The Parameterization of All Plants Stabilized by Proportional Controller Formultiple-Input/Multiple-Output Plant

2011 ◽  
Vol 497 ◽  
pp. 246-254
Author(s):  
Takaaki Hagiwara ◽  
Kou Yamada ◽  
Satoshi Aoyama ◽  
An Chinh Hoang
Keyword(s):  
Pid Controller ◽  
Multiple Input Multiple Output ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Proportional Controller

In this paper, we examine the parameterization of all plants stabilized by a proportionalcontroller for multiple-input/multiple-output plant. A proportional controller is a kind of Proportional-Integral-Derivative (PID) controllers. PID controller structure is the most widely used one in industrialapplications. Recently, if stabilizing PID controllers for the plant exist, the parameterization of allstabilizing PID controllers has been considered. However, no paper examines the parameterizationof all plants stabilized by a PID controller. In this paper, we clarify the parameterization of all plantsstabilized by a proportional controller for multiple-input/multiple-output plant. In addition, we presentthe parameterization of all stabilizing proportional controllers for the plant stabilized by a proportionalcontroller.

The Parameterization of All Plants Stabilizied by a PID Controller for Multiple-Input/Multiple-Output Plants

2013 ◽  
Vol 596 ◽  
pp. 158-167
Author(s):  
Takaaki Hagiwara ◽  
Kou Yamada ◽  
An Chinh Hoang ◽  
Satoshi Aoyama ◽  
Huo Hui
Keyword(s):  
Pid Controller ◽  
Multiple Input Multiple Output ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Multiple Input ◽  
Input Multiple Output

In this paper, we examine the parameterization of all plants that can be stabilized bya Proportional–Integral–Derivative (PID) controller for multiple-input/multiple-output plants.The PID controller structure is the most widely used controller structure in industrial appli-cations. Recently, if stabilizing PID controllers for the plant exist, the parameterization of allstabilizing PID controllers was considered. However, the parameterization of all plants that canbe stabilized by a PID controller for multiple-input/multiple-output plants has not been exam-ined. In this paper, we clarify this question. In addition, we present the parameterization of allstabilizing PID controllers for multiple-input/multiple-output plants that can be stabilized bya PID controller.

scholarly journals Ziegler-Nichols Based Proportional-Integral-Derivative Controller for a Line Tracking Robot

2018 ◽  
Vol 9 (1) ◽  
pp. 221 ◽  
Author(s):  
Kim Seng Chia
Keyword(s):  
Pid Controller ◽  
Control Strategies ◽  
Tracking Task ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Pid Algorithm ◽  
Tracking Process ◽  
Line Tracking ◽  
Proportional Controller

<p>Line tracking robots have been widely implemented in various applications. Among various control strategies, a proportional-integral-derivative (PID) algorithm has been widely proposed to optimize the performance of a line tracking robot. However, the motivation of using a PID controller, instead of a proportional (P) or a proportional-integral (PI) controller, in a line tracking task has seldom been discussed. Particularly, the use of a systematic tuning approach e.g. closed loop Ziegler Nichols rule to optimize the parameters of a PID controller has rarely been investigated. Thus, this paper investigates the performance of P, PI, and PID controllers in a line tracking task, and the ability of Ziegler Nichols rule to optimize the parameters of the P, PI, and PID controllers. First, the ultimate gain value, K<sub>u</sub> and ultimate period of oscillation, P<sub>u</sub> were estimated using a proposed approach. Second, the values of K<sub>P</sub>, K<sub>I</sub> and K<sub>D</sub> were estimated using the Ziegler Nichols formulae. The performance of a differential wheeled robot in the line tracking task was evaluated using three different speeds. Results indicate that the Ziegler Nichols rule coupled with the proposed method is able to identify the parameters of the P, PI, and PID controllers systematically in the line tracking task. Findings indicate that the mobile robot coupled with a proportional controller achieved the best performance compared to PI and PID controllers in the line tracking process when the estimated initial parameters were used.</p>

Multiple-input and multiple-output force control for automobile component road simulation using model predictive control with proportional–integral–derivative

2021 ◽  
pp. 107754632110005
Author(s):  
Hong-Cheol Na ◽  
Hai-Bo Yuan ◽  
Gyuhae Park ◽  
Young-Bae Kim
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Multiple Input Multiple Output ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
The Road ◽  
Hybrid Controller ◽  
Multiple Input ◽  
Entire Vehicle ◽  
Input Multiple Output

When developing an entire vehicle system, testing the structure of the vehicle or each component as a module or individually is necessary to determine the reliability and ensure the endurance of the entire vehicle. Various tests have been conducted to check the durability of the parts. However, the most important part is the verification of the fatigue limit of the load vibration from the road surface when the vehicle is being driven. Verification can be achieved by experimenting while driving on a real road with a prototype vehicle best suited to the actual conditions. However, issues such as problems in time, space, and environmental constraints, inconsistency in driving characteristics of the test driver, and continuous monitoring exist. For testing the load vibration of the road surface in automobile parts in the laboratory, hydraulic servo actuators are used because they provide vibrational loads in multiple directions by configuring them in multiple axes rather than a single axis. In this article, a multiple-input multiple-output model predictive control–proportional–integral–derivative hybrid controller is proposed as the method for optimal control of a multi-axis hydraulic servo actuator used in a random road signal reproduction experiment. Its performance is compared with the simple proportional–integral–derivative controller. A method for obtaining an efficient black box multiple-input multiple-output system model using LabVIEW in a laboratory in the field is also introduced, and the effectiveness of the model predictive control–proportional–integral–derivative hybrid controller is shown by reproducing the actual road load.

scholarly journals A Design Method for Modified PID Controllers for Multiple-Input/Multiple-Output Plants

2012 ◽  
Vol 6 (2) ◽  
pp. 131-144 ◽  
Author(s):  
Takaaki HAGIWARA ◽  
Kou YAMADA ◽  
Shun MATSUURA ◽  
Satoshi AOYAMA
Keyword(s):  
Design Method ◽  
Multiple Input Multiple Output ◽  
Pid Controllers ◽  
Multiple Input ◽  
Input Multiple Output

scholarly journals Position Tracking of ML System using CSA Based PID Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 1956-1959
Keyword(s):  
Pid Controller ◽  
Magnetic Levitation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Pid Controllers ◽  
Position Tracking ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Simulation Results

The classical proportional integral derivative (PID) controllers are still use in various applications in industry. Magnetic levitation (ML) systems are rigidly nonlinear and sometimes unstable systems. Due to inbuilt nonlinearities of ML systems, tracking of position of ML Systems is still difficult. For the tracking purpose of position, PID controller parameters are found by choosing Cuckoo Search Algorithm (CSA) of optimization. The ranges of parameters are customized by z-n method of parameters. Simulation results show the tracking of position of ML systems using conventional and optimized parameters obtained with the CSA based controller.

scholarly journals Using Simplified Swarm Optimization on Multiloop Fuzzy PID Controller Tuning Design for Flow and Temperature Control System

2020 ◽  
Vol 10 (23) ◽  
pp. 8472
Author(s):  
Ting-Yun Wu ◽  
Yun-Zhi Jiang ◽  
Yi-Zhu Su ◽  
Wei-Chang Yeh
Keyword(s):  
Control System ◽  
Pid Controller ◽  
Fuzzy Theory ◽  
Multiple Input Multiple Output ◽  
Temperature Control System ◽  
Pid Controllers ◽  
Swarm Optimization ◽  
Pid Tuning ◽  
Input Multiple Output ◽  
Environment Control

This study proposes the flow and temperature controllers of a cockpit environment control system (ECS) by implementing an optimal simplified swarm optimization (SSO) fuzzy proportional-integral-derivative (PID) control. The ECS model is considered as a multiple-input multiple-output (MIMO) and second-order dynamic system, which is interactive. In this work, we use five methods to design and compare the PID controllers in MATLAB and Simulink, including Ziegler–Nicolas PID tuning, particle swarm optimization (PSO) PID, SSO PID, and the combination of the fuzzy theory with PSO PID and SSO PID, respectively. The main contribution of this study is the pioneering implementation of SSO in a fuzzy PI/PID controller. Moreover, by adding the original gain parameters Kp, Ki, and Kd in the PID controller with delta values, which are calculated by fuzzy logic designer, we can tune the parameters of PID controllers in real time. This makes our control system more accurate, adaptive, and robust.

scholarly journals Optimized Proportional-Integral-Derivative Controller for Upper Limb Rehabilitation Robot

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 826 ◽  
Author(s):  
M. Kamran Joyo ◽  
Yarooq Raza ◽  
S. Faiz Ahmed ◽  
M. M. Billah ◽  
Kushsairy Kadir ◽  
...  
Keyword(s):  
Upper Limb ◽  
Pid Controller ◽  
Degrees Of Freedom ◽  
Optimization Technique ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Effective Response ◽  
Upper Limb Rehabilitation ◽  
Proportional Integral ◽  
Limb Rehabilitation

This paper proposes a nature inspired, meta-heuristic optimization technique to tune a proportional-integral-derivative (PID) controller for a robotic arm exoskeleton RAX-1. The RAX-1 is a two-degrees-of-freedom (2-DOFs) upper limb rehabilitation robotic system comprising two joints to facilitate shoulder joint movements. The conventional tuning of PID controllers using Ziegler-Nichols produces large overshoots which is not desirable for rehabilitation applications. To address this issue, nature inspired algorithms have recently been proposed to improve the performance of PID controllers. In this study, a 2-DOF PID control system is optimized offline using particle swarm optimization (PSO) and artificial bee colony (ABC). To validate the effectiveness of the proposed ABC-PID method, several simulations were carried out comparing the ABC-PID controller with the PSO-PID and a classical PID controller tuned using the Zeigler-Nichols method. Various investigations, such as determining system performance with respect to maximum overshoot, rise and settling time and using maximum sensitivity function under disturbance, were carried out. The results of the investigations show that the ABC-PID is more robust and outperforms other tuning techniques, and demonstrate the effective response of the proposed technique for a robotic manipulator. Furthermore, the ABC-PID controller is implemented on the hardware setup of RAX-1 and the response during exercise showed minute overshoot with lower rise and settling times compared to PSO and Zeigler-Nichols-based controllers.

The Parameterization of All Plants Stabilized by a PID Controller

2013 ◽  
Vol 534 ◽  
pp. 173-181 ◽  
Author(s):  
Takaaki Hagiwara ◽  
Kou Yamada ◽  
An Chinh Hoang ◽  
Satoshi Aoyama
Keyword(s):  
Pid Controller ◽  
Industrial Applications ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Proportional Integral

In this paper, we examine the parameterization of all plants that can be stabilizedby a Proportional–Integral–Derivative (PID) controller. The PID controller structure is themost widely used controller structure in industrial applications. Recently, if stabilizing PIDcontrollers for the plant exist, the parameterization of all stabilizing PID controllers was considered.However, the parameterization of all plants that can be stabilized by a PID controllerhas not been examined. In this paper, we clarify this question. In addition, we present theparameterization of all stabilizing PID controllers for plants that can be stabilized by a PIDcontroller.

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