Multivariable Control Design Research Articles

Multivariable PID controller design tuning using bat algorithm for activated sludge process

The designing of a multivariable PID control for multi input multi output is being concerned with this project by applying four multivariable PID control tuning which is Davison, Penttinen-Koivo, Maciejowski and Proposed Combined method. The determination of this study is to investigate the performance of selected optimization technique to tune the parameter of MPID controller. The selected optimization technique is Bat Algorithm (BA). All the MPID-BA tuning result will be compared and analyzed. Later, the best MPID-BA will be chosen in order to determine which techniques are better based on the system performances in terms of transient response.

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

Multivariable loop-shaping control design for stability augmentation and oscillation rejection in wide-area damping using HVDC

Two high-voltage DC (HVDC) transmission lines known as the Pacific DC Intertie and Intermountain DC line operate in the western North American Power System. These HVDC lines have the potential to be capable actuators in both single-input, single-output (SISO) and multivariable wide-area damping controllers. Implementation of a multivariable design is investigated and compared to two SISO controllers implemented simultaneously using these actuators. Control synthesis is executed using a robust multivariable auto-synthesis methods with modifications based on closed-loop behavior. Sensitivity weighting functions which result in controllers that emphasize feedback at troublesome frequencies while meeting prescribed bandwidth restrictions are applied. Performance criteria include transient responses to large contingency events and disturbance rejection. A forced oscillation is used to perturb the system at an eigenfrequency of interest to determine the effectiveness of each controller in rejecting oscillatory disturbances system-wide.

A shifting pole placement approach for the design of performance-varying multivariable PID controllers via BMIs

In this paper, the design of a performance-varying multivariable Proportional-Integral-Derivative (PID) controllers is presented. The main objective is to provide a framework for changing online the closed-loop behavior of the controlled system using the shifting pole placement approach. In order to carry out this target, the PID design problem is transformed into a static output feedback design problem which is analyzed through the linear parameter-varying (LPV) paradigm. An academic example is used to demonstrate the effectiveness of the proposed approach.

Multivariable Controller Design Achieving Diagonal Dominance Using Frequency Response Data

SUMMARYA model‐based controller design method is widely used in the industrial field. However, in this method, a modeling error deteriorates the control performance. This article proposes a multivariable controller design method using only frequency response data sets. This method omits the time‐consuming model identification and designs a controller directly from raw data sets of the controlled plant. The proposed method provides the parameters of a PID controller using convex optimization subject to stability constrains. The optimization problem is formulated in terms of the diagonal dominance of the system. The designed controller suppresses an interaction and achieves high gain of the system in the case of highly diagonal dominance. The stability of MIMO systems can be evaluated by eigenvalue loci without overestimation of eigenvalue loci. The formulated optimization problem involving nonconvex functions and/or constraints is solved using concave–convex procedure that solves a problem written as a difference between two convex functions. The experimental results show the effectiveness of the proposed method in comparison with the conventional method.

Control multivariable lineal con desacoples en un intercambiador de calor

El presente artículo describe la validación de los diseños de control multivariable lineal en un intercambiador de calor bajo ambiente virtual usando Hysys, cuyo interés se centró en analizar las diferentes respuestas del sistema MIMO cuando se realizó el control regulatorio con diferentes desacopladores y sin desacoplador. Se usaron tres tipos de desacopladores: Simplificado de Estado Estacionario, Simplificado y Generalizado. Los valores de las consignas fueron 60 °C para la temperatura del caudal a la salida de los tubos, y 41 °C para la temperatura del caudal a la salida de la carcasa; las variables manipuladas fueron los caudales de entrada a los tubos y la carcasa, gracias a los porcentajes de apertura de las válvulas correspondientes. Se emplearon controladores PI que fueron sintonizados con la aplicación del diseño IMC. De los resultados se pudo apreciar que el desacople simplificado fue el que más disminuyó el efecto del acoplamiento.

凸最適化による制御仕様を保証する周波数応答を用いた多変数固定構造制御器設計法

凸最適化による制御仕様を保証する周波数応答を用いた多変数固定構造制御器設計法

Small-signal modeling and multivariable PI control design of VSC-HVDC transmission link

This paper employs a multivariable PI (proportional-integral) controller for inner-loop control and SISO (single-input single-output) PI controllers for outer-loop control for a VSC-HVDC (voltage source converter-based high voltage direct current) transmission system. To design the control system, first, a state-space model of VSC-HVDC transmission link is obtained in dq-coordinate frame. Then, using linearization technique the small-signal model is obtained. Next, a multivariable optimal PI controller is designed for the (inner-loop) linearized model using LQR (linear quadratic regulator) approach. Finally, the outer-loop SISO PI controller parameters are obtained using sequential design method (i.e., assuming that the inner-loop is must faster than the outer-loop). The advantage of using the MIMO (multi-input multi-output) PI controller over the conventional decouple-based PI controller for inner-loop control is studied through different performance and robustness measures, and also verified through simulations in presence of several disturbances and parameter variations.

Design and assessment of variable-structure LQG PID multivariable controllers

Summary Control Performance Assessment (CPA) and tuning of PID controllers are studied in this paper. We propose a framework for systematic analysis of the tradeoff between the structural complexity of the controller and its performance. As the measure of the controller performance, an LQG based index is used. The problem is augmented with an additional term which forces sparsity on the complexity of a decentralized PID controller. The desired complexity is controlled via a weighting parameter which determines the cost of each additional element (i.e., P, I, and D terms). The result is a decentralized multivariable PID controller in which the complexity of each loop controller is optimized such that the desired LQG Performance Index is achieved with the lowest controller complexity. For larger multivariable systems, this will translate into a substantially reduced set of controller parameters. Copyright © 2016 John Wiley & Sons, Ltd.

Three-Dimensional Impact Angle Guidance Laws Based on Model Predictive Control and Sliding Mode Disturbance Observer

This paper presents a suboptimal three-dimensional guidance law to intercept unknown maneuvering targets with terminal angle constraint using multivariable control design. The presented guidance law is essentially a composite control method, which is constructed through a combination of standard continuous model predictive control (MPC) and adaptive multivariable sliding mode disturbance observer (SMDO). More specifically, the MPC method is utilized to obtain optimal line-of-sight (LOS) angle tracking performance for nonmaneuvering targets, while the SMDO technique is used to estimate and compensate for the unknown target maneuver online. By virtue of the adaptive nature, the proposed guidance law does not require any information on the bounds of target maneuver and its gradient except for their existence. The stability of the closed-loop guidance system is also analyzed by using Lyapunov function method. Simulation results clearly confirm the effectiveness of the proposed formulation against a maneuvering target.

Design and Development of a Multivariable Control for a Distillation System Using Fuzzy Logic

Most systems have several measurement variables to be controlled and several manipulated variables. Many traditional industrial processes are essentially multivariable nature boilers, blends, paper plants, distillation columns, chemical reactors, heat exchangers, steam distribution networks, air conditioning systems, etc. A unique phenomenon of these systems is the interaction occurring among their variables, so that an input affects several output variables, which can greatly hinder the design of the control system. This paper presents the design of a multivariable decoupledcontroller implementing fuzzy logic using the method of Mamdaniwith the MATLAB fuzzy toolbox. The previous, through the validation of the Multi-Input Multi-Output (MIMO) system of the binary distillation column are used for separating methanol and water. The results of simulation cases studied are presented.

Multivariable Offset-Free Model Predictive Control for Quadruple Tanks System

The design and implementation of a robust multivariable model predictive control (MPC) on a quadruple tanks system (QTS) is addressed in this paper. Mismatch between the MPC’s model and the process may cause constraint violation, nonoptimized performance, and even instability. It is the objective of this paper to offset-free control the process in the presence of constraints and model mismatch. It is shown in this paper how this model mismatch is compensated by augmented state disturbances, and also how the steady-state error is eliminated. In the proposed method, an observer is designed to estimate the disturbances and states. The results show how the proposed control method increases the robustness of the model predictive controller in simulation and in real-time implementations on a new QTS proposed in this work together with techniques designed to identify the parameters of this novel plant.

Multi-variable variable gain controller design and simulation based on aero engine

Multi-variable variable gain controller design and simulation based on aero engine

周波数応答を用いた対角優勢を実現する多変数制御器設計

A model-based controller design method is widely used in the industrial field. However, in this method, a modeling error deteriorates the control performance. This paper proposes a multivariable controller design method using only frequency response data sets. This method omits the time-consuming model identification and designs a controller directly from raw data sets of the controlled plant. The proposed method provides the parameters of a PID controller using convex optimization subject to stability constrains. The optimization problem is formulated in terms of the diagonal dominance of the system. The designed controller suppresses an interaction and achieves high gain of the system in the case of highly diagonal dominance. The stability of MIMO systems can be evaluated by eigenvalue loci without overestimation of eigenvalue loci. The formulated optimization problem involving nonconvex functions and/or constraints is solved using concave-convex procedure that solves a problem written as a difference between two convex functions. The experimental results show the effectiveness of the proposed method in comparison with the conventional method.

Sequential design of controller for grinding circuit

Multivariable controller design for grinding circuit is the subject of this work. Model of grinding circuit in Majdanpek Copper Mine is subjected to the newly proposed sequential design method, based on the Gauss-Jordan factorization and Nyquist-like techniques. Due to the nature of the applied design method, generalization to any other grinding circuit is straightforward. An extensive simulation analysis proved the effectiveness of proposed design in reference tracking, even in the case of some loop failures.

Multivariable controller tuning for suppression of closed-loop interaction using frequency response dataset

This paper proposes a multivariable controller design method using frequency response data. The proposed method evaluates diagonal dominance in a closed-loop manner by shaping singular values and designs a controller by solving an optimisation problem that involves non-convex quadratic matrix inequalities. This optimisation problem is rewritten as linear matrix inequalities (LMI) by introducing iterative LMI constraints. The highly diagonal dominant controller is designed by repeating update of the optimisation problem and convex optimisation. This optimisation algorithm guarantees monotonically convergence of an evaluation value in terms of iteration. The experimental results show the effectiveness of the proposed method in comparison with the conventional method.

Multivariable controller tuning for suppression of closed-loop interaction using frequency response dataset

This paper proposes a multivariable controller design method using frequency response data. The proposed method evaluates diagonal dominance in a closed-loop manner by shaping singular values and designs a controller by solving an optimisation problem that involves non-convex quadratic matrix inequalities. This optimisation problem is rewritten as linear matrix inequalities (LMI) by introducing iterative LMI constraints. The highly diagonal dominant controller is designed by repeating update of the optimisation problem and convex optimisation. This optimisation algorithm guarantees monotonically convergence of an evaluation value in terms of iteration. The experimental results show the effectiveness of the proposed method in comparison with the conventional method.

Incremental multivariable predictive functional control and its application in a gas fractionation unit

The control of gas fractionation unit (GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay. PID controllers are still applied in most industry processes. However, the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process. In this work, an incremental multivariable predictive functional control (IMPFC) algorithm was proposed with less online computation, great precision and fast response. An incremental transfer function matrix model was set up through the step-response data, and predictive outputs were deduced with the theory of single-value optimization. The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm, and thereby making the control variable smoother. The predictive output error and future set-point were approximated by a polynomial, which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory. Then, the design of incremental multivariable predictive functional control was studied. Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.

Multivariable Negative-Imaginary Controller Design for Damping and Cross Coupling Reduction of Nanopositioners: A Reference Model Matching Approach

This paper presents the design and experimental implementation of a novel multi-input multi-output (MIMO) control structure using two negative-imaginary damping controllers to damp the first resonant mode and attenuate cross coupling effects between the axes of a piezoelectric tube scanner. The dynamics of the scanner in the lateral and longitudinal axes are identified from measured data and the design of the controller using a MIMO framework is done based on a reference model matching approach. The controller proposed in the paper is able to achieve a bandwidth near to the first resonance frequency of the scanner and the proposed controller is robust against changes in resonance frequencies that are due to load changes on the scanner. Experimental results are presented to show the effectiveness of the proposed controller.

Diversity Controlled Self Adaptive Differential Evolution based design of non-fragile multivariable PI controller

This paper proposes the design of non-fragile multivariable PI controller for industrial Multi Input Multi Output (MIMO) processes using Diversity Controlled Self-Adaptive Differential Evolution with Local Search (DCSaDE-LS) algorithm. The proposed non-fragile multivariable PI controller design is formulated as a single objective optimization problem with the objective of robustness subject to non-fragileness constraint. Monte Carlo Simulation is used to evaluate the non-fragileness constraint against stochastic uncertainty of controller parameters. Wood and Berry Distillation process and a real time Level-Temperature reactor process model with simple de-coupler are considered as a test systems. For constraint handling, Constraint Domination Principle (CDP) and Penalty Parameter Less (PPL) methods are incorporated in DCSaDE-LS, seperately. Also, the performance of DCSaDE-LS is compared with the recent adaptive DE algorithms namely Ensemble of mutation strategies and control parameters in Differential Evolution (EPSDE), Composite Differential Evolution (CoDE) and SaDE. The robustness and non-fragileness of the controller is analyzed under +40% and −40% variations in nominal process and controller parameters. The results demonstrated that the DCSaDE-LS designed non-fragile multivariable PI controller has good time domain performance, better robustness and non-fragileness than the reported multivariable PI controllers namely Maghade-PI, IMC-PI and NDT-PI.