integral<br />square error and then final optimum gains are validated on the laboratory<br />testbed. The proposed method is also tested in various cases to check the<br />performance of the drive. The experiments are also performed at low speeds<br />to know the superiority of the proposed method.</p>

An effective Smith predictor based fractional-order PID controller design methodology for preservation of design optimality and robust control performance in practice

In this study, an optimal and robust fractional order PID (FOPID) controller design approach is suggested for Smith predictor based FOPID (SP-FOPID) control system design. This new design approach considers continued fraction expansion (CFE) based approximate models in the optimal design task of FOPID controllers, and this approach preserves the design optimality in control applications. For this purpose, an inverse controller loop shaping design methodology is performed in order to approximate the frequency response of a Bode’s ideal loop reference model, and robust performance CFE based FOPID controller models are obtained by solving a multi-objective optimisation problem via a genetic algorithm. Thus, the suggested algorithm can deal with several controller realisation concerns in the design task of FOPID controllers and overcome real-world controller performance issues related with model approximation errors and signal saturation boundaries of electronic hardware. Illustrative design examples demonstrate that the suggested design scheme can preserve design optimality and improve practical control performance in practice.

Effectual GA Optimized PID Control Strategy based MPPT Controller for Extracting Maximum Power from Photo Voltaic system

In renewable sources, especially Photo Voltaic (PV) is actively playing an important role in both autonomous and grid-connected applications. A real challenge with any PV system is its dependency on sun’s irradiance. Hence, it needs a supporting system for obtaining its maximum output power irrespective of the weather condition. The objective of this paper is to design a Genetic Algorithm (GA) tuned PID controller for a boost converter. Such design will track possible output power from the PV system. In the proposed controller, Perturb and Observe (P&O) methodology has been used for enhancing the system output in presence of varying irradiance. The advantage of discussed control strategy is that it adequately responds with the change in irradiance value and also boosts the conversion operation efficiency. The system performance has been validated both in MATLAB simulation and also in hardware implementation with 18V and 0.5A rated solar panel. The control algorithm has been implemented using low power, low-cost PIC16F877A microcontroller.
 HIGHLIGHTS
 
 An efficient MPPT controller has been developed to track the optimal MPP for PV system
 GA optimized PID control strategy has been utilized that operates effectively and enhances PV performance efficiency
 The developed control strategy has the capability to track the optimal power at all varying irradiance conditions
 
 GRAPHICAL ABSTRACT

Optimal PID Controller Tuning for DC Motor Speed Control Using Smell Agent Optimization Algorithm

This paper presents an optimal PID controller for speed control of DC motor using Smell Agent Optimization (SAO) Algorithm. DC Motor is an energy converter which transforms electrical energy into mechanical energy used in several industrial applications. A SAO based PID is proposed for optimal tuning of the parameters of the controller to improve speed control of the motor. The SAO uses the phenomenon of smell perception and the intuitive trailing behavior of an agent to identify the source of smell. The integral of time multiplied absolute error (ITAE) is taken as the objective function for obtaining the parameters of the PID controller. It has been observed SAO-PID controller outperforms the GA based PID controller with less settling time, rise time and overshoot. Keywords— DC motor, PID controller, rising time, settling time, smell agent optimization algorithm

A new expert system for active vibration control (AVC) for high-speed train moving on a flexible structure and PID optimization using MOGA and NSGA-II algorithms

A new expert system for active vibration control (AVC) for high-speed train moving on a flexible structure and PID optimization using MOGA and NSGA-II algorithms

An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System

In recent decades, one of the scientists’ main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, an adaptive neuro-fuzzy integrated system (ANFIS) for satellite attitude estimation and control was developed. The controller was trained with the data provided by an optimal controller. Furthermore, a pulse modulator was used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the proposed controller in closed-loop simulation, an ANFIS observer was also used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor, and data gyro data. However, a new ANFIS system was proposed that can jointly control and estimate the system attitude. The performance of the proposed controller was compared to the optimal PID controller in a Monte Carlo simulation with different initial conditions, disturbance, and noise. The results show that the proposed controller can surpass the optimal PID controller in several aspects including time and smoothness. In addition, the ANFIS estimator was examined and the results demonstrate the high ability of this designated observer. Consequently, evaluating the performance of PID and the proposed controller revealed that the proposed controller consumed less control effort for satellite attitude estimation under noise and uncertainty.

PID Controller Parameter Tables for Time-Delayed Systems Optimized Using Hill-Climbing

Several parameter tables for PID controllers are known from the literature, for the control of time-delayed systems. The best known is that of Ziegler and Nichols, but there are others also. In this publication, a parameter table is presented that minimizes the quality criteria IAE, ITAE and ISE for the control of time-delayed systems that can be identified with PTn systems. The controller output limitation is also taken into account. Since it is very computationally intensive to calculate these PID parameter sets, a well-known approach from the field of artificial intelligence was chosen. This was hill-climbing, which can find the parameter sets with much less computational effort than is needed for nested loops. With this method, the PID parameter tables for the minimized quality criteria were found and compared, each on the basis of many randomized starting parameter values. The application of the hill-climbing method shows rapid convergence. The parameters are discussed with two examples of time-delayed systems: a PT3 and a PT5. The results are regulated systems which show very good transient behavior. Furthermore, it is also possible to use the procedure presented for optimal PID parameters to control general, stable systems.

Research on Temperature Control of Liposome High Pressure Homogenizer Based on Genetic Algorithm Optimization PID

To ensure quality of cosmetic liposome, the amended genetic algorithm (GA) is constructed to optimize parameters of PID controller, and proposed GA optimization PID controller is utilized to control temperature of high pressure homogenizer, Firstly, the preparation of liposome is studied, and the preparing process of cosmetic liposome is analyzed, and the effect of temperature on preparing liposome is analyzed. Secondly, the GA is improved through introducing the crossover probability and mutation probability of hyperbolic cosine adaptive GA and generating population based on chaotic theory, the improved GA is applied to optimize parameters of PID controller. Finally, a high pressure homogenizer is used as researching object to carry out temperature controlling simulation analysis, and results illustrate that established GA optimization PID controller has quicker regulation speed and higher controlling precision. The proposed controller has wide application view.