scholarly journals Pneumatic positioning control system using constrained model predictive controller: Experimental repeatability test

2021 ◽  
Vol 11 (5) ◽  
pp. 3913
Author(s):  
Siti Fatimah Sulaiman ◽  
M. F. Rahmat ◽  
Ahmad Athif Faudzi ◽  
Khairuddin Osman ◽  
S. I. Samsudin ◽  
...  
Keyword(s):  
Fast Response ◽  
Pneumatic System ◽  
Positioning System ◽  
Model Predictive Controller ◽  
Positioning Control ◽  
Environment Model ◽  
Identification Technique ◽  
Predictive Controller ◽  
Functional Control ◽  
The Mathematical Model

Most of the controllers that were proposed to control the pneumatic positioning system did not consider the limitations or constraints of the system in their algorithms. Non-compliance with the prescribed system constraints may damage the pneumatic components and adversely affect its positioning accuracy, especially when the system is controlled in real-time environment. Model predictive controller (MPC) is one of the predictive controllers that is able to consider the constraint of the system in its algorithm. Therefore, constrained MPC (CMPC) was proposed in this study to improve the accuracy of pneumatic positioning system while considering the constraints of the system. The mathematical model of pneumatic system was determined by system identification technique and the control signal to the valves were considered as the constraints of the pneumatic system when developing the controller. In order to verify the accuracy and reliability of CMPC, repetitive experiments on the CMPC strategy was implemented. The existing predictive controller, that was used to control the pneumatic system such as predictive functional control (PFC), was also compared. The experimental results revealed that CMPC effectively improved the position accuracy of the pneumatic system compared to PFC strategy. However, CMPC not capable to provide a fast response as PFC.

scholarly journals Enhancement in pneumatic positioning system using nonlinear gain constrained model predictive controller: experimental validation

2021 ◽  
Vol 23 (3) ◽  
pp. 1385
Author(s):  
Siti Fatimah Sulaiman ◽  
M. F. Rahmat ◽  
Ahmad Athif Faudzi ◽  
Khairuddin Osman ◽  
N. H. Sunar
Keyword(s):  
Positioning System ◽  
Nonlinear Gain ◽  
Model Predictive Controller ◽  
New Approach ◽  
Positioning Control ◽  
Positioning Systems ◽  
Predictive Controller ◽  
Identification Approach ◽  
Functional Control ◽  
Speed Response

The issues of inaccurate positioning control have made an industrial use of pneumatic actuator remains restricted to certain applications only. Non-compliance with system limits and properly control the operating system may also degrade the performance of pneumatic positioning systems. This study proposed a new approach to enhance pneumatic positioning system while considering the constraints of system. Firstly, a mathematical model that represented the pneumatic system was determined by system identification approach. Secondly, model predictive controller (MPC) was developed as a primary controller to control the pneumatic positioning system, which took into account the constraints of the system. Next, to enhance the performance of the overall system, nonlinear gain function was incorporated within the MPC algorithm. Finally, the performances were compared with other control methods such as constrained MPC (CMPC), proportional-integral (PI), and predictive functional control with observer (PFC-O). The validation based on real-time experimental results for 100 mm positioning control revealed that the incorporation of nonlinear gain within the MPC algorithm improved 21.03% and 2.69% of the speed response given by CMPC and PFC-O, and reduced 100% of the overshoot given by CMPC and PI controller; thus, providing fast and accurate pneumatic positioning control system.

scholarly journals Anti-Windup Scheme for Practical Control of Positioning Systems

1970 ◽  
Vol 5 (2) ◽  
Author(s):  
Wahyudi Tarig Faisal and Abdulgani Albagul
Keyword(s):  
Controller Design ◽  
Actuator Saturation ◽  
Fast Response ◽  
High Accuracy ◽  
Positioning System ◽  
Practical Application ◽  
Positioning Control ◽  
Positioning Systems ◽  
Trajectory Following ◽  
Point To Point

Positioning systems generally need a good controller to achieve high accuracy, fast response and robustness. In addition, ease of controller design and simplicity of controller structure are very important for practical application.  For satisfying these requirements, nominal characteristic trajectory following controller (NCTF) has been proposed as a practical point-to-point (PTP) positioning control. However, the effect of actuator saturation can not be completely compensated for due to the integrator windup as the plant parameters vary. This paper presents a method to improve the NCTF controller for overcoming the problem of integrator windup using simple and classical tracking anti-windup scheme. The improved NCTF controller is evaluated through simulation using a rotary positioning system. The results show that the improved NCTF controller is adequate to compensate for the effect of integrator windup. Keywords: Positioning, point-to-point, integrator windup, compensation, controller, robustness.

Direct Current Deadbeat Predictive Controller for BLDC Motor Using Single DC-Link Current Sensor

2020 ◽  
Vol 38 (8A) ◽  
pp. 1187-1199
Author(s):  
Qaed M. Ali ◽  
Mohammed M. Ezzalden
Keyword(s):  
Control System ◽  
High Speed ◽  
Fast Response ◽  
Bldc Motor ◽  
Current Controller ◽  
Three Phase ◽  
Predictive Controller ◽  
Two Phases ◽  
Dc Current ◽  
Three Phase Inverter

BLDC motors are characterized by electronic commutation, which is performed by using an electric three-phase inverter. The direct control system of the BLDC motor consists of double loops; including the inner-loop for current regulating and outer-loop for speed control. The operation of the current controller requires feedback of motor currents; the conventional current controller uses two current sensors on the ac side of the inverter to measure the currents of two phases, while the third current would be accordingly calculated. These two sensors should have the same characteristics, to achieve balanced current measurements. It should be noted that the sensitivity of these sensors changes with time. In the case of one sensor fails, both of them must be replaced. To overcome this problem, it is preferable to use one sensor instead of two. The proposed control system is based on a deadbeat predictive controller, which is used to regulate the DC current of the BLDC motor. Such a controller can be considered as digital controller mode, which has fast response, high precision and can be easily implemented with microprocessor. The proposed control system has been simulated using Matlab software, and the system is tested at a different operating condition such as low speed and high speed.

Enlarging the region of stability in robust model predictive controller based on dual-mode control

2021 ◽  
pp. 014233122110123
Author(s):  
Fatemeh Khani ◽  
Mohammad Haeri
Keyword(s):  
Stability Region ◽  
Linear Matrix ◽  
Input State ◽  
Dual Mode ◽  
Model Predictive Controller ◽  
Mode Control ◽  
Robust Model ◽  
Output Constraints ◽  
Predictive Controller ◽  
The Stability

Industrial processes are inherently nonlinear with input, state, and output constraints. A proper control system should handle these challenging control problems over a large operating region. The robust model predictive controller (RMPC) could be an linear matrix inequality (LMI)-based method that estimates stability region of the closed-loop system as an ellipsoid. This presentation, however, restricts confident application of the controller on systems with large operating regions. In this paper, a dual-mode control strategy is employed to enlarge the stability region in first place and then, trajectory reversing method (TRM) is employed to approximate the stability region more accurately. Finally, the effectiveness of the proposed scheme is illustrated on a continuous stirred tank reactor (CSTR) process.

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