Multivariable Predictive Control Algorithm Research Articles

Predictive control based double-electrode submerged arc welding for filet joints

Double-electrode submerged arc welding (DE-SAW) is an innovative process to achieve the same metal deposition rate at reduced heat input. To maximally take advantage of this new capability, the heat input needed to produce acceptable welds needs to be reduced. A joint gap is successfully introduced to reduce the required heat input but it introduces variations affecting currents/heat input. To minimize the resultant variations in currents and resultant welds, the process is modeled and a multivariable predictive control algorithm is developed to control the currents at desired levels. Experiments verified the effectiveness of the resultant feedback controlled DE-SAW process.

Lessons learned from closed loops in engineering: towards a multivariable approach regulating depth of anaesthesia

In this paper is presented a brief state of art regarding the multivariable formulation for controlling the depth of anaesthesia by means of two intravenously administrated drugs, i.e. propofol and remifentanil. In a feasibility study of determining a suitable variable to quantify analgesia levels in patients undergoing cardiac surgery, the bispectral index and an electromyogram-based surrogate variable are proposed as the controlled variables. The study is carried on in the context of implementing a multivariable predictive control algorithm. The simulation results show that such a paradigm is feasible, although it does not guarantee perfect knowledge of the analgesia level-in other words, the variable is not validated against typical evaluations of the pain levels (e.g. clinical scores).

Towards a Multivariable Model for Controlling the Depth of Anaesthesia using Propofol and Remifentanil

In this paper is presented a brief state of art regarding the multivariable formulation for controlling the depth of anaesthesia by means of two intravenously administrated drugs, i.e. Propofol and Remifentanil. In a feasibility study of determining a suitable variable to quantify analgesia levels in patients undergoing cardiac surgery, the Bispectral index and an electromyogram-based surrogate variable are proposed as the controlled variables. The study is carried on in the context of implementing a multivariable predictive control algorithm. The simulation results show that such a paradigm is feasible, although it does not guarantee perfect knowledge of the analgesia level – in other words, the variable is not validated against typical evaluations of the pain levels (eg. clinical scores).

Constrained multivariable stable predictive control based on Toeplitz equation

A stable constrained multivariable predictive control algorithm based on the Toeplitz predictive equation is presented. An analytical method for transformation between left and right coprime factorization of multivariable system is put forward, and then an unconstrained control law and its stable condition are given. Furthermore, for the constrained multivariable controlled system, a predictive controller and its asymptotically stable condition are presented and proved. Finally, the constrained receding horizon optimization is transformed into a typical linear matrix inequality (LMI) problem and solved. The detailed control algorithm is depicted in a numerical example, and the simulation results reveal the effectiveness and practicability of the presented algorithm. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

Multivariable Predictive Control of Thin Film Deposition Using a Stochastic PDE Model

In this work, we focus on a thin film deposition process which takes place on a two-dimensional (2D) lattice and is governed by three microscopic processes including molecule adsorption, surface migration, and desorption. A 2D linear stochastic partial differential equation (PDE) model is initially constructed which describes the spatio-temporal evolution of the film surface. Then, the control problem is formulated as the one of regulating the thin film thickness and surface roughness by manipulating the substrate temperature and adsorption rate. Subsequently, a computationally efficient multivariable predictive control algorithm is developed which uses a finite-dimensional approximation of the stochastic PDE model to regulate the thin film thickness and surface roughness at desired levels at the end of the deposition. The predictive controller is then applied to the kinetic Monte Carlo simulation of the deposition process. Closed-loop system simulation results demonstrate that the model is adequately accurate and that the controller is effective in enforcing the desired control objectives and reducing film variance.

Robust multivariable predictive control: an application to an industrial test stand

The air conditioning system of an aircraft is used to regulate the cockpit temperature and pressure during flight and usually generates its airflow from the compressor turbine of the jet engine. Testing an air conditioning system requires simulation of the running conditions at ground level. The article concerns the application to such a simulation of /spl alpha/-MPC, a robust extension of the initial multivariable predictive control (MPC) law that improves the disturbance-rejection properties of the closed-loop system, reducing the H/sup /spl infin//-norm of the multivariable sensitivity function with an extra parameter. This augmented algorithm has been chosen to carry out the new tests on the industrial process. Experimental recordings reported here have confirmed significant performance improvement with this new approach relative to the former PID regulation. The article is organized as follows. First we introduce the original MPC. Next we describe the extended /spl alpha/-MPC algorithm and analyze the robustness of the closed-loop system through the H/sup /spl infin// approach. Then we discuss the methodology of the control design task and describe the experimental test stand, focusing on the software and hardware implementation. Finally, we report the results of the /spl alpha/-MPC control law on the actual test stand. Special attention is given to the comparison with the former control system.

Development and Application of Generalized Dual-Target Predictive Control

This paper describes the development and application of a multi-variable predictive control algorithm referred to as generalized dual-target predictive control (GDTPC). The algorithm allows specification and tracking of both input and output set points in an optimal manner while limiting the size of input moves as required by many industrial processes. With recursive least-squares on-line identification, the GDTPC algorithm was applied in food extrusion control. Both simulation and application results are presented to demonstrate the usefulness of the algorithm.

A High Performance Control System for Pressurized Headbox of Paper Machines

The pressurized headbox is one of the most important pieces of equipment on a modern paper machine. It is a special two-inputs-twooutputs process, and proper matching of input and output of control loops is very important. Relative gain analysis and computer simulation based on a mathematics model of the pressurized headbox shows that the common used configuration in paper industry is not the best. For this kind of configuration, a multi variable predictive control algorithm is designed. Computer simulation shows that this kind of control algorithm can deal with the headbox control very well. A control system with this kind of controller for full scale pressurized headbox has a good performance with excellent control precision, very short response time and very small offsets.

A Multivariable Dynamic Performance Predictive Control with Application to Power Generation Plants

The paper presents a multivariable predictive control algorithm. The algorithm is based on a Generalized Predictive Control type performance index. For such an index a dynamic infinite horizon optimisation problem is solved. This new controller (DPC) provides a minimum achievable value of the GPC type performance index in the steady state. The performance of the controller is tested on an example of a power plant gas turbine.

A pilot-scale distillation facility for digital computer control research

A new pilot-scale multicomponent distillation facility and associated computer control system are described. The control system design utilizes a hierarchical structure, with a microcomputer for direct digital control of secondary process variables and a general-purpose, real-time computer for high-level control of the primary variables. The facility is used for advanced process control research and real-time system instruction. Some typical results incorporating a new multivariable predictive control algorithm are presented.

A Pilot-Scale Distillation Facility for Digital Computer Control Research

A new pilot-scale multicomponent distillation facility and associated computer control system are described. The control system design utili s a hierarchical structure, with a microcomputer for direct digital control of secondary process variables and a general- purpose, real-time computer for high-level control of the primary variables. The facility is used for advanced process control research and real-time system instruction. Some typical results incorporating a new multivariable predictive control algorithm are presented.