Analisa Perbandingan Kontroler PID Terhadap Motor BLDC Menggunakan Penalaran Cohen-Coon dan Trial & Error
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The purpose of this research is to design PID control on BLDC motors using 2 tuning methods, namely Cohen-Coon and Trial & Error. PID control of formula calculations with calculations in Simulink Matlab. From the simulation results shown in graphical form, the use of the PID control gives a better effect than the use of the P and PI controls. This can be seen in the comparison curve which shows the speed of the initial start process when using the PID control. In the Trial & Error method, the response value of the system to controller P is obtained, namely, rise time = 0.0151 s, settling time = 0.6 s, overshoot = 75.9%, peak time = 1.74 s, and time delay = 0.424 s. on the PI controller namely, rise time = 0.0148 s, settling time = 0.591 s, overshoot = 76.3%, peak time = 1.74 s, and time delay = 0.0416 s. on the PID controller namely, rise time = 0.0496 s, settling time = 0.55 s, overshoot = 44 %, peak time = 1.31 s, and time delay = 0.128 s. In the Cohen-Coon method, the response value of the system to controller P is obtained, namely, rise time = 0.0168 s, settling time = 0.575 s, overshoot = 73.3%, peak time = 1.71 s, and time delay = 0.0469 s. on the PI controller namely, rise time = 0.0573 s, settling time = 0.603 s, overshoot = 39.3%, peak time = 1.23 s, and time delay = 0.142 s. on the PID controller namely, rise time = 0.276 s, settling time = 0.658 s, overshoot = 2.42 %, peak time = 0.159 s, and time delay = 0.576 s. From the simulation results it is shown that the value for the Cohen-Coon tuning method is better than the Trial & Error method, perhaps because the input value for the Trial & Error method is larger.Keywords:
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Overshoot (microwave communication)
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Abstract A 40 liter spherical tank with varying time delay was subjected to open loop analysis using a step response technique with sodium chloride solution as tracer. The experimental data was adequately represented by a first order plus dead time (FOPDT) model with an error of less than five percent. These model parameters were used to generate Smith Predictor controller, IMC controller, and IMC PID controller using MATLAB. For closed loop control of the process based on rise time, settling time, overshoot, peaktime, decay ratio, and ISE, it was found that the IMCPID controller is better suited for this process.
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Overshoot (microwave communication)
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The primary purpose of this paper is to investigate the response time of magnetorheological (MR) dampers and the effect of operating parameters. Rapid response time is desired for all real-time control applications. In this experimental study, a commercially available MR damper was tested and the response time was found for various operating conditions. The parameters considered include operating current, piston velocity, and system compliance. The authors define the response time as the time required to transition from the initial state to 63.2% of the final state, or one time constant. Using a triangle wave to maintain constant velocity across the damper, various operating currents ranging from 0.5 Amps to 2 Amps were applied and the resulting force was recorded. The results show that, for a given velocity, the response time remains constant as the operating current varies, indicating that the response time is not a function of the applied current. To evaluate the effect of piston velocity on response time, velocities ranging from 0.1 in/s to 3 in/s were tested. The results show that the response time decreases exponentially as the velocity increases, converging on some final value. Further analysis revealed that this result is an artifact of the compliance in the system. To confirm this, a series of tests were conducted in which the compliance of the system was artificially altered. The results of the compliance study indicate that compliance has a significant effect on the response time of the damper.
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Piston (optics)
Shock absorber
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The most widely used controllers in industries are PI or PID controllers. The major concern with designing of such controllers is the determination of controller parameters. An intelligent method is discussed in this paper to determine the controller parameter to control the dynamic performance of buck converter by optimizing these parameters with the big bang big crunch (BBBC) algorithm. Initially, the mathematical modeling is developed and thereafter the weighted numerical values of overshoot, peak time, rise time and settling time are summed to make a fitness function which is to be minimize for the better dynamic response. The performance of BBBC-PI controller is analyzed by settling time, rise time and overshoot of the output response. The disturbance rejection ability of optimized PI controller is verified by three cases such as step change in input voltage, output voltage and output load resistance. The closed loop operation of buck converter is simulated and verified at the MATLAB/Simulink platform.
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Buck converter
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The combined application of approximate time windows (ATW) and rough sets in deriving rules for tuning controllers is discussed. The approach is to build an approximate reasoning system based approximate measures of controller performance, namely, overshoot, rise time, and settling time. Overshoot is the biggest deviation of step response from a particular steady state after the step response has reached a tolerance band for the first time. Rise time r/sub t/ is the time when a step response reaches 90% of its steady-state value for the first time, and settling time s/sub t/ is measured relative to rise time (i.e. the clock for s/sub t/ is reset at t=r/sub t/). In this paper, the clocks used to measure durations required to achieve controller objectives are modeled as ATWs. An ATW partitions time relative to granules (clumps of similar timing measurements) such as early, ontime, late. An ATW determines the degree of membership of each observed duration in each of its temporal partitions. Based on observations of the degree of overshoot, rise time, and settling time during the operation of a control system, the architecture of an approximate time rough control system is established. The rough controller is guided by rules derived from a real-time decision-making system. The focus of this paper is a description of how rough control rules derived from a real-time decision system table have been used in fine-pointing for attitude control of a small satellite. The contribution of this paper is the application of rough sets, fuzzy sets and approximate time windows in the design of approximate time rough control systems.
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The aim of this paper is to comprehend the performance of back calculation anti windup scheme with difference tracking time constant, Ta on Proportional – Integral - Derivative (PID) controller for improving temperature regulation of glycerin bleaching process. Several available well tuning methods including Ziegler Nichols (ZN), Internal Model Control (IMC) and Integral Square Error (ISE)-Load are used and analyzed. The performance of the controller tuning methods are compared based on percentage of overshoot, settling time, rise time and time to recovery on the presence of disturbance. From the results, the best performance of temperature regulation for glycerin bleaching process can be reached by using ISE-Load tuning with tracking time constant, Ta equal to derivative time constant, Td.
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In this paper a robust fractional order PID (FOPID) controller is proposed to control the automatic voltage regulator (AVR) system, the tuning of the controller gains are done using whale optimization algorithm (WOA) and integral time absolute error (ITAE) cost function is adopted to achieve an efficient performance. The transient analysis was done and compared with conventional PID in terms of overshoot, settling time, rise time, and peak time to explain the superiority of the proposed controller. Finally, a robustness analysis is done by adding external disturbances to the system and changing the system parameters by ±20% from its original value, the controller overcomes the disturbances signals with less than 0.25 s and faces the changes of the system values and returning the response within (0.7-1) sec and led the system to the desired response efficiently. The numerical simulations showed that the smart WOA offers satisfying results and faster response reflected clearly on the robust and stable performance of the proposed controller in improving the transient analysis of AVR system response.
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This paper presents temperature control and estimation using full state feedback controller with observer mechanism (FSFBCOM) in data centre. The temperature dynamic of a data centre was obtained in the form of transfer function and transformed into state space model. The system was initially modelled in MATLAB as an open loop system and simulation test was conducted to study the temperature characteristic performance of data centre without controller. The transient and steady state performance was presented in terms of time domain parameters: rise time, settling time, percentage overshoot, final value, and steady state error. The simulation result of the open loop system indicated a rise time of 1.41 min. (84.8 s), percentage overshoot of 0%, settling time of 2.68 min. (161 s), and final value to unit input is 10 oC, and steady state error of -9 oC. Simulation conducted when the designed FSFBCOM was introduced into the system showed that the performance parameters: rise time, percentage overshoot, settling time, final value, and steady state error became 0.41 min. (24.456 s), 0.232%, 0.8594 min. (51.564 s), 1 oC, and 0 oC respectively. Thus, the addition of the designed controller has improved the computer room temperature response performance of data-centre and provided good temperature estimation capacity even for different temperature values required of a data-centre.
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Overshoot (microwave communication)
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A PPI controller is investigated for set-point tracking associated with a highly oscillating secondorder-like process. The controller is tuned using the MATLAB optimization toolbox and five different errorbased objective functions. All the objective functions result in a same time response of the closed-loop control system to a unit step input. The unit step reference input time response of the control system has a zero maximum overshoot and a settling time of 16 seconds. It has an oscillatory nature for a response time up to 10 seconds. The simulation results using the PPI controller are compared with using I-PD, PD-PI, PIPD, PID + first-order lag and PID controllers. The PPI can compete with I-PD, PD-PI and PI-PD controllers regarding the maximum percentage overshoot. However, it cannot compete with all the other five controllers regarding the settling time.
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Controlling the temperature of the glycerin purification process system was not an easy task, as an increase in operating temperature would significantly reduce the quality of the purified glycerin. This is because an unlimited increase in temperature beyond the set point and an excessive prolongation of the heating process would result in the formation of an excessive secondary oxidation product in the final purified glycerin. This paper discusses the transient response characteristics of the glycerin heating process using a parallel PID controller. The glycerin heating process behavior was determined experimentally using step input test and modelled as the First Order plus Delay Time. The controller parameters wereadjusted using Ziegler-Nichols, Cohen-Coon and Wang tuning methods, each of which was analyzed on the basis of the corresponding integral error criterion value. The Integral Square Error, Integral Absolute Error and Integral Time-weighted Absolute Error criteria value were used to evaluate the efficiency of the glycerin heating process. The transient response performances in terms of overshoot, rise time and settling time were also evaluated. Simulation work has shown that the process has experienced high overshoots for Ziegler-Nichols and Cohen-Coon, and has taken longer time to settle. Wang method exhibits with no overshoot but slow response. The lower gain PID controller was found to improve the process response in terms of overshoot but increase in the rise time and settling time. The results indicate that the desired process performance were more or less influenced by the interaction between the tuning parameters. The Ziegler-Nichols PID controller is not recommended for controlling glycerin heating process due to process response oscillations that are difficult to eliminate without prolonging the heating cycle.
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The purpose of this research is to design a PID control on a DC motor using 2 tuning methods, namely Trial & Error and Ziegler-Nichols. The results showed that the Pittman DC Motor system response from the Pittman DC motor was very unstable, in which there were still many oscillations and a very high overshoot value. In the Trial & Error method, the system response value was obtained on the P controller, namely, rise time = 0.000551 s, settling time = 0.00468 s, overshoot = 37.1 %, peak time = 0.972 s, and time delay = 0.00134 s. on the PI controller namely, rise time = 0.000396 s, settling time = 0.00534 s, overshoot = 47.7%, peak time = 1.23 s, and time delay = 0.00102 s. on the PID controller namely, rise time = 0.000223 s, settling time = 0.00502 s, overshoot = 64.6%, peak time = 1.54 s, and time delay = 0.000601 s. In the Ziegler-NIchols method, the response value of the system to the P controller is obtained, namely, rise time = 0.00118 s, settling time = 0.00564 s, overshoot = 15.4%, peak time = 0.178 s, and time delay = 0.00262 s. on the PI controller namely, rise time = 0.000275 s, settling time = 0.00531 s, overshoot = 58.3%, peak time = 1.44 s, and time delay = 0.000767 s. on the PID controller, namely, rise time = 0.00133 s, settling time = 0.00446 s, overshoot = 12.6 %, peak time = 0.0237 s, and time delay = 0.00288 s. The simulation results show that the value for the Ziegler-Nichols tuning method is better than the Trial & Error method, perhaps because the input value for the Trial & Error method is larger.
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