scholarly journals Intelligent Positioning Plate Predictive Control and Concept of Diagnosis System Design

2017 ◽  
Vol 15 (1-2) ◽  
Author(s):  
Matej Oravec ◽  
Anna Jadlovská
Keyword(s):  
Artificial Intelligence ◽  
Distributed Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Control Algorithms ◽  
Laboratory Model ◽  
Distributed Control System ◽  
Diagnosis System ◽  
Intelligent Positioning ◽  
Algorithm Verification

<span lang="EN-US">This paper presents design of the predictive control algorithms, which are verified using the simulation and laboratory model of the Intelligent Positionig Plate. The results of the predictive control algorithm verification are presented also in this article. The created tool called the <em>IPPtools</em> is based on the designed predictive control algorithms and it is shortly presented. A part of the paper is dedicated to the concept of the diagnosis system, which is designed and implemented into the 5-level Distributed Control System of the Department of the Cybernetics and Artificial Intelligence. Possibility how to modify the predictive control algorithms using diagnosis system is also stated in the last section of this paper.</span>

Research on Accurate Modeling and Control for Pneumatic Electric Braking System of Commercial Vehicle Based on Multi-Dynamic Parameters Measurement

2020 ◽  
Author(s):  
Yongtao Zhao ◽  
Yiyong Yang ◽  
Xiuheng Wu ◽  
Xingjun Tao
Keyword(s):  
Dynamic Response ◽  
Real Time ◽  
Predictive Control ◽  
Control Algorithm ◽  
Control Algorithms ◽  
Dynamic Parameters ◽  
Braking System ◽  
Control Scheme ◽  
Braking Force ◽  
Electric Braking

Abstract Accurate pressure control and fast dynamic response are vital to the pneumatic electric braking system (PEBS) for that commercial vehicles require higher regulation precision of braking force on four wheels when braking force distribution is carried out under some conditions. Due to the lagging information acquisition, most feedback-based control algorithms are difficult to further improve the dynamic response of PEBS. Meanwhile, feedforward-based control algorithms like predictive control perform well in improving dynamic performance. but because of the large amount of computation and complexity of this kind of control algorithm, it cannot be applied in real-time on single-chip microcomputer, and it is still in the stage of theoretical research at present. To address this issue and for the sake of engineering reliability, this article presents a logic threshold control scheme combining analogous model predictive control (AMPC) and proportional control. In addition, an experimental device for real-time measuring PEBS multi-dynamic parameters is built. After correcting the key parameters, the precise model is determined and the influence of switching solenoid valve on its dynamic response characteristics is studied. For the control scheme, numerical and physical validation are executed to demonstrate the feasibility of the strategy and for the performance of the controller design. The experimental results show that the dynamic model of PEBS can accurately reflect its pressure characteristics. Furthermore, under different air source pressures, the designed controller can stably control the pressure output of PEBS and ensure that the error is within 8KPa. Compared with the traditional control algorithm, the rapidity is improved by 32.5%.

scholarly journals Self-Tuning Predictive Control of Nonlinear Servo-Motor

2010 ◽  
Vol 61 (6) ◽  
pp. 365-372 ◽  
Author(s):  
Vladimír Bobál ◽  
Petr Chalupa ◽  
Marek Kubalčík ◽  
Petr Dostál
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Laboratory Model ◽  
Servo Motor ◽  
Real Time Control ◽  
Highly Nonlinear ◽  
Model Control ◽  
Self Tuning

Self-Tuning Predictive Control of Nonlinear Servo-MotorThe paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model — Amira DR300.

scholarly journals Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8493
Author(s):  
Adnan Khalid ◽  
Mujtaba Hussain Jaffery ◽  
Muhammad Yaqoob Javed ◽  
Adnan Yousaf ◽  
Jehangir Arshad ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Constrained Optimization ◽  
Predictive Control ◽  
Control Algorithm ◽  
Computational Time ◽  
Control Algorithms ◽  
Control Framework ◽  
Output Constraints ◽  
Powered Descent ◽  
Stated Problem

It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem; however, the predictive control algorithm with constraint handling’s ability has not been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of Explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB.

scholarly journals Design and Implementation of the Universal Servo Control Algorithm Verification System Based on High-Speed Communication Fieldbus

2021 ◽  
Vol 25 (2) ◽  
pp. 248-257
Author(s):  
Yonghua Xiong ◽  
Ke Li ◽  
Zhen-Tao Liu ◽  
Jinhua She ◽  
◽  
...  
Keyword(s):  
High Speed ◽  
Control Algorithm ◽  
Experimental System ◽  
Servo Control ◽  
Theoretical Research ◽  
Control Algorithms ◽  
Master Program ◽  
Theoretical Simulation ◽  
Industrial Sites ◽  
Algorithm Verification

In recent years, there have been several breakthroughs in the theoretical research of servo control algorithms. However most of these control algorithms remain in the simulation stage. They are difficult to be applied directly to practical platforms or complex industrial sites because of the lack of an experimental system suitable for the verification of their effectiveness. To address this problem, we designed a multi-function servo control algorithm verification experiment system (MVES) within the MATLAB/Simulink theoretical simulation model directly to communicate with the TwinCAT 3 PLC master program to perform different servo control experiments. The MVES supports various Simulink models. However, its and the operation is simple and convenient, which greatly reduces the workload of the algorithm test and has important practical value. Two sets of comparative experiments were used to verify the versatility and superiority of MVES.

scholarly journals Simulation of electric heating prediction model by internet of things technology and room thermal performance analysis

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3139-3147
Author(s):  
Na Qu ◽  
Wen You
Keyword(s):  
Control System ◽  
Performance Analysis ◽  
Internet Of Things ◽  
Distributed Control ◽  
Predictive Control ◽  
Electric Heating ◽  
Control Technology ◽  
Distributed Control System ◽  
Three Phase ◽  
Differential Control

The fault diagnosis and fault-tolerant control of electric heating distributed control system are improved by the thermal performance analysis of rooms. The given values are tracked to meet the heating requirements, and the reliability of heating is increased without increasing hardware resources, which improves the reliability and economy of electric heating. From the perspective of energy conservation of electric heating for buildings and rooms, a predictive control model based on load-side three-phase power self-balance is proposed. A fault tolerance method for the electric heating distributed control system control system heating is designed. The load-side three-phase power self-balancing method of the electric heating control system is implemented by using the advantages of the Internet of Things and the heat storage performance of a room, which is its characteristics. Simulation results show that the performance of predictive control for non-minimum phase process is significantly better than that of conventional proportion integral differential control. For complex control problems, predictive control technology can provide better control performance than proportion integral differential control technology. Without increasing any hardware resources, reliable and economical heating is achieved through software.

Implementasi DCS (Distributed Control System) Sebagai Pengontrol Suhu Pada Proses Pemanasan Air Dengan Metode MPC (Model Predictive Control)

2021 ◽  
Vol 6 (3) ◽  
pp. 17
Author(s):  
Mahgarita Tri Kurniawati ◽  
Tarmukan Tarmukan ◽  
Muhamad Rifa'i
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Distributed Control ◽  
Predictive Control ◽  
Distributed Control System ◽  
Heat Control

Implementasi DCS sebagai pengontrol suhu pada proses pemanasan air dengan metode MPC pada laboratorium sistem kendali proses bertujuan untuk memprediksi output proses selama rentang waktu tertentu dengan meminimalisasi fungsi objek, dengan memanfaatkan HMI yang berfungsi untuk melakukan tugas monitoring pada sistem water heat control.Pengujian yang dilakukan pada pengimplementasian DCS sebagai pengontrol suhu air menggunakan metode MPC ini adalah pengujian sistem dengan beberapa variasi nilai setting point yang diberikan. Berdasarkan hasil pengujian sistem yang dilakukan, menunjukkan bahwa proses implementasi metode kontrol MPC yang terdapat dalam perangkat DCS pada sistem water heat control telah berhasil dilakukan. Sistem dapat mempertahankan suhu air dalam tangki sesuai dengan nilai setting point yang diberikan dengan rata-rata maximum overshoot sebesar 1,92%. Dengan demikian, alat ini dapat digunakan sebagai bahan pembelajaran tentang simulasi water heat control dan penggunaan metode MPC dalam DCS oleh mahasiswa Teknik Elektronika, Politeknik Negeri Malang.

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