Research on a novel PID based controller for nonmagnetic hydraulic navigation simulator with AMESim simulation

Wang, Xuyong; Tao, Jianfeng; Guo, Xiaojun; Xu, Chuanhui

doi:10.1109/control.2012.6334680

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The valve-controlled hydraulic motor speed servo system, known for its quick, accurate, and efficient response due to the high-frequency response of the servo valve, is typically used in low-and medium-power applications requiring excellent accuracy. The authors in [31] have proposed a controller design utilising a feedforward PIDD 2 controller for the valve-controlled hydraulic motor system, which shows better performance in position tracking capability when simulated and compared to PID, PIDD, and differential PID controllers. It is clear from the results that the PIDD 2 controller design is a promising option for controlling various systems, providing better performance than traditional PID controllers.…”

Section: Control System Applicationsmentioning

confidence: 99%

Design of PIDDα Controller for Robust Performance of Process Plants

Fawwaz,

Bingi,

Ibrahim

et al. 2023

Algorithms

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Managing industrial processes in real-time is challenging due to the nonlinearity and sensitivity of these processes. This unpredictability can cause delays in the regulation of these processes. The PID controller family is commonly used in these situations, but their performance is inadequate in systems and surroundings with varying set-points, longer dead times, external noises, and disturbances. Therefore, this research has developed a novel controller structure for PIDDα that incorporates the second derivative term from PIDD2 while exclusively using fractional order parameters for the second derivative term. The controllers’ robust performance has been evaluated on four simulation plants: first order, second order with time delay, third-order magnetic levitation systems, and fourth-order automatic voltage regulation systems. The controllers’ performance has also been evaluated on experimental models of pressure and flow processes. The proposed controller exhibits the least overshoot among all the systems tested. The overshoot for the first-order systems is 9.63%, for the third-order magnetic levitation system, it is 12.82%, and for the fourth-order automatic voltage regulation system, it is only 0.19%. In the pressure process plant, the overshoot is only 4.83%. All controllers for the second-order systems have a time delay, while the flow process plant has no overshoot. The proposed controller demonstrates superior settling times in various systems. For first-order systems, the settling time is 14.26 s, while in the pressure process plant, the settling time is 8.9543 s. Similarly, the proposed controllers for the second-order system with a time delay and the flow process plant have the same settling time of 46.0495 s. In addition, the proposed controller results in the lowest rise time for three different systems. The rise time is only 0.0075 s for the third-order magnetic levitation system, while the fourth-order automatic voltage regulation system has a rise time of 0.0232 s. Finally, for the flow process plant, the proposed controller has the least rise time of 25.7819 s. Thus, in all the cases, the proposed controller results in a more robust controller structure that provides the desired performance of a regular PIDD2 controller, offering better dynamic responses, shorter settling times, faster rise times, and reduced overshoot. Based on the analysis, it is evident that PIDDα outperforms both PID and FOPID control techniques due to its ability to produce a more robust control signal.

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Section: Control System Applicationsmentioning

confidence: 99%

Design of PIDDα Controller for Robust Performance of Process Plants

Fawwaz,

Bingi,

Ibrahim

et al. 2023

Algorithms

View full text Add to dashboard Cite

show abstract

“…The model is created with exchangeable parameters in processing interface. Adaptive control, Proportional-Integral-Derivative (P-I-D) control, and Fuzzy control are mostly used to control hydraulic systems and robotic manupulator as mentioned in Zhou et al [11] and Shakya et al [12]. In addition, forward kinematic and inverse kinematic expressions that perform dynamic ability of RRRRRR industrial robotic manipulator are also mentioned in this paper.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis and Control of Hydraulic Machine System and Industrial Robotic Manipulators

Hwang

Tran

2018

AMM

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Dynamic control on hydraulic machine system and kinematic control on industrial robotic manipulators are two studied topics in this research. The main objective of this study is to analyze dynamic, forward kinematic and inverse kinematic on a couple of mechanical systems and hydraulic mechanical systems in order to control these machines. The characteristics of hydraulic and manipulator robot parameters are firstly calculated by using dynamic theories. In the former topic, we perform an example on CNC machine tools which is designing a hydraulic controller to move a cutting tool along a circular path. Dynamics analysis, forward kinematics and inverse kinematics of industrial robotic are archived in the latter topic. Two experiments were also performed on RRR and RRRRRR manipulators by analyzing the inverse kinematic equations to make these robots follow the desired trajectories. This study takes innovations and achieves control improvement in different systems with optimization controller or trajectory planning.

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Research on a novel PID based controller for nonmagnetic hydraulic navigation simulator with AMESim simulation

Cited by 2 publications

References 4 publications

Design of PIDDα Controller for Robust Performance of Process Plants

Design of PIDDα Controller for Robust Performance of Process Plants

Dynamic Analysis and Control of Hydraulic Machine System and Industrial Robotic Manipulators

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