Internal Model Control Method Research Articles

Discrete Control for an Industrial Manipulator Using both Dahlin Algorithm and Internal Model Control Design Approaches

This paper presents the design of a discrete controller for the positioning of an industrial manipulator used to extract preheated billets from inside a rotary hearth furnace. The comparison between two common discrete design algorithms is made: the Dahlin algorithm and the Internal model control (IMC) method. Both methods are based on the manipulator’s positioning system discrete-time model. For each design method different controllers are derived. The obtained controllers are compared via simulation based on their overall performances. For the most suitable discrete controller the ringing effect is analyzed and a method to eliminate it is proposed.

Nonlinear Internal Model Control for Bearingless Induction Motor Based on Inverse System Theory

The bearingless induction motor is a nonlinear, multi-variable and strongly coupling system. In this paper, a new nonlinear internal model control (IMC) strategy based on inverse system theory is proposed to realize the decoupling control for the bearingless induction motor. The mathematical model of the motor is built and then the inverse system method is applied to decouple the original nonlinear system. Finally the internal model control method is introduced to ensure the robustness of the closed-loop system. The effectiveness of the proposed strategy are demonstrated by simulation.

English

In process industries, the level of the liquid must be controlled and it is a mandatory process. But controlling a non-linear process is a highly complicated and complex process. Many process industries employs conical tank because of its non-linear shape, because of its constantly changing cross section, with respect to height controlling. For each operating region, the linear portion of the nonlinear process is determined as first order plus dead time model (FOPD). Different controller scheme such as conventional PID controllers based on Ziegler-Nicholas (ZN-PID) method, Chien, Hrones and Reswich (CHR-PID) method, Tyres-Luyben (T-L-PID) method, Internal Model Control (IMC-PID) and Model Predictive Controller (MPC) method were proposed and their responses was compared. The main objective of the controller is to provide a minimum settling time, peak overshoot and rise time and to provide a minimum performance error. Among the various controllers, Model Predictive Control (MPC) satisfies the above criteria. The controller will be simulated using MATLAB SIMULINK

Internal Model Control Design for Autothermal Reforming System

This paper focuses on the control system design for a process of autothermal reforming (ATR) of ethanol. The targeted application is within an on-board fuel processor of ATR operating at the adiabatic reaction temperature for hydrogen production. An internal model control (IMC) method is designed for controlling the adiabatic reaction temperature of ATR reactor by manipulating the input air flow rate. Two strategies of controller design with and without the feed temperature control of the preheater unit are proposed in order to determine the suitable controller system as the surrounding temperature is a major disturbance for cold weather. Theoretical analysis demonstrates that IMC strategy can achieve desired performance. Two loops of control system of the ATR process combined with the feed temperature control can compensate the surrounding temperature better than without the feed temperature control.

Intelligent Self-Adaptive Control Method Based on RBFNN and its Application in Hydraulic Control

We proposed an intelligent self-adaptive control method based on RBFNN in this paper, dynamic identification model of nonlinear control system is built based on radial basis neural network, mixed intelligent method of dynamic self-adaptive internal model control is developed by adjusting online for nonlinear control system. We applied the intelligent self-adaptive control method to nonlinear hydraulic control, simulation shows the dynamic characteristic is greatly improved by the intelligent control strategy for nonlinear control system, good tracking and control effect is reached in condition of high frequency response, and the intelligent control method has higher precision, smaller overshoot and stronger robustness compared with common PID control, BPNN control and fuzzy control. It provides a new control method for nonlinear control system.

Warp Tension Control System Based on IMC

To ensure stability of warp tension during weaving process, the paper presented a warp tension control system which uses two permanent magnet synchronous motor (PMSM) drive let-off and take-up mechanism respectively. Based on analyzing factors influencing warp tension, a warp tension mathematical model was established. The paper proposed a tension adjustment arithmetic based on internal model control (IMC) method. The method has only one adjustable parameter which decides performance of the system. The simulation results show that the warp tension control system based on IMC has a fine effect on tension stability of warp and it is significantly superior to conventional PID control in terms of robustness and immunity.

Design and implementation of a model‐based PI‐like control scheme in a reset configuration for stable single‐loop systems

This article draws its motivation from classical model‐based controller design methods, such as Direct Synthesis (DS) and Internal Model Control (IMC) methods, and develops a model‐based PI‐like control scheme for stable systems. The proposed control scheme has been realized in a reset configuration. Furthermore, the control scheme has a tuning parameter, and its control action resembles a PI‐like controller. The proposed scheme shows improved performance as compared to that of IMC, Skogestad‐IMC‐PI, and Skogestad‐IMC‐PID control schemes. In addition, an automated approach to designing this scheme is proposed. The efficacy of the proposed control scheme is demonstrated via experimental evaluation on a computer‐interfaced pilot‐scale two‐tank system.

A Study of the Dynamics and Control of the Model IV Fluidized Catalytic Cracking Process

Fluid catalytic cracking (FCC) is one of the most important chemical units in oil refineries due to its economic benefits. This research work concentrates on improving the control system of the Model IV FCC unit where dynamic modeling and the controllability based on the McFarlane et al. (1993) model. Different open-loop tests were carried out in the wash oil flow rate (F1) and the furnace fuel flow rate (F5) to find the FCC models using Sundaresan and Krishnaswamy (S&K) and fraction incomplete response (FIR) methods. The riser temperature (Tr) and the regenerator bed temperature (Tg) were chosen as the control variables while (F1 and F5) were selected as the corresponding manipulated variables based on the relative gain array (RGA). PI controller tuning parameters were evaluated using the internal model control (IMC) method and different closed-loop control responses were examined for both set point tracking and disturbance rejection changes. Additional adjustments to the IMC filter constant were employed to further improve the closed loop responses for the system.

The Hydraulic System of Intelligent Asphalt Disperser Based on Neural Network Internal Model Control

Taking into consideration the lack of control precision of the hydraulic system of intelligent asphalt disperser, an internal model control method based on BP neural network was proposed. In this method, the ability of identification of complicated systems using neural network was combined with the feature that internal model control has low requirements on the model precision. In this way, the design of hydraulic controller of asphalt disperser can be achieved. The results show that this method has better robustness with regard to the systematic uncertainty and disturbance when compared with the PID control system, and the control effect is better, which provides a novel approach for the control of hydraulic system of intelligent asphalt disperser.

Control Strategy of Cascade STATCOM based on Internal Model Theory

Internal model control (IMC) method enables the system to be of good dynamic and steady performance, which is simple, and is easy to be implemented. In allusion to the cascade STATCOM feature of high order, instability, multi-variable, non-linearity and tight coupling, the mathematical model of cascade STATCOM in d-q-0 coordinates was deduced. Decoupling model of cascade STATCOM was given by Internal Model Control principle, computer simulation and experiment results were also given. Results show that with IMC, 3-phase currents control method of cascade STATCOM has good tracking performance and control precision both in a-b-c coordinates and in d-q-0 coordinates, and also achieves excellent current compensation results. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4452 Full Text: PDF

A Classic Controller Design for a Billet's Unloading Machine Using the IMC Approach

Abstract Classic controllers are widely used in the hot rolling process in order to obtain seamless tube pipes. This process relies on heating up the billets inside a rotary hearth furnace (RHF). For the design of a classic positioning control system, of an unloading machine's arm that extracts the billets from the RHF, the authors used the Internal Model Control (IMC) method. The parameters used for tuning the controllers are the IMC method filter's time constant and the filter order. The obtained controllers are compared in terms of overall performance and the one that generates the most suitable performance set is chosen. Validation of the results is made via simulation.

Design of decentralised Smith predictor for multivariable non-square processes with multiple time delays

In this paper, multivariable Smith predictor is designed in a decentralised sense for multivariable non-square systems with multiple time delays where the number of process inputs is higher than the number of process outputs. As the relative gain array method is not applicable for MIMO non-square processes, pairing for input and output variables is carried out using the block relative gain technique. The controllers are designed individually based on the corresponding process models. For the delay-compensated system, decentralised multivariable proportional-integral controllers are designed by simplified internal model control method. The proposed method is applied to shell standard control problem (three inputs and two outputs) and mixing tank (three inputs and two outputs) and the performance is compared with that of decentralised multivariable control system without any delay compensator. Simulation studies were carried out for both servo and regulatory problems. Robustness studies were carried out for uncertainty in all the process model parameters. The decentralised delay-compensated system with decentralised controllers showed better performance than that of uncompensated system.

Pseudo-feed-forward decoupling internal model control for singularity plants with time delays

In view of a kind of singularity plants with time delays, a new decoupling internal model control method is first proposed based on pseudo-feed-forward decoupling in this paper, which opened up a new study direction for internal model control. A pseudo-feed-forward decoupling is first designed to decouple the system into an open-loop diagonal dominant system. Rather than performing complex and lengthy calculations, this decoupling method is much better for simplifying the design and calculation. Then the diagonal elements of the decoupled plant are used as an internal model to design a multi-loop internal model controller. The proposed method is also verified applicable for non-singular plants. In order to improve control precisions, the diagonal filter is designed with new Luus–Jaakola (NLJ) optimization algorithm. During the optimization process, the integral of absolute error is adopted as an evaluation index. Finally, two industrial processes are taken as the simulation objects to demonstrate the validity and feasibility of the new method. Simulations results illustrate that the proposed strategy not only avoided complicated computations, but also achieved good trade-off between performance and robustness, even when the system model is mismatched.

The Analysis of Internal Model Control Methods

On the basis of summary of the history and present situation of internal model control, The article analyses the major achievements of single variable internal model control achieved , analyses the development advantages of multivariable internal model control and the existing problems, introduces detailedly the general method of internal model control, and proposes the view on the improvement strategies of internal model control method.

Multi-loop Constrained Iterative Model Predictive Control Using ARX -PLS Decoupling Structure

A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares (ARX-PLS) framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restriction. ARX-PLS decoupling character enables to turn the multivariable model predictive control (MPC) controller design in original space into the multi-loop single input single output (SISO) MPC controllers design in latent space. An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control (IMC) method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.

ILC-Based Internal Model Control of Superheater Steam Temperature

This paper proposes a novel Internal Model Control (IMC) method for the control of superheater steam temperature. The IMC is known for good robustness including handling of systems with time delays. While Iterative Learning Control (ILC) is a strategy for dealing with periodic disturbances. By using a combination of IMC and ILC, their individual advantages are used to increase the robustness against modeling uncertainties and handling time as well as decreasing the influence of the periodic disturbances affecting the superheater steam. Some simulation examples are shown to illustrate performance improvements that can be achieved by the new method over the conventional IMC and PID methods. Key Words: Superheater Steam Temperature; Internal Model Control; Iterative Learning Control

A multivariable IMC-PID method for non-square large time delay systems using NPSO algorithm

Multiple time delays and strong interactions among different loops are the main problems in the design of multivariable controller for non-square systems. In this paper, the concept of effective open-loop transfer function (EOTF) is extended to non-square systems. By applying the internal model control (IMC) method, the controllers with equivalent models are designed. For practical applications, the NPSO algorithm is used to obtain the parameters of the incremental PID with first-order lag filter. This new method does not only avoid the complex computation caused by the procedure of decoupling first and then designing controllers but also employs the advantages of IMC-PID's suitable for large time delay systems and strong robustness. Simulation is carried out to demonstrate the effectiveness of the proposed method; also significant performance improvement has been achieved with the proposed method compared with other methods.

Nonlinear Internal Model Control for Bearingless Induction Motor Based on Neural Network Inversion

The bearingless induction motor is a nonlinear, multi-variable and strong-coupled system. For this system, a novel internal model control strategy based on neural network αth-order inverse system theory is proposed to realize the decoupling control. By cascading the αth-order inverse model approximated by the dynamic neural network with the original system, the nonlinear bearingless induction motor system is decoupled into four independent pseudo-linear subsystems, that is, two radial displacement subsystems, a speed subsystem and a rotor flux subsystem. Then, the internal model control method is introduced to the four pseudo-linear subsystems to ensure the robustness and anti-jamming ability of the closed-loop system. The effectiveness and superiority of the proposed strategy are demonstrated by simulation and experiment.

A Nonlinear Dynamical Modeling and Control Method for the Vorticity Control of the Flow past a Circular Cylinder

In this paper a systematic approach for the nonlinear modeling and feedback control of vorticity behind an immersed circular cylinder system was developed. In this technique first a number of control input points over the cylinder and some measurement points for vorticity past the cylinder are assigned. A type of nonlinear dynamic model (namely a Hammerstein-Wiener (HW) model) of the flow field is estimated via system identification techniques using measurement data obtained from a chirp input function. Once the dynamical model of the system is estimated, a controller for the linear block of the HW model is designed using internal model control method, and this controller is then mapped to the HW model by reversing the input/output nonlinearity functions. The procedure described is implemented and tested numerically in MATLAB and CFD computations performed on the closed-loop system show that the controller is capable of achieving significant reduction in the vorticity levels past the cylinder.

Fuzzy Adaptive Internal Model Control Schemes for PMSM Speed-Regulation System

In this paper, the speed regulation problem for permanent magnet synchronous motor (PMSM) system under vector control framework is studied. First, a speed regulation scheme based on standard internal model control (IMC) method is designed. For the speed loop, a standard internal model controller is first designed based on a first-order model of PMSM by analyzing the relationship between reference quadrature axis current and speed. For the two current loops, PI algorithms are employed respectively. Second, considering the disadvantages that the standard IMC method is sensitive to control input saturation and may lead to poor speed tracking and load disturbance rejection performances, a modified IMC scheme is developed based on a two-port IMC method, where a feedback control term is added to form a composite control structure. Third, considering the case of large variations of load inertia, two adaptive IMC schemes with two different adaptive laws are proposed. A method based on disturbance observer is adopted to identify the inertia of PMSM and its load. Then a linear adaptive law is developed by analyzing the relationship between the internal model and identified inertia. Considering the control input saturation in practical applications, a fuzzy adaptive law based IMC scheme is developed based on apriori experimental tests and experiences, where a fuzzy inferencer based supervisor is designed to automatically tune the parameter of speed controller according to the identified inertia. The effectiveness of the proposed methods have been verified by Matlab simulation and TMS320F2808 DSP experimental results.