Interactive Tuning Tool of Proportional-Integral Controllers for First Order Plus Time Delay Processes

Ruz, Mario L.; Garrido, Juan; Vázquez, Francisco; Morilla, Fernando

doi:10.3390/sym10110569

Cited by 14 publications

(11 citation statements)

References 42 publications

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“…The suggested AMIGO tuning rules for PID controller of FOPDT model as mentioned in [17] are, Meanwhile, AMIGO tuning rules for PI controller as shown in [19] are,…”

Section: Robust Pid Control Designmentioning

confidence: 99%

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Robust PID Control Design in CPS-based Batch Distillation Column

Ayu¹,

Rusmin²,

Hidayat³

2019

EECSI

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Interconnected system between computation and physical process (Cyber-Physical Systems) has been widely used in industrial processes. In CPS-based industrial process, sensors, controllers, and actuators are connected into a communication network. The communication network may introduce delay time uncertainties due to shared resources and load traffic in the network. Furthermore, the nonlinear timevarying characteristic of batch distillation column may causes another uncertainties to take into account in control system design. Parameter model and delay process uncertainty is introduced due to linearized system approximation that unmodeled high-frequency dynamics. The dynamic uncertainty on both I/O channel are also introduced to the system uncertainty. In this paper, robust PI and PID controller using AMIGO method with appropriate weighting function is designed to guarantee robust stability spesification of batch distillation column. The impact of system uncertainties to closed-loop system performances such as peak overshoot and integral error is investigated. MATLAB/Simulink simulation is used to validate the methods before its implementation in CPS-based batch distillation column. Based on simulation, the proposed robust PI/PID controller can guarantee robust stability of system compared to conventional PID controller. Furthermore, the robust PI/PID controller can improve closedloop system performances compared to conventional PID.

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“…The suggested AMIGO tuning rules for PID controller of FOPDT model as mentioned in [17] are, Meanwhile, AMIGO tuning rules for PI controller as shown in [19] are,…”

Section: Robust Pid Control Designmentioning

confidence: 99%

“…The determinant of choosing PI or PID controller is depend on performance characteristic in design process [19]. If the Derivative-term (D) is present in controller, the high frequency noise is highly amplified, hence the closed-loop system will sensitive to noise measurement.…”

Section: Robust Pid Control Designmentioning

confidence: 99%

Robust PID Control Design in CPS-based Batch Distillation Column

Ayu¹,

Rusmin²,

Hidayat³

2019

EECSI

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“…The most popular controllers used for tracking of the grid-connected inverter output current are proportional-integral (PI) [19][20][21][22][23][24][25], proportional-integral-derivative (PID) [3,21,26,27], and proportional-resonant (PR) [2,15,21,[28][29][30][31][32] controllers. However, more sophisticated predictive current controllers can be used as well [4,33,34].…”

Section: Introductionmentioning

confidence: 99%

“…The comparison of proposed controller with other more advanced controllers that can be used for current control, e.g., with proportional-resonant and predictive current controllers was not analyzed in this work because they are less popular in microinverter applications. On the other hand, it is self-evident without analysis that these controllers provide lower current distortions and lower steady-state error, but their implementation is more complicated [4,[32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Controller for the Grid-Connected Microinverter Output Current Tracking

2020

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The modification of the proportional–integral (PI) controller with the variable proportional constant for tracking of the grid-connected photovoltaic microinverter output current has been proposed. The obtained results show that in the case when the proportional constant of the PI controller varies in time according to the appropriate law, the microinverter output current sinus shape distortions decrease as compared to the case when the ordinary PI controller is used. The operation of the microinverter with the proposed controller was investigated for the cases when the electrical grid voltage sinus shape is not distorted and when it is distorted by the higher harmonics.

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“…Like Monte Carlo For the tracking and adaptive cruise control of an intelligent vehicle, the proportion integration differentiation (PID) control is a control loop mechanism employing feedback that is widely used in industrial control systems and a variety of other applications requiring continuously modulated control. It has been the classic type of controller since the mid-20th century and will continue as the most often used industrial control scheme, due to its remarkable effectiveness, simple implementation, and broad applicability [6,7]. However, the parameter setting processing of conventional PID controllers is very complicated and the results are hard to be satisfactory, which might cause a great waste of manpower, material resources, and equipment.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Approach to Design Practical Adaptive Control for a Small-Scale Intelligent Vehicle

Yang

et al. 2019

Symmetry

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Reinforcement learning (RL) based techniques have been employed for the tracking and adaptive cruise control of a small-scale vehicle with the aim to transfer the obtained knowledge to a full-scale intelligent vehicle in the near future. Unlike most other control techniques, the purpose of this study is to seek a practical method that enables the vehicle, in the real environment and in real time, to learn the control behavior on its own while adapting to the changing circumstances. In this context, it is necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. Meanwhile, in order to realize adaptive cruise control specifically, a set of symmetrical control actions consisting of steering angle and vehicle speed needs to be optimized simultaneously. In this paper, firstly, the experimental setup of the small-scale intelligent vehicle is introduced. Subsequently, three model-free RL algorithm are conducted to develop and finally form the strategy to keep the vehicle within its lanes at constant and top velocity. Furthermore, a model-based RL strategy is compared that incorporates learning from real experience and planning from simulated experience. Finally, a Q-learning based adaptive cruise control strategy is intermixed to the existing tracking control architecture to allow the vehicle slow-down in the curve and accelerate on straightaways. The experimental results show that the Q-learning and Sarsa (λ) algorithms can achieve a better tracking behavior than the conventional Sarsa, and Q-learning outperform Sarsa (λ) in terms of computational complexity. The Dyna-Q method performs similarly with the Sarsa (λ) algorithms, but with a significant reduction of computational time. Compared with a fine-tuned proportion integration differentiation (PID) controller, the good-balanced Q-learning is seen to perform better and it can also be easily applied to control problems with over one control actions.

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Interactive Tuning Tool of Proportional-Integral Controllers for First Order Plus Time Delay Processes

Cited by 14 publications

References 42 publications

Robust PID Control Design in CPS-based Batch Distillation Column

Robust PID Control Design in CPS-based Batch Distillation Column

Controller for the Grid-Connected Microinverter Output Current Tracking

Reinforcement Learning Approach to Design Practical Adaptive Control for a Small-Scale Intelligent Vehicle

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