Benchmark temperature microcontroller for process dynamics and control

Park, Junho; Martin, Ronald A.; Kelly, Jeffrey D.; Hedengren, John D.

doi:10.1016/j.compchemeng.2020.106736

Cited by 63 publications

(32 citation statements)

References 54 publications

(43 reference statements)

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“…In this case, the solution of the regulation problem, as the task of stabilization of the loading process parameters, requires identifying an adequate control law by known methods. This is the approach applied in some researches [11,12,19], but the problem of loading Heavy Lift vessels involves a significant increase in safety requirements for operations, and necessitates the synthesis of movement control laws in the "vessel-crane-cargo" system. So, the methodology based on the synthesis of control laws involves regulation compliance at risk and time constraints.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Solovey¹,

Ben²,

Dudchenko³

et al. 2020

EEJET

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The aim of the work is to develop a method for optimal control of handling operations with heavy lift cargo on sea vessels. Based on the review of scientific research in the field of loading heavy lift cargo, priority directions for improving the automated control systems for cargo handling operations on ships have been determined. Within a scientific hypothesis, it was proposed to synchronize solutions to the problem of ship propulsion control and automated control of heavy lift onboard cranes in order to improve the accuracy of loading processes. The paper analyzes the dynamic model of the "vessel-crane-cargo" system and the criteria of optimality in the problem of ship regulation-stabilization under minimization of loading time. An inverse loading algorithm has been developed, based on the principles of the loading control optimization with limiting the choice of motion by linear displacements and turns of the vessel. When executing the inverse algorithm, restrictions associated with the minimization of heeling moments in the "vessel-crane-cargo" system and restrictions associated with the maximum and minimum boom outreach are applied. The study determined the technical feasibility of achieving invariance in the cargo stabilization system with the inverse loading algorithm on heavy lift vessels. On the basis of the proposed method, simulation modeling of the ship loading process was carried out on simulators at the Kherson State Maritime Academy. The simulation modeling has shown that the use of the inverse algorithm will reduce the time of cargo operations by 50-70 percent and, as a result, reduce the risk of emergencies when loading the ship. It was also determined that the use of the inverse algorithm is appropriate for cargo of more than 100 tons

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Solovey¹,

Ben²,

Dudchenko³

et al. 2020

EEJET

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“…This study uses the MATLAB ® /SIMULINK ® connection. More information regarding TCLab can also be found in [35][36][37][38].…”

Section: Temperature Control Laboratory (Tclab)mentioning

confidence: 99%

“…The paradigm is shifting from monolithic laboratory experiments that require significant resources to take-home and modular experiments. The Temperature Control Laboratory (TCLab) proposed by [35][36][37][38] is used to teach PID control to undergraduate engineering students [39,40]. The TCLab is a low-cost Arduino-based lab which is a pocket-sized, plug-and-play kit, meaning that it does not require the user to perform the assembly.…”

Section: Introductionmentioning

confidence: 99%

Swarm-Based Design of Proportional Integral and Derivative Controllers Using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study

2020

Self Cite

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Simple and easy to use methods are of great practical demand in the design of Proportional, Integral, and Derivative (PID) controllers. Controller design criteria are to achieve a good set-point tracking and disturbance rejection with minimal actuator variation. Achieving satisfactory trade-offs between these performance criteria is not easily accomplished with classical tuning methods. A particle swarm optimization technique is proposed to design PID controllers. The design method minimizes a compromise cost function based on both the integral absolute error and control signal total variation criteria. The proposed technique is tested on an Arduino-based Temperature Control Laboratory (TCLab) and compared with the Grey Wolf Optimization algorithm. Both TCLab simulation and physical data show that satisfactory trade-offs between the performance and control effort are enabled with the proposed technique.

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“…TCLab(temperature control lab) is a temperature control experimental device with a microcontroller as shown in Figure 13(a) [37]. As a hardware benchmark test device, TCLab is widely used in the teaching of process control [38,39].…”

Section: Experimental Verificationmentioning

confidence: 99%

Tuning of Linear Active Disturbance Rejection Controllers Based On Step Response Curves

Cui

Tan

et al. 2020

IEEE Access

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Most of the controlled plants in the industrial field are complex in structure and the models are difficult to obtain, thus tuning of the controller parameters without a detailed model is critical in practice. This paper proposes a specific parameter tuning formula for second-order linear active disturbance rejection controller (LADRC) based on step response curves of the controlled plant. The proposed tuning formula is only related to the two special points on the step response curve of the plant. Simulation results for a wide range of systems show that the proposed method can achieve satisfactory performance in disturbance rejection and robustness, and the Bode plots show that this tuning method also performs well in noise suppression. The effect is also verified with a practical heater temperature control experiment.

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Benchmark temperature microcontroller for process dynamics and control

Cited by 63 publications

References 54 publications

Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Swarm-Based Design of Proportional Integral and Derivative Controllers Using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study

Tuning of Linear Active Disturbance Rejection Controllers Based On Step Response Curves

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