Performance-Adaptive Generalized Predictive Control-Based Proportional-Integral-Derivative Control System and Its Application

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Takao Sato ◽  
Toru Yamamoto ◽  
Nozomu Araki ◽  
Yasuo Konishi
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Pid Control ◽  
Design Method ◽  
Design Parameters ◽  
Generalized Predictive Control ◽  
Control Performance ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control

In the present paper, we discuss a new design method for a proportional-integral-derivative (PID) control system using a model predictive approach. The PID compensator is designed based on generalized predictive control (GPC). The PID parameters are adaptively updated such that the control performance is improved because the design parameters of GPC are selected automatically in order to attain a user-specified control performance. In the proposed scheme, the estimated plant parameters are updated only when the prediction error increases. Therefore, the control system is not updated frequently. The control system is updated only when the control performance is sufficiently improved. The effectiveness of the proposed method is demonstrated numerically. Finally, the proposed method is applied to a weigh feeder, and experimental results are presented.

MIMO PREDICTIVE PID CONTROL: A PRACTICAL APPROACH FOR QUADRUPLE TANK

2013 ◽  
Vol 22 (03) ◽  
pp. 1350009 ◽  
Author(s):  
QAMAR SAEED ◽  
VALI UDDIN ◽  
REZA KATEBI
Keyword(s):  
Control System ◽  
System Design ◽  
Predictive Control ◽  
Generalized Predictive Control ◽  
Control System Design ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Tank System ◽  
Proportional Integral Controller ◽  
And Performance

In this paper, predictive control has been implemented by several methods in a most simplified manner for quadruple tank system, i.e., a multi-input multi-output control problem. Quadruple tank system is a nonlinear system which can be adjusted to obtain two different class of system i.e., minimum and nonminimum phase, therefore tuning for each phase of system has to be tackled separately. Control system design has been obtained by manual proportional-integral controller, traditional generalized predictive control, predictive proportional-integral-derivative on the basis of generalized predictive control, predictive iterative learning control and finally adaptive predictive proportional-integral-derivative control has been developed successfully. Stability and performance of the each control system design has also been discussed.

Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor

2020 ◽  
Vol 26 (17-18) ◽  
pp. 1574-1589
Author(s):  
Mohammad Javad Mahmoodabadi ◽  
Nima Rezaee Babak
Keyword(s):  
Control System ◽  
Sliding Surface ◽  
Proportional Integral Derivative ◽  
Adaptation Mechanism ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Grasshopper Optimization Algorithm ◽  
Proportional Integral Derivative Control ◽  
Grasshopper Optimization ◽  
Fuzzy Adaptive

Proportional–integral–derivative is one of the most applicable control methods in industry. Although it is simple and effective in most cases, it does not provide robustness against disturbances and may not perform well in cases with uncertainties and nonlinearities. In this study, a fuzzy adaptive robust proportional–integral–derivative controller is used to control a nonlinear 4 degree-of-freedom quadrotor. An adaptation mechanism is submitted to the proportional–integral–derivative controller for updating the proportional, derivative, and integral gains of proportional–integral–derivative control. Furthermore, a sliding surface is generated and submitted to the adaptation mechanism for better regulation of proportional–integral–derivative gains. Afterward, a fuzzy engine is applied to regulate the sliding surface for better performance of the adaptive proportional–integral–derivative when there are disturbance and uncertainties. The multi-objective grasshopper optimization algorithm is implemented on the control system for the regulation of the control system parameters to minimize the error and control effort of the proposed hybrid control system. Finally, the obtained results are presented for a nonlinear 4 degree-of-freedom multi-purpose (for marine, ground, and aerial maneuvers) quadrotor system designed and built in Sirjan University of Technology, Sirjan, Iran, to assure the effectiveness of this technique.

scholarly journals Analysis and Simulation of Proportional Derivative and Proportional Integral Derivative Control Systems Using Xcos Scilab

2021 ◽  
Vol 3 (1) ◽  
pp. 36-44
Author(s):  
Edi Kurniawan
Keyword(s):  
Pid Control ◽  
System Response ◽  
Response Analysis ◽  
System Control ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Pid Tuning ◽  
Proportional Integral Derivative Control ◽  
Tuning Process ◽  
Derivative Control

PID (Proportional Integral Derivative) control is a popular control in the industry and aims to improve the performance of a system. This control has controlling parameters, namely Kp, Ki, and Kd which will have a control effect on the overall system response. In this research, P, PD, and PID control simulations with the transfer function of the mass-damper spring as a plant using Xcos Scilab. The method used is the trial and error method by setting and varying the values of the control constants Kp, Ki, and Kd to produce the desired system response. The value adjustment of system control parameters is carried out with several variations, namely Kp control variation, Kp variation to constant Kd, Kd variation to constant Kp, Kp variation to Ki, constant Kd, variation of Ki to Kp, constant Kd and variation of Kd to Kp, Ki constant. The second method is automatic tuning which is done through mathematical calculations to obtain PID control constants, namely Zieglar Nichols PID tuning with the oscillation method. From the system simulation results, the best parameter is obtained through the Zieglar Nichols PID tuning process based on the results of the transient response analysis, namely when the proportional gain value (Kp) is 50. The system performance characteristics produced in the tuning process are 3.994 seconds of settling time at 2.36 seconds research time. resulting in a maximum overshoot value of 3.6% and a peaktime value of 3.994 seconds

Synthesis of a Proportional Integral Derivative control law based on the Meixner-like model

2018 ◽  
Vol 41 (3) ◽  
pp. 780-792 ◽  
Author(s):  
Safa Maraoui ◽  
Kais Bouzrara ◽  
José Ragot
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Variable Parameter ◽  
Control Law ◽  
Laguerre Functions ◽  
Proportional Integral Derivative ◽  
Parameter System ◽  
Control Approach ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control

This paper proposes a new Proportional Integral Derivative (PID) control algorithm based on the Meixner-like model. The Meixner-like functions are an extension of Laguerre functions and are convenient when the system has a slow start. The parameters of the PID controller are auto adjusted by the model predictive control (MPC) technique. To improve system performance, the resolution of the problem of saturation of the control signal is proposed. A numerical example of a variable parameter system is given to illustrate the effectiveness of the proposed control approach.

Simulation and adaptive control of back propagation neural network proportional–integral–derivative for special launcher using new version of transfer matrix method for multibody systems

2019 ◽  
Vol 26 (9-10) ◽  
pp. 757-768 ◽  
Author(s):  
Yunfei Miao ◽  
Guoping Wang ◽  
Xiaoting Rui
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Transfer Matrix Method ◽  
Matrix Method ◽  
Multibody Systems ◽  
Proportional Integral Derivative ◽  
Backpropagation Neural Network ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control

Rocket launcher system, as a special launcher placed on tactical vehicles, is a very complex mechanical system with characteristics of strong shock and vibration. In order to improve position accuracy, as well as reduce vibration, this paper creates a nonlinear dynamics model of the launcher system by using a new version of the transfer matrix method for multibody systems. The overall transfer equation of the nonlinear model is deduced. Combining with general kinematics equations of the rocket, the system launch dynamics are simulated and compared with experiments to verify the correctness of the model. On this basis, a backpropagation neural network proportional–integral–derivative adaptive control system is designed to improve servo control of the launcher. Then, the effectiveness of this method is verified by comparing with the traditional proportional–integral–derivative control method. Simulated results show that the backpropagation neural network proportional–integral–derivative control system makes the azimuth and elevation angles reach the target values smoothly and quickly, with higher accuracy. The results prove that the proposed method prominently reduces vibrations of the launcher, by adjusting the control parameters online according to the operation state of the system, presenting a better stability and robustness.

Dynamic Simulation of Human Gait Using a Combination of Model Predictive and PID Control

2014 ◽  
Author(s):  
Jinming Sun ◽  
Philip A. Voglewede
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Pid Control ◽  
Muscle Activity ◽  
A Priori ◽  
Human Gait ◽  
Proportional Integral Derivative ◽  
Biomechanical Models ◽  
Long Period ◽  
The Central Nervous System

Human gait studies have not been applied frequently to the prediction of the performance of medical devices such as prostheses and orthoses. The reason is most biomechanics simulations require experimental data such as muscle activity or joint moment information a priori. In addition, biomechanical models are normally too complicated to be adjusted and these simulations normally take a long period of time to be performed which makes testing of various possibilities time consuming; therefore they are not suitable for prediction purpose. The objective of this research is to develop a control oriented human gait model that is able to predict the performance of prostheses and orthoses before they are experimentally tested. This model is composed of two parts. The first part is a seven link nine degree-of-freedom (DOF) plant to represent the forward dynamics of human gait. The second part is a control system which is a combination of Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control. The purpose of this control system is to simulate the central nervous system (CNS). This model is sufficiently simple that it can be simulated and adjusted in a reasonable time, while still representing the essential principles of human gait.

Sign in / Sign up

Export Citation Format

Share Document