First Order Plus Dead Time Model Research Articles

Alternative Identification Algorithms for Obtaining a First-Order Stable/Unstable Process Model from a Single Relay Feedback Test

A first-order-plus-dead-time (FOPDT) model is most commonly used for control system design and on-line tuning. By using a single biased/unbiased relay feedback test, four alternative identification algorithms are proposed for obtaining a FOPDT model for a stable process and four alternative FOPDT algorithms for unstable processes. By deriving the exact relay response expressions for FOPDT stable/unstable processes, two of the above algorithms are proposed for implementation simplicity based on some measurable parameters of the relay response, while the other two algorithms are developed with more computation effort for obtaining enhanced accuracy for identification of high-order processes, according to whether a single biased or unbiased relay feedback test is used. A denoising method based on a low-pass Butterworth filter is presented to guarantee identification robustness in the presence of measurement noises. Illustrative examples are given to demonstrate the effectiveness and merits of the proposed id...

Performance and Robustness Improvement in the IMC-PID Tuning Method

This manuscript presents an extension of the IMC-PID tuning method. A higher order structure is adopted for the process model than in the original method, where a FOPDT (First-Order Plus Dead Time) model is used. Then, the general IMC procedure is applied, and the so obtained regulator is approximated with a (real) PID. Simulations and experimental results indicate that abandoning the FOPDT structure in the IMC approach is beneficial both in terms of performance and robustness.

PID controller tuning for the first-order-plus-dead-time process model via Hermite-Biehler theorem

This paper discusses PID stabilization of a first-order-plus-dead-time (FOPDT) process model using the stability framework of the Hermite-Biehler theorem. The FOPDT model approximates many processes in the chemical and petroleum industries. Using a PID controller and first-order Padé approximation for the transport delay, the Hermite-Biehler theorem allows one to analytically study the stability of the closed-loop system. We derive necessary and sufficient conditions for stability and develop an algorithm for selection of stabilizing feedback gains. The results are given in terms of stability bounds that are functions of plant parameters. Sensitivity and disturbance rejection characteristics of the proposed PID controller are studied. The results are compared with established tuning methods such as Ziegler-Nichols, Cohen-Coon, and internal model control.

Hybrid algorithm with fuzzy system and conventional PI control for the temperature control of TV glass furnace

This paper presents a practical application of fuzzy logic to the temperature control of glass melting furnace for television picture tube. Because of the complexity and nonlinearity, temperature control of glass melting furnace is still delegated to human operators. Though the overall characteristics of glass melting furnace are complex and nonlinear, one part of the furnace characteristics can be modeled as a linear system. The linear part of the furnace dynamics is modeled with a first-order-plus-dead-time (FOPDT) system and a proportional integral (PI) controller is applied to the FOPDT model. The remaining complex and nonlinear part of the furnace dynamics is covered by the fuzzy system, i.e., rules of human experts. The PI controller and fuzzy system are combined in cascade. The PI controller is applied to maintain the left-right symmetric operation of the furnace, and a linear regression is used for the temperature trend to determine input to the fuzzy system in order to overcome the high frequency disturbance. The proposed hybrid controller is implemented in Samsung-Corning Company to demonstrate the effectiveness of the proposed control algorithm.

Improving Performance and Robustness in the IMC-PID tuning method

This paper presents an extension of the IMC-PID tuning method. A more complex structure is adopted for the process model than in the original method, where a FOPDT (First-Order Plus Dead Time) model is used, the IMC procedure is applied, and the so obtained regulator is approximated with a (real) PID. Experimental results indicate that abandoning the FOPDT structure in the IMC approach is beneficial both in terms of performance and robustness. The selection of a more realistic model structure looks very promising for the synthesis of industrial regulators.

A New On-Line PID Tuning Method Using Neural Networks

Abstract This paper presents an on-line PID tuning control method, based on the parameters of a first-order plus dead-time (FOPDT) model, which are obtained by using Neural Networks (NN). The outputs of the neural networks are the three parameters of the FOPDT model. By combining this algorithm with a conventional PID controller, an adaptive controller is obtained which requires very little a priori knowledge about the plant under control. The simplicity and feasibility of the scheme for real-time control provide a new approach for implementing neural network applications for a variety of on-line industrial control problems. Simulation results demonstrate the feasibility and adaptive property of the proposed scheme.

A Tuning Strategy for Unconstrained SISO Model Predictive Control

This paper presents an easy-to-use and reliable tuning strategy for unconstrained SISO dynamic matrix control (DMC) and lays a foundation for extension to multivariable systems. The tuning strategy achieves set point tracking with minimal overshoot and modest manipulated input move sizes and is applicable to a broad class of open loop stable processes. The derivation of an analytical expression for the move suppression coefficient, λ, and its demonstration in a DMC tuning strategy is one of the significant contributions of this work. The compact form for the analytical expression for λ is achieved by employing a first order plus dead time (FOPDT) model approximation of the process dynamics. With tuning parameters computed, DMC is then implemented in the classical fashion using a dynamic matrix formulated from step response coefficients of the actual process. Just as the FOPDT model approximation has proved a valuable tool in tuning rules such as Cohen−Coon, ITAE, and IAE for PID implementations, the tunin...

A novel tuning strategy for multivariable model predictive control

Model predictive control (MPC) has established itself as the most popular form of advanced multivariable control in the chemical process industry. However, the benefits of this technology cannot be realized unless the controller can be operated with desirable performance for an extended period of time. The objective of this work is to present an easy-to-use and reliable tuning strategy that enables the control practitioner to maintain MPC at peak performance with minimal effort. A novel analytical expression that computes the move suppression coefficients, guidelines to select the additional adjustable parameters, and their demonstration in an overall tuning strategy are some of the significant contributions of this work. The compact form for the analytical expression that computes the move suppression coefficients is derived as a function of a first order plus dead time (FOPDT) model approximation of the process dynamics. With tuning parameters computed. MPC is then implemented in the classical fashion using an internal model formulated from step response coefficients of the actual process. Just as a FOPDT model approximation has proved a valuable tool in tuning rules such as Cohen-Coon. ITAE and IAE for PID implementations, the tuning strategy presented here is significant because it offers an analogous approach for multivariable MPC.

Control of ethanol concentration in a fed-batch cultivation of Acinetobacter calcoaceticus RAG-1 using a feedback-assisted iterative learning algorithm

A control scheme using a feedback-assisted iterative learning control algorithm to maintain the optimal ethanol concentration for the enhancement of cell growth and emulsan production in fed-batch cultivation of Acinetobacter calcoaceticus RAG-1 is presented. The optimal concentration of ethanol for cell growth and emulsan production was determined by considering the inhibitory effect of ethanol. To maintain the optimal concentration of ethanol, continuous feeding of ethanol was adopted using the feedback-assisted iterative learning control algorithm. A mathematical kinetic model is used to simulate the objective process. The transfer function of the objective process was approximated to the FOPDT (First Order Plus Dead Time) model and model parameters were determined by the LSE (Least Square Estimation) method. In the transfer function modeling, to evaluate the process model parameters which can describe the objective process, the disturbance included in the process was eliminated by linearization of two data sets of preliminary fed-batch runs. Fed-batch cultivation experiments using a feedback-assisted learning control algorithm were performed. The results showed that the convergence performance was improved as the run was iterated and the emulsan yield was increased compared with that of the runs controlled by only the feedback loop.