Multivariable Internal Model Control Research Articles

Artificial neural network based identification of process dynamics and neural network controller design for continuous distillation column

In this study, an equation based nonlinear autoregressive with exogenous input neural network model for a multiple input and output process was proposed to enhance the accuracy of the model, a control system’s speed, and robustness. A continuous lab-scale binary distillation column was considered for a case study, and an experiment was conducted. The forward and inverse neural network models have been developed and incorporated into a multivariable internal model control strategy to control the distillate and bottom compositions of the column simultaneously. These models demonstrated excellent performances, as supported by lower mean square error values of 2.73E-05 and 1.26E-04 for the forward and inverse models. Then, the proposed control method was applied to control both set point changes and disturbance changes, and in each case, it is contrasted with the traditional proportional-integral-derivative (PID) technique. The integrated absolute and integrated square errors for the proposed control were 0.1603 and 2.32E-03, respectively, while for PID control, these performance indexes were 0.7822 and 8.67E-03, respectively, for set-point tracking in top composition. Similarly, for bottom composition in tracking set points and rejecting disturbance, these performance indexes were also very less for the neural network control scheme.

Extended Approach to Analytical Triangular Decoupling Internal Model Control of Square Stable Multivariable Systems With Delays and Right-Half-Plane Zeros

The Analytical Triangular Decoupling Internal Model Control (ATDIMC) technique for 2×2 systems is generalized to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> × <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> systems ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ≥2) with delays and right-half-plane (RHP) transmission zeros. The formulation is done by first creating a triangular closed-loop transfer function matrix corresponding to the achievement of the triangular decoupling objective of restraining inverse-response and control-loop-interaction characteristics to a single plant output. Subsequently, the corresponding multivariable internal model controller is calculated, with transfer-function approximations made using optimization algorithm that minimizes the Integral Time-Weighted Absolute Error (ITAE) of the difference between the step responses of the original and reduced expressions. It is shown that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ATDIMC designs emerge that achieve the shifting of inverse responses and interactions to a least-desired output, with delays retained for all outputs and asymptotic tracking of setpoints achieved for all <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> outputs of each design. To mitigate the possible effect of severe interaction on the least-desired output, a modification of this formulation is performed to spread inverse-response behavior to a second output, while minimizing the interaction of that output with the initial least-desired output. Simulation results for selected 3×3 and 4×4 systems show the effectiveness of these propositions.

Centralized Robust Multivariable Controller Design Using Optimization

The paper deals with the determination of controller parameters for multivariable systems by means of parameter optimization. In order to reduce the computational burden, the starting point in the optimization is determined through information provided by the feedback controller parameterized using the multivariable internal model controller (IMC) computed from the plant's p/q moment approximant and the original plant's multi-loop controller parameter magnitudes. Thereafter, controller parameter optimization proceeds to completion using the MATLAB optimization toolbox which is widely available. A potency of this technique is that all performance specifications and stipulated constraints are easily accommodated in the problem formulation thus facilitating complete problem solution in one go. The technique has been found to be effective for all plants considered so far and generally produces closed loop systems with favorable characteristics when compared to systems designed by similar methods.

Attitude Controller Design of Multiple Independently Targeted Reentry Vehicle Based on Internal Model Control

A Multiple Independently Targeted Reentry Vehicle (MIRV) is a ballistic missile payload containing several warheads each capable of hitting one of a group of targets. In the process of missile flight control, the release of warheads brings about coupling to the missile attitude control system which will lower the flight stability. In order to solve this problem, a missile attitude controller, which combined the α-order integral inverse system with internal model principle, was presented. Firstly, determine the Post Boost Vehicle (PBV) attitude dynamics model. Then, combine the linearization of attitude dynamics equation with feed-forward decoupling method to implement the attitude decoupling. Finally, a two-degree of freedom (TOF) multivariable internal model controller was set up to optimize the control system performance. Simulation results show that the coupling of attitude control system has been eliminated. Compared with the original system, the internal model controller provides the control system better input-tracking performance, robust stability and interference suppression capacity.

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.

Global Harmonic Current Rejection of Nonlinear Backstepping Control with Multivariable Adaptive Internal Model Principle for Grid-Connected Inverter under Distorted Grid Voltage

Based on a brief review on current harmonics generation mechanism for grid-connected inverter under distorted grid voltage, the harmonic disturbances and uncertain items are immersed into the original state-space differential equation of grid-connected inverter. A new algorithm of global current harmonic rejection based on nonlinear backstepping control with multivariable internal model principle is proposed for grid-connected inverter with exogenous disturbances and uncertainties. A type of multivariable internal model for a class of nonlinear harmonic disturbances is constructed. Based on application of backstepping control law of the nominal system, a multivariable adaptive state feedback controller combined with multivariable internal model and adaptive control law is designed to guarantee the closed-loop system globally uniformly bounded, which is proved by a constructed Lyapunov function. The presented algorithm extends rejection of nonlinear single-input systems to multivariable globally defined normal form, the correctness and effectiveness of which are verified by the simulation results.

Boiler Combustion System of Multivariable Internal Model Control Application Research

In the industrial process of common multivariable time delay input/output system, a multivariable decoupling internal model controller design method is put forward based on the internal model control structure, the method for the design of internal model controller has the function of decoupling and controller. The advantage has the ability to achieve approximate or complete decoupling in nominal system output response, the method is used on the multivariable strong coupling of the boiler combustion control system to design and simulation study, the simulation results prove the effectiveness of the method. And in the process model and process mismatch showed strong robustness and anti jamming ability.

Design of a multivariable internal model controller based on singular value decomposition

In this article, a novel internal model control (IMC) approach based on singular value decomposition (SVD) is proposed for the control of multiple-input–multiple-output (MIMO) systems with multiple time delays. This approach achieves decoupling using a compensation term and improves the robustness using SVD in the inverse of the steady-state gain matrix of process. Meanwhile, a novel filter is designed for decoupling and fast response speed of multivariable systems with multiple delays. The design of the controller can be extended to non-square systems where there are more inputs than outputs. Examples are included to illustrate the effectiveness of the method. © 2012 Canadian Society for Chemical Engineering

Decoupling control design via linear matrix inequalities

A new method for the design of multivariable internal model control (IMC) is proposed, aimed at obtaining good loop performance and small loop couplings based on linear matrix inequalities (LMIs). The decoupling design with performance constraints is formulated into an optimisation problem with LMI constraints, and thus the problem can be solved effectively. Robust stability is analysed and simulations show that good control performance can be achieved.

Model based control of a four-tank system

A multi-disciplinary laboratory for control education has been developed at the University of Delaware to expose students to realistic process system applications and advanced control methods. One of the experiments is level control of a four-tank system. This paper describes two model-based methods students can implement for control of this interacting four-tank system. Sub-space identification is used to develop an empirical state space model of the experimental apparatus. This model is then used for model based control using internal model control (IMC). This represents an application of inner—outer factorization for non-minimum phase multivariable IMC design. Modeling is also performed using step tests and Aspen software for use with dynamic matrix control (DMC).

Multivariable nonlinear internal model control applied to a bioreactor

Abstract Nonlinear control has emerged as an area of extensive research activity during the last decade. An important class of problems concerns the study of disturbance inputs for analysis and controller synthesis purposes. This paper describes some aspects of the design of mullivariable non-linear internal model control in the case of continuous flow-fermentation process with several types of modelling errors. The controller is based on the inverse of the process model and a non-linear filter. The approach used get rid of the assumption of full state feedback. Simulation results for a continuous stirred tank reactor illustrate the good behaviour of the control strategy

Operating an ideal heat integrated distillation column with different control algorithms

The operation of an ideal heat integrated distillation column is addressed. Five control strategies, namely, single loop composition control system, multi-loop composition control system, multivariable internal model control (IMC) system, modified multivariable IMC control (MIMC) system and nonlinear process model based control (NPMC) system, are design and applied to the process. Simulation results demonstrate that all the control systems are capable of keeping both end products on their specifications. The NPMC is the best one in the operation of the ideal HIDiC. It can very smoothly realize setpoint transition and efficiently counteract external disturbances. The MIMC ranks the second for the regulatory responses to feed composition disturbances have relative longer setting time. The next comes to the multi-loop composition control system. Although it works well for the operation of the ideal HIDiC, it produces relative larger oscillations and longer setting time in the system responses. The IMC can not compete with the multi-loop composition control system because it is extremely sensitive to operating condition changes. The single-loop composition control system is the worst one for the responses of the uncontrolled product is extremely sluggish.

Identification and Internal Model Control of an Ideal Heat Integrated Distillation Column (HIDiC).

An appropriate structure for a reduced-order model of transfer function type is first proposed for an ideal heat integrated distillation column (HIDiC) based on the process dynamics. The reduced-order model could well represent the process not only in steady state but dynamic state as well. A multivariable internal model control (IMC) system is designed and applied to the ideal HIDiC. The simulation results demonstrate that the IMC controller can deal with the interactive nature of the ideal HIDiC more effectively than PI controllers, indicating the IMC controller could be a better means for operation of the ideal HIDiC in the case where high system performance is required. However, the ideal HIDiC is extremely sensitive to changes in operating conditions and this makes it necessary to adopt an on-line model adaptation mechanism to facilitate the performance of the IMC controller.

Multivariable controller design based on shrinking process poles

A systematic method based on the pole placement approach is proposed to design non-square multivariable internal model control systems. The control law is a kind of the classical IMC controller for square MIMO systems, but process model factorization and inversion are combined into the derivation of the approach. The control law can be extended to handle any minimum or non-minimum phase n input and m output systems. The absolute value of the implemented tuning parameters is restricted to (0,1) to guarantee internal stability of the control system. If all the parameters are set equal to one, the resulting closed-loop responses follow the conservative behaviour in simplified model predictive control. Alternatively, if the parameters are shrunk to zero, the proposed control law becomes multivariable deadbeat control.

The Design and Implementation of a Multivariable Internal Model Controller for a Continuous Polybutadiene Polymerization Train

A systematic procedure for the design and implementation of a multivariable controller for a commercial scale polymerization reactor is presented. The reactor characteristics are typical of many industrial processes, including unmeasured disturbances, significant deadtimes, and sampling and measurement errors. Closed loop experimentation is used to generate appropriate input/output data. Principal component analysis coupled with partial least squares regression techniques are applied to the data to reduce the dimensionality of the identification and control problem. An identified discrete transfer function model was formulated and successfully implemented in an internal model controller structure. Results indicate a reduction in polymer product variability attributed to consistent feedback manipulations and identification of output variable dependency.

Multivariable internal model control for a full-scale industrial distillation column

A multivariable internal model control system was developed for a full-scale distillation column. The basis for the design of the control is a two-input/two-output matrix of linear transfer functions describing column dynamics. The control system is designed to perform dual composition control of the overhead and bottom products. Hardware constraints dictate multirate sampling of the product concentrations. In addition, the column exhibits inverse response to changes in reflux flow. The multirate sampling is addressed by a unique implementation of a feedforward decoupling control. A reduced-order controller design technique is developed to handle the non-minimum-phase behavior of part of the column. Simulations demonstrate the improvement in control obtained from this approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>