OVERCOMING DEAD TIME IN THERMAL PROCESSES: A COMPARATIVE EVALUATION OF PID-SP AND PID-IMC CONTROL STRATEGIES

Dead time is a critical factor that often causes instability in process systems, so appropriate control strategies are needed to address it. This study focuses on the outlet temperature control of a stirred-tank heater that exhibits dead time. A 10 L laboratory-scale tank equipped with an electric heater was constructed, and the long outlet pipeline introduces a measurable delay in the temperature response. The outlet temperature is maintained by adjusting the electrical heating input, while PID parameters are tuned using the Process Reaction Curve (PRC) method. Two control strategies are examined: PID with Smith Predictor (PID-SP) and PID with Internal Model Control (PID-IMC). System models were implemented and tested using XCOS/Scilab. Closed-loop simulation results indicate that PID-IMC performs better than PID-SP, as indicated by a lower integral absolute error (IAE). These results provide evidence of the practical advantages of PID-IMC in compensating for dead time in thermal process systems and offer useful guidance for improving process control design in industrial applications.

Online Auto Selection of Tuning Methods and Auto Tuning PI Controller in FOPDT Real Time Process-pH Neutralization

Steam Power Plant is one of power plant who supplies the main needs of electricity in Indonesia. The production capacity of this power plant is 45,47% from all of Indonesian power plant. Therefore, the need for operational protection to keep electricity production stable and efficient. In this research, used method of determining control strategy in the form of coordinate control to protect electricity production in steam power plant. Coordinate control is one of strategy control types that makes the load changes in generator as a feedforward signal for both control system in boilers and turbines like a parallel. This coordinate control principle using load changes signal to simultaneously after the turbine governor valve and boiler fuel control valves, with the result that this control strategy has a fast and appropriate responsibility. To simulate the flow of process control on steam power plant control using software named HYSYS. While tunning used in this researce is based on system modeling using Intenal Model Control (IMC) with first order plus dead time (FOPDT) for modeling, so this method can be believed to be able to overcome the disturbance changes in the system. In this researce given two types of testing, that is test based of set point and disturbance changes. The best IMC tunning is obtained parameters Kc = 0,24928 Ti = 1,27711 Td = 0,01383 for power control by turbine governor valve with the result of settling time = 19,54 second, maximum overshoot 0,5% and integral absolute error 80,835 when given set point change of +5% dan settling time = 11,04 second, maximum overshoot 0,3%, and integral absolute error 25 when given disturbance changes of +5%. As for controlling the pressure by throttle pressure controller, the best IMC tunning obtained parameters Kc = 3,14 Ti = 41,3 Td = 24,2 with the result of settling time = 19,54 second, maximum overshoot 10,3%, and integral absolute error 12,321 when given set point changes of +5% and settling time = 10,18 second, maximum overshoot 0,3%, and integral absolute error 67,269 when given disturbance changes of +5%.

This article demonstrates the improvement of control performance in formaldehyde production process using model predictive control (MPC) in comparison between conventional proportional-integral control. MPC is an advance process control which can improve the performance of a control process in terms of time delay, open loop instability, constraints, and thereof combinations. MPC will reduce the variance in the control variable that affects the process to operate closer to physical constraints. The empirical model of the MPC controller is based on the process reaction curve (PRC) by using the first order plus dead time (FOPDT) approach. Four controllers which were flow control (FIC-102), temperature control (TIC-101), pressurce control (PIC-101), and liquid level control (LIC-101) were tested by changing the set points (SP) and giving disturbances. The performance indicator for the controllers are shown by their value of integral of absolute error (IAE) and integral of square error (ISE). The results show that the MPC improved the controllers’ performance either tested by changing SP or giving disturbance and are better in terms of IAE or ISE.

A control strategy must be developed to maintain the product composition of the two- or more-mixed-flow reactor series. In this research, a two-mixing tank series (TMTS) was utilized in the laboratory to approach the two-mixed flow reactors’ problems. Both tanks are designed to overflow so that the volume remains constant. The salt solution was directly fed to Tank 1, while the water was charged to both tanks as the mixing progressed. The output of Tank-1 flowed to Tank-2 through a rather long pipe, resulting in dead time in Tank-2. The Smith predictor was applied to overcome dead time. The PRC (process reaction curve) method was used to tune the PID (proportional-integral-derivative) parameters of Tank-1 and Tank-2. The novel composition controls, adaptive-PID (APID) and modified metaheuristic adaptive-PID (MMAPID), have been proposed and compared with conventional PID. The mathematical models were rigorously examined through XCOS simulations. The processing system is considered sensitive to changes in input disturbances since the process time constants of both tanks were quite low. The dead time of 0.5 minutes was found in Tank 2. The PID parameters produced by PRC experiments resulted in stable responses to changes in the disturbance system. As shown in the dynamic simulation study, both novel APID and MMAPID give almost the same results; their responses are faster than the conventional PID. Based on the integral absolute error (IAE) results, APID with scheduling PID parameters produced the smallest IAE and outperformed the conventional PID. The results of the study highlight APID and MMAPID potential in PID control applications for composition control in a TMTS. The combined APID-Smith and MMAPID-Smith were able to overcome the dead time well. This study creates opportunities for engineers and professionals to use cutting-edge control technologies, improving automation and chemical process industries.

Tuning the parameters of a controller is very important in system performance. Ziegler and Nichols tuning method is simple and cannot guarantee to be effective always. This paper investigates the design of fuzzy logic based self tuning Proportional Integral (PI) controller on a First order Plus Dead Time (FOPDT) non linear spherical tank system using block box modelling. The real time PI controller was designed for a liquid level process, the servo and regulatory responses of the controller were obtained. The simulation results of conventional PI controller, Internal Model Control (IMC) based PI controller and Self tuning Fuzzy PI controller is obtained using MATLAB. From the simulation it is clear that there is substantial improvement in the Self tuning Fuzzy PI controller in terms of peak overshoot, settling time, peak time, rise time, Integral Square Error (ISE) and Integral Absolute Error (IAE).

A system with varying transportation lags has been experimentally studied for modeling. Modeling is performed using a step test. The tracer is sodium chloride solution whose conductivity is measured using an online conductivity analyzer. Based on the step response, the model parameters are determined and the lag processes are represented by a first order plus dead time (FOPDT) model. For the models developed, an internal model control (IMC) scheme is designed. Performance comparison, based on rise time, settling time, and overshoot, is done among the designed IMC controllers, conventional PID controllers, and Smith Predictor controllers. The present study depicts that IMC controllers outperform PID and Smith Predictor controllers.

A Model Identification Method for Tuning of PID Controller in a Smith Predictor Structure

Design of Neural Based PID Controller For Nonlinear Process

This work presents an advanced and systematic approach to analytically design the optimal parameters of a two parameter second-order internal model control (IMC) filter that satisfies operational constraints on the output process, the manipulated variable as well as rate of change of the manipulated variable, for a first-order plus dead time (FOPDT) process. The IMC parameters are designed to minimize a control objective function composed of the weighted sum of the error between the process variable and the set point, and the rate of change of the manipulated variable, and to satisfy the desired constraints. The feasible region of the constrained IMC control parameters was graphically analyzed, as the process parameters and the constraints varied. The resulting constrained IMC control parameters were also used to find the corresponding industrial proportional-integral controller parameters of a Smith predictor structure.

A New Tuning Method for Two-Degree-of-Freedom Internal Model Control under Parametric Uncertainty

In model based control, performance of the controlled plant considerably depends on the accuracy of real plant being modelled. In present work, an attempt has been made to design Internal Model Control (IMC), for three interacting tank process for liquid level control. To avoid complexities in controller design, the third order three interacting tank process is modelled to First Order Plus Dead Time (FOPDT) model. Exploiting the admirable features of Fractional Calculus, the higher order model is also modelled to Fractional Order First Order Plus Dead Time (FO-FOPDT) model, which further reduces the modelling error. Moving to control section, different IMC schemes have been proposed, where the fractional order filter is introduced along with the conventional integer order filter. Various simulations have been performed to show the greatness of Fractional order modelled system & fractional order filters over conventional integer order modelled system & integer order filters respectively. Both for parameters estimation of reduced order model and filter tuning, Genetic Algorithm (GA) is being applied.

In this article, design of PID controller using a modified internal model control (IMC) filter for right half plane (RHP) pole process with dead time is proposed. To possess H2 optimal behaviour, the derived IMC controller minimises the integral square error (ISE) for step input disturbances by defining the Blaschke product of unstable poles of the specific input and the model. Then it is converted into a single feedback loop controller as either PID or PID with first order filter on the basis of proposed underdamped IMC filter to improve the integral action and thereby providing fast response which is not feasible with critically damped filter. Maclaurin series approximation is used to design PID controller and Pade's approximation is used to design PID with first order lead lag filter. Various first order plus dead time (FOPDT) examples are taken and simulation is executed on diverse unstable processes and compared with some of the developed methods available in the literature. The two proposed controllers provide improved responses with respect to both nominal and perturbed conditions. The robustness studies have also been carried out for uncertainties in the plant dynamics and it is apparent that the proposed tuning method is robust.

Good control of processes with long dead time is often achieved using a Smith predictor configuration. However, not much work has been carried out on obtaining simple tuning rules for a Smith predictor scheme. This paper develops optimal analytical tuning formulas for proportional-integral-derivative (PID) controllers in a Smith predictor configuration assuming perfect matching. Exact limit cycle analysis has been used to estimate the unknown parameters of a first-order plus dead time (FOPDT) or second-order plus dead time (SOPDT) plant transfer function. Simple analytical tuning rules based on these FOPDT and SOPDT are then derived which can be used to tune a PID controller in a Smith predictor scheme. Some examples are given to show the value of the approach presented.

The concept of Model Predictive Control (MPC) is considered as one of the most important controlling strategies. It is used in several fields, such as petrochemical, oil refinery, fertilizer and chemical plants. It is also well spread among the clinicians and in the biomedical fields. In this context, our paper aims to investigate the thermal conditions inside the infant incubator for premature babies. In this study, we propose the Dynamic Matrix Control (DMC) as a control strategy. The most particularity of this strategy is applicable to the Multi-input Multi-output (MIMO) systems. It aims to compare different coupled transfer functions achieved by two identification methods in previous work. Also, a simulation of the air temperature and humidity is achieved inside the unit care. In this work, we focus on the tuning controlling parameters because it is considered as a key step in the successful performance of (DMC). Then, to obtain the (DMC), we have used an analytic tool, which is the Process Reaction Curve (PRC), for higher order transfers function because it needs a lot of work for this purpose. It should be approximated as a low order transfer function with time delay, which is achieved by using the First Order Plus Dead Time (FOPDT) of processing models. Finally, the result of the comparison of the infant-incubator is provided to show an optimal and good performance of the thermal behavior of our propos methodology and prove that a good identification ensures a better performance.