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.

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.

scholarly journals Neural Network Control for the Probe Landing Based on Proportional Integral Observer

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yuanchun Li ◽  
Tianhao Ma ◽  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Landing Site ◽  
Network Control ◽  
Control Law ◽  
Neural Network Control ◽  
Control Approach ◽  
Proportional Integral ◽  
The Neural Network

For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

scholarly journals Slip control during inertia phase of clutch-to-clutch shift using model-free self-tuning proportional-integral-derivative control

2020 ◽  
Vol 234 (9) ◽  
pp. 2279-2290
Author(s):  
Shuichi Yahagi ◽  
Itsuro Kajiwara ◽  
Tomoaki Shimozawa
Keyword(s):  
Fuel Efficiency ◽  
Control Law ◽  
Proportional Integral Derivative ◽  
Slip Control ◽  
Proportional Integral ◽  
Tuning Method ◽  
Model Free ◽  
Self Tuning ◽  
Proportional Integral Derivative Control ◽  
Controlled Object

Transmissions require a good shift feeling and improved fuel efficiency. In state-of-the-art stepped automated transmissions, the number of gear stages increases, and the lock-up area is expanded to improve fuel efficiency. However, this makes it difficult to obtain a good shift feeling and it takes a large number of calibration man-hours. Therefore, to reduce the number of calibration man-hours and improve the shift feeling, we propose a slip control law between the engine and the clutch, which is composed of a proportional-integral-derivative (PID) controller and a disturbance observer. Moreover, PID gain is adjusted online by installing an automatic tuning method, which does not require a controlled object model. The effects of the proposed method are verified via an experiment using an actual vehicle. The experimental results show that the proposed method is effective for automatically adjusting PID gain and improving the shift feeling of the stepped automated transmission.

A new fractional sliding mode controller based on nonlinear fractional-order proportional integral derivative controller structure to synchronize fractional-order chaotic systems with uncertainty and disturbances

2021 ◽  
pp. 107754632098245
Author(s):  
Seyede Zeynab Mirrezapour ◽  
Assef Zare ◽  
Majid Hallaji
Keyword(s):  
Fractional Order ◽  
Chaotic Systems ◽  
Sliding Mode ◽  
Control Law ◽  
Sliding Mode Controller ◽  
Proportional Integral Derivative ◽  
Lyapunov Theorem ◽  
Control Approach ◽  
Proportional Integral ◽  
Bounded Disturbance

This study presents a new fractional sliding mode controller based on nonlinear fractional-order proportional integral derivative controllers to synchronize fractional-order chaotic systems with uncertainties and affected by disturbance. According to the proposed control approach, a new fractional order control law is presented which ensures robust and stable synchronization of chaotic systems in the presence of uncertainties of the master and slave systems and bounded disturbance according to Lyapunov theorem. The proposed sliding mode controller is used to synchronize two non-smooth chaotic jerk systems affected by disturbance and uncertainty. Simulation results verify effectiveness and robustness of the proposed control law.

Precise variable spraying system based on improved genetic proportional-integral-derivative control algorithm

2021 ◽  
pp. 014233122110224
Author(s):  
Yanlei Xu ◽  
Xindong Wang ◽  
Yuting Zhai ◽  
ChenXiao Li ◽  
Zongmei Gao
Keyword(s):  
Weed Management ◽  
Control Algorithm ◽  
Field Tests ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control ◽  
Selection Operator ◽  
Adaptive Crossover ◽  
Processing Subsystem ◽  
Derivative Control

Currently, the most efficient method of resolving the pollution problem of weed management is by using variable spraying technology. In this study, an improved genetic proportional-integral-derivative control algorithm (IGA-PID) was developed for this technology. It used a trimmed mean operator to optimize the selection operator for an improved searching rate and accuracy. An adaptive crossover operator and mutation operator were constructed for a rapid convergence speed. The weed density detection was performed through an image acquisition and processing subsystem which was capable of determining the spraying quantity. The variable spraying control sub-system completed variable spraying operation. The performance of the system was evaluated by simulations and field tests, and compared with conventional methods. The simulation results indicated that the parameters of the overshoot (1.25%), steady-state error (1.21%) and the adjustment time (0.157s) of IGA-PID were the lowest when compared with the standard algorithms. Furthermore, the field validation results showed that the system with the proposed algorithm achieved the optimal performance with spraying quantity error being 2.59% and the respond time being 3.84s. Overall, the variable spraying system based on an IGA-PID meets the real-time and accuracy requirements for field applications which could be helpful for weed management in precise agriculture.

Study on the visual tracking control technology of six-crawler machine

2019 ◽  
Vol 233 (17) ◽  
pp. 6051-6075
Author(s):  
Shuai Wang ◽  
Haoran Ge ◽  
Ruoding Ma ◽  
Da Cui ◽  
Xinhui Liu ◽  
...  
Keyword(s):  
Control System ◽  
Visual Tracking ◽  
Tracking Control ◽  
Control Algorithm ◽  
Least Square ◽  
Control Parameters ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control ◽  
Derivative Control

In this paper, the autonomous navigation of six-crawler machine is studied, and a visual tracking control method based on machine vision for fuzzy proportional–integral–derivative control of six-crawler machine is proposed. The steering principle of the six-crawler machine and the matching relationship between the steering angle and the speed of each crawler are introduced, and the control system is described in detail. Besides, the mathematical model for the unsteady steering is introduced to analyze the influence of deflection angle on the steering trajectory of the six-crawler machine. The image processing algorithm is programmed by LabVIEW software. After the image is fitted by graying, binary, filtering, edge detection, and least square method, the navigation line-fitting curve is obtained. The fuzzy proportional–integral–derivative control algorithm is programmed in the control system to control the six-crawler machine to drive along the navigation line. In order to obtain reasonable control parameters, a virtual prototype model of a six-crawler machine is established. In the CoLink module, the control algorithm of a six-crawler machine is established, and the co-simulation is carried out. By analyzing the simulation results, the control parameters of the fuzzy proportional–integral–derivative controller of the six-crawler machine are established. In order to verify the control effect of the visual tracking control system of the six-crawler machine, a physical prototype of the six-crawler machine is constructed and tested. The results show that the visual tracking control system of the six-crawler machine can complete the preset functions.

Adaptive PID Controller Based on Single Neuron

2012 ◽  
Vol 466-467 ◽  
pp. 981-985 ◽  
Author(s):  
Xin Yun Qiu ◽  
Yuan Gao
Keyword(s):  
Pid Controller ◽  
Control Algorithm ◽  
Single Neuron ◽  
Variable Parameter ◽  
Fast Response ◽  
Control Law ◽  
Parameters Tuning ◽  
Application Condition ◽  
Simulation And Experiment ◽  
New Type

An adaptive PID controller based on single neuron is proposed. The properties, control algorithm, parameters tuning, the control law and the application condition of the controller are studied in the paper. To satisfy the properties of the requirements of the control system in an electromotor group, such as a broad dynamic changing range, a fast response, a little overshoot and time-variable parameter, a new-type self-optimizing PID controller based on artificial neural networks is proposed and studied. It is verified that the controller has few adjustable parameters and excellent robust performance. The results of simulation and experiment prove that the controller is superior to the traditional PID controller.

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