Design Of Multivariable Control Systems Research Articles

PID controller design of TITO system based on ideal decoupler

The proposed method for designing multivariable controller is based on ideal decoupler D( s) and PID controller optimization under constraints on the robustness and sensitivity to measurement noise. The high closed-loop system performance and robustness are obtained using the same controller in all loops. The method is effective despite the values and positions of the right half plane zeros and dead-times in the process transfer function matrix G p( s). The validity of the proposed multivariable control system design and tuning method is confirmed using a test batch consisting of Two-Input Two-Output (TITO) stable, integrating and unstable processes, and one Three-Input Three-Output (TITO) stable process.

A Design of Discrete-Time Multivariable Iterative Learning Control Systems with Interactor Matrix

In this paper, it is proposed a discrete-time multivariable iterative learning control (ILC) algorithm which guarantees the monotonic convergence of the tracking error norms along with the number of the trials. It is pointed out that an interactor matrix plays an important role in the iterative learning control. Since ILC is a design method not requiring plant parameters explicitly, the information on the interactor matrix should be treated same manner as in an adaptive control systems. In this paper, it is presented a design of ILC using G-interactor which is derived not requiring the plant parameters. It is also presented a design of ILC using a diagonal precompensator which will make the interactor of the compensated plant be diagonal. Numerical simulations are presented to confirm the validity of the proposed design.

Fundamental Analysis of the Static VAr Compensator Performance Using Individual Channel Analysis and Design

In this paper the performance of a synchronous generator – SVC system is evaluated using Individual Channel Analysis and Design (ICAD), a control-oriented framework suitable for small-signal stability assessments. The SVC is already a mature piece of technology, which has become very popular for providing fast-acting reactive power support. The great benefits of ICAD in control system design tasks are elucidated. Fundamental analysis is carried out explaining the generator dynamic behavior as affected by the SVC. A multivariable control system design for the system is presented, with particular emphasis in the closed-loop performance and stability and structural robustness assessment. It is formally shown in the paper that although the addition of the SVC with no damping control loop does not improve the dynamic of the system, its inclusion is very effective in enhancing voltage stability. Moreover, ICAD analysis shows that with the use of the SVC the dynamical structure of the system is preserved and no considerable coupling or adverse dynamics are added to the plant.

Multiscale theory for linear dynamic processes: Part 1. Foundations

Multiscale models of linear dynamic systems offer a representation that links states at different time scales over distinct time periods. As such they allow a natural bridging of scales (ranges of frequencies) and time, and are more attractive than models defined in the time- or frequency-domain, alone. The term “multiscale model”, as used in this two-part series, is quite different from the notion of a “multiscale model”, as this is used in recent publications, where in essence such model bridges the states of a system at two distinct scales (an atomistic scale of fast elementary phenomena, and a macroscopic scale characterizing slow continuum-based physics), described by two distinctly different physical models. The multiscale dynamic models, introduced in this paper, link state dynamics over a number of distinct time scales, and are in essence “multiresolution” facets of a single model. The outgrowth of a series of developments, which came about with the advent of the wavelet decomposition for the analysis of discrete signals, multiscale models are defined on dyadic trees, which span the time-scale space. The nodes of such trees are used to index the values of states, inputs and outputs, modeling errors, and measurement errors of dynamic systems. These values are localized in both time and scale (range of frequencies), and thus they offer a hybrid domain that is particularly conducive for estimation and control problems. In Part 1 of this series, which focuses on the foundations of a multiscale systems theory, we will describe the generation of multiscale models from their time-domain counterparts, and develop conditions governing their stability, controllability and observability. In addition, we will address the development of algorithms for the solution of basic problems, such as: optimal control, state estimation, and optimal fusion of measurements and control actions at various scales. It will be shown that multiscale models lend themselves nicely to construction of very effective parallelizable algorithms. In Part 2 of this series, we will apply all these developments to the formulation of a Multiscale Model Predictive Control (MS-MPC) approach and we will show that MS-MPC is a more attractive framework for the design and implementation of multivariable control systems.

A multi-crossover genetic approach to multivariable PID controllers tuning

In this paper, we will propose a modified crossover formula in genetic algorithms (GAs), and use this method to determine PID controller gains for multivariable processes. It is well known that GA is globally optimal search method borrowing the concepts from biological evolutionary theory. In the traditional crossover fashion, only two parent chromosomes are usually used to be crossed by each other. The proposed algorithm, however, is designed to provide a more accurate adjusting direction for evolving offspring because of the use of multi-crossover genetic operations. Then we apply the innovative GA into the design of multivariable PID control systems for deriving optimal or near optimal control gains such that the defined performance criterion of integrated absolute error (IAE) is minimized as much as possible. Finally, a 2 × 2 multivariable controlled plant with strong interactions between input and output pairs will be illustrated to demonstrate the effectiveness of the proposed method. Some comparison results with the traditional GA and BLT method are also demonstrated in the simulation.

Multivariable PID Control Systems Design by Stable Partial Model Matching on Frequency Domain

In this paper, a tuning method of multivariable PID controllers based on partial model matching on frequency domain is proposed. Different from the previous methods which were considered by authors, in this case, determination of multivariable PID controller parameters is defined as a nonlinear optimization problem with inequality constraints. In this procedure, the objective function is constructed so as to minimize the relative frequency model error between the loop transfer function of the control system and that of reference model system at finite frequency points. Inequality constraints are introduced so as to guarantee the stability criteria of multivariable control system given in Rosenbrock's stability theorem. The effectiveness of the method is illustrated through a simulation of 2-input 2-output system.

A practical approach for the design of multivariable control systems

A practical approach for the design of multivariable control systems

Plasma multivariable robust control system design and simulation for a thermonuclear tokamak-reactor

The paper reports results on the design and analysis of the multivariable feedback Hinfin; robust system for plasma current, position and shape control in the fusion energy advanced tokamak (FEAT) developed in the International Thermonuclear Experimental Reactor (ITER) project. The system contains the fast loop with the SISO plasma vertical speed robust controller and the slow loop with the MIMO plasma current and shape robust controller. The goal is to study the resources of the system robustness to achieve a higher degree of the FEAT operation reliability. Two Hinfin; block diagonal controllers {K SISO, K MIMO} were designed by a mixed sensitivity approach in the framework of the disturbance rejection configuration. These controllers were compared with block diagonal decoupling, PI and LQG controllers at the set of FEAT key scenario points according to the multiple-criterion: nominal performance at minor disruptions, robust stability and robust performance. The Hinfin; controllers showed larger multivariable stability margin and better nominal performance.

Quantitative non‐diagonal controller design for multivariable systems with uncertainty

AbstractThe loop coupling reduction of multivariable systems under the presence of plant uncertainty is currently a most discussed topic. Following the ideas suggested by Horowitz, in this paper the role played by the non‐diagonal controller elements is analysed in order to state a design methodology. Thus, the definition of a coupling matrix and a quality function of the non‐diagonal elements come into use to quantify the amount of loop interaction and to design the controllers, respectively. This yields a criterion that makes possible to propose a sequential design methodology of the fully populated matrix controller, in the quantitative feedback theory (QFT) robust control frame. Finally, a real example with the heat exchanger of a pasteurization plant is included to show the practical use of this technique. Copyright © 2002 John Wiley & Sons, Ltd.

A HISTORICAL VIEW OF MULTIVARIABLE FREQUENCY DOMAIN CONTROL

The late sixties and early to mid seventies saw and explosion of interest and innovation in the area of frequency domain methods in the analysis and design of multivariable control systems. The emergence of multivariable frequency domain control design techniques in the 1960's and 1970's was based on traditional views of the role of the designer in the design process. Classical influences on the emerging Nyquist and root-locus theory were therefore natural and, it can be argued, very successful. This paper presents a personal review of the ideas, concepts and techniques from the perspective of a researcher active in the area at the time. The paper will aim to provide a personal insight into the issues and provide an opportunity for young researchers to review the wide range of contributions made before the current use of H-infinity (and related optimisation) methods become so prevalent. Copyright© 2002 IFAC

A SIMULATION AND ANIMATION TOOL FOR STUDYING MULTIVARIABLE CONTROL

A simulation and animation tool for education in multivariable control is presented. The purpose of the tool is to support studies of various aspects of multivariable dynamical systems and design of multivariable feedback control systems. Different ways to use this kind of tool in control education are also presented and discussed.

Automatic design of multi-variable quantitative feedback theory control systems via evolutionary computation

This paper proposes a multi-objective evolutionary automated design methodology for multi-variable quantitative feedback theory (QFT) control systems. Unlike existing analytical and convex optimization-based QFT design approaches, the evolutionary ‘intelligent’ technique is capable of automatically evolving both the nominal controller and the pre-filter simultaneously to meet the usually conflicting multiple performance requirements in QFT, without going through the sequential and conservative design stages for each of the multi-variable subsystems. In addition, it avoids the need of manual QFT bound computation and trial-and-error loop-shaping design procedures, which are particularly useful for multi-variable or unstable plants where stabilizing controllers may be difficult to synthesize. The effectiveness of the proposed QFT design methodology is validated upon a benchmark multi-variable system, which offers a set of low-order Pareto optimal controllers satisfying all closed-loop performances under practical constraints.

出力むだ時間をもつ多変数制御系のコントローラ・パラメトリゼーションによる設計

出力むだ時間をもつ多変数制御系のコントローラ・パラメトリゼーションによる設計

A Multivariable Control System Design which Considers Plural Control Purposes Using Partial Model Matching on Frequency Domain and Its Experimental Evaluation by Pilot-Scale Career in the Warehouse.

Recently, authors have proposed a multivariable linear controller design method based on partial model matching on frequency domain. The practical effectiveness of the method was also confirmed through several experiments using complex mechanical experimental equipment. The application was set limit to the so-called regulator problem including a design of vibration control system. However, there exist many complicated mechanical control systems such that we have to solve regulator problem and tracking control problem simultaneously. This paper is concerned with a design technique of multivariable linear controller on frequency domain, which attains regulator control purpose and tracking control purpose at the same time. The effectiveness of the proposed method is confirmed through an experiment of vibration suppression control and positioning control of pilot scale career in the warehouse with a flexible vibratory pendulum system.

Iterative Learning Identification and Design of Industrial Furnace Process Control

An applications oriented control systems engineering approach for a class of well- posed thermal systems, e.g. industrial furnaces and ovens, that is consistent with most of theoretical results in systems and control sciences has been elaborated and tested in designing controls for several industrial-scale furnaces. It provides a methodology for iterative learning and resolving process identification and control design for multi-variable systems within a discrete convolution framework and using truncated k-time sequence matrices of characteristic input-output modes as well as their characteristic patterns and singular characteristic patterns, starting with standard non-parametric process time-domain models identified under operating conditions. Within computer process control environment and for practical engineering and maintenance reasons, digital implementations are sought in terms of partial steady-state decoupling and two-term laws or combination of certain MIMO and SISO controls. The pusher furnace in Skopje Steelworks is used to illustrate this methodological approach

A normalizing precompensator for the design of effective and reliable commutative controllers

Sensitivity to parameter perturbation represents the main caution regarding the use of the characteristic locus method on the design of multivariable control systems. The method is not effective when the condition number of the plant eigenvector matrix is high or, equivalently, when the plant transfer matrix differs a great deal from normality. With the view to coping with this problem, it is proposed in this paper a precompensator structure and, in the sequel, two optimization problems are formulated and solved: the first one, for 2 x 2 systems, aims at minimizing the eigenvector matrix condition number; the second one, for the general m x m case, is intended to make the precompensated system as normal as possible by minimizing a defined measure of normality. Once the precompensated system matrix has been made close to a normal one, the characteristic locus method can then be applied effectively, leading to reliable control systems, as far as stability in face of uncertainty is concerned.

A systematic method for design of multivariable fuzzy logic control systems

This paper proposes a systematic method to design a multivariable fuzzy logic controller for large-scale nonlinear systems. In designing a fuzzy logic controller, the major task is to determine fuzzy rule bases, membership functions of input/output variables, and input/output scaling factors. In this work, the fuzzy rule base is generated by a rule-generated function, which is based on the negative gradient of a system performance index; the membership functions of isosceles triangle of input/output variables are fixed in the same cardinality and only the input/output scaling factors are generated from a genetic algorithm based on a fitness function. As a result, the searching space of parameters is narrowed down to a small space, the multivariable fuzzy logic controller can quickly constructed, and the fuzzy rules and the scaling factors can easily be determined. The performance of the proposed method is examined by computer simulations on a Puma 560 system and a two-inverted pendulum system.

Generalization of integral constraints on sensitivity to time-delay systems

Extends results on integral constraints to time-delay systems. Key technical issues in this extension include properties of nonminimum phase zeros, the convergence of Blaschke products, and the Poisson integral. The results have implications for scalar and multivariable control system design as well as in filtering problems.

Knowledge-based multivariable PID control

Knowledge-based control has become an important approach towards the realization of intelligent control systems. To date, multivariable control system design is restricted to off-line design aided possibly by a knowledge-based system. A first-generation on-line knowledge-based multivariable PID control system is implemented in this paper. Real-time considerations are taken into account. The integration of the knowledge-based system with conventional process control system software and hardware is achieved through an interfaced approach. Heuristics developed in recent research on intelligent PID control is implemented to attain some of the visionary goals of knowledge-based control. An implementation on a laboratory scale heat exchanger is also given.

ROBUST DECOUPLING CONTROL BASED ON EXTERNAL LOOPS

Decoupling control can substantially simplify the design of multivariable control systems. However, decouplers are usually sensitive to modeling errors, especially for the systems with relative gains being far away from unity. Hence, decoupling control is limited in practical application. In the present study, robust control of a decoupling system with external loops is developed. Design of the control system consists of two steps. The first step is to design the decoupler and the controller based on the nominal plant. The second step is to design the filters in the external loops by using a robust stability criterion. The robust control is implemented without changing the original design of the decoupler and the controller. The control system with external loops for decoupling control significantly improves the control performance. The proposed robust control enhances the applicability of decoupling control.