Multivariable Model-based Control Research Articles

Design and implementation of multi-variable [formula omitted] robust control for the underground coal gasification project Thar

The energy per unit time is an important performance indicator in determining the performance of an underground coal gasification (UCG) site to produce electricity. In literature, model-based strategies are employed by considering UCG as a single input single output (SISO) system, in which only the heating value of syngas is maintained at the desired level by varying inlet gas flow rate. However, the energy per unit time is also dependent on the flow rate of the produced gas mixture. Therefore, in this work, a model-based multi-variable robust control design, based on H∞ technique is proposed for the UCG process. The actual nonlinear model of UCG is very complex due to its 3D axisymmetric geometry, which makes the model-based control design a formidable task. Thus, a simple linear model with two inputs (flow rate and composition of inlet gas) and two outputs (flow rate and heating value of syngas) is identified by using subspace-based (N4SID) system identification technique. The linear model is then employed to design the H∞ (S/KS mixed sensitivity) multi-variable robust controller. The simulation results show that the designed controller has achieved both robust stability and performance in the presence of modeling inaccuracies and external disturbance. Furthermore, the designed controller is also implemented on the actual nonlinear cavity simulator (CAVSIM) for the UCG process. The controller exhibits an adequate performance by tracking the desired set points for the heating value and flow rate of the syngas.

Nonlinear Model-Based Multivariable Control for Air & Charging System of Diesel Engine with Short and Long Route EGR Valves

The objective of this study is to investigate a nonlinear model-based multivariable (MIMO, Multi Input Multi Output) technique to decouple actuators interaction and to reduce the calibration effort, while increasing control performances, above all in transient conditions, and robustness with respect to model uncertainties and system parameter variations. The presented control technique is based on the development of a nonlinear dynamical physical model of the diesel air and charging system. Feedback Linearization control is then applied to decouple actuators’ interactions and compensate for nonlinearities. A new set of virtual inputs are defined inverting the system differential equations. Relation among the new virtual inputs and the outputs is purely linear and decoupled, meaning that each virtual input affects linearly only one output. Moreover, a linear control block is added to guarantee transient and steady state performances and closed loop robustness. The proposed control approach has been validated through small diesel engine dyno and vehicle activities. Transient test bench maneuvers show that the control is able to coordinate the actuators in order to fulfill the targets and to guarantee similar performances in different operating points. In addition the robustness to environmental changes has been demonstrated by vehicle tests at different ambient conditions.

Detection and diagnosis of model-plant mismatch in multivariable model-based control schemes

The extent of approximation in modelling a given process, characterized by the model-plant mismatch (MPM), amongst other factors, critically determines the performance of a model-based control scheme. It is necessary therefore to carry out model maintenance and correction on a regular basis. However, a complete re-identification is usually a costly exercise. Therefore, it is highly desirable to precisely determine the specific elements that are in mismatch and re-identify only those parameters. In the recent times, the plant-model ratio (PMR) was proposed as an effective metric for diagnosing MPM in single-input single-output (SISO) systems from closed loop data. The PMR facilitates unique detection of mismatch in gain, dynamics and delay. A straightforward application of PMR to multivariable closed-loop systems is challenging primarily due to the confounding effects of other inputs and loop-to-loop interactions under closed-loop conditions. Furthermore, the metric requires high-frequency excitation for identification of delay mismatch. In this work, we first present a method to overcome the latter requirement using Hilbert transform relation and partial cross-spectral densities. Subsequently, we present the key contribution of this work, that of generalizing the PMR approach to multivariable control systems. Two threshold-based hypothesis tests are presented for diagnosing mismatch in gain and dynamics. Three simulation case studies are presented to demonstrate the efficacy of the proposed method.

Model-based Fuzzy Control of Air-Conditioning Systems using Air Bypass for Optimized Dehumidification

This paper presents a model-based multi-variable controller for large-scale air-conditioning systems. First, a generic model of a cross-flow heat exchanger is proposed. Thereafter a control-oriented model in Takagi-Sugeno form is derived based on well-reasoned simplifications. The Takagi-Sugeno structure allows for the design of a parallel-distributed output feedback controller for the entire operating range of the air-conditioning system with respect to the outlet air temperature and humidity. An illustrative example with common requirements and ambient conditions demonstrates the effectiveness of the proposed controller scheme.

Feedback control design for non-inductively sustained scenarios in NSTX-U using TRANSP

This paper examines a method for real-time control of non-inductively sustained scenarios in NSTX-U by using TRANSP, a time-dependent integrated modeling code for prediction and interpretive analysis of tokamak experimental data, as a simulator. The actuators considered for control in this work are the six neutral beam sources and the plasma boundary shape. To understand the response of the plasma current, stored energy, and central safety factor to these actuators and to enable systematic design of control algorithms, simulations were run in which the actuators were modulated and a linearized dynamic response model was generated. A multi-variable model-based control scheme that accounts for the coupling and slow dynamics of the system while mitigating the effect of actuator limitations was designed and simulated. Simulations show that modest changes in the outer gap and heating power can improve the response time of the system, reject perturbations, and track target values of the controlled values.

Characterisation, control, and energy management of electrified turbocharged diesel engines

The electrification of engine components offers significant opportunities for fuel efficiency improvements. The electrified turbocharger is one of the most attractive options since it recovers part of the engine exhaust gas mechanical energy to assist boosting. Therefore, the engine can be downsized through improved transient responsiveness. In the electrified turbocharger, an electric machine is mounted on the turbine shaft and changes the air system dynamics, so characterisation of the new layout is essential. A systematic control solution is required to manage energy flows in the hybrid system. In this paper, a framework for characterisation, control, and energy management for an electrified turbocharged diesel engine is proposed. The impacts of the electric machine on fuel economy and air system variables are analysed. Based on the characterisation, a two-level control structure is proposed. A real-time energy management strategy is employed as the supervisory level controller to generate the optimal values of critical variables, while a model-based multi-variable controller is designed as the low level controller to track the values. The two controllers work together in a cascade to address both fuel economy optimisation and battery state-of-charge maintenance. The proposed control strategy is validated on a high fidelity physical engine model. The tracking performance shows the proposed framework is a promising solution in regulating the behavior of electrified engines.

Energy-efficient fuzzy model-based multivariable predictive control of a HVAC system

In this paper the novel approach of a fuzzy model-based multivariable predictive functional control (FMBMPC) of a heating ventilating and air conditioning (HVAC) system is presented, which is implemented on a real-world test plant. The control law is derived in the state-space domain and is given in an analytical form without an optimization algorithm. The basic principles of the predictive control were extended in a fuzzy multivariable manner and the suggested tuning rules for the proposed control algorithm were depicted, which normally gives satisfactory results. The proposed approach introduces a compact and relatively simple design in the case of higher-order and nonminimal phase plants, but it is limited to open-loop stable plants. For the comparison a classical optimal proportional–integral (PI) controller was also designed and applied. The results show that the FMBMPC approach performs better due to the HVACs’ nonlinear dynamics. In case of interactions influence rejection by the HVAC system, the FMBMPC algorithm outperforms the classical PI approach. The results also show that the proposed approach exhibits better reference-model tracking across a wider operating range. The energy consumption comparison shows that the FMBMPC approach is also more energy-efficient. A shortened literature review of applications of energy-efficient and MPC control for HVAC systems is also presented. FMBMPC control is interesting in the case of batch reactors, furnaces, pressure vessels, HVAC systems and any processes that have strong nonlinear dynamics, multivariable natures and long transport delays.

A cascaded control structure for air-path control of diesel engines

In order to comply with increasingly stringent diesel-engine emission legislation, fast and precise control of the turbochargers and exhaust-gas recirculation are necessary. The difficulties lie in certain disadvantageous plant properties such as cross-couplings and non-linearities, the disturbance of the air path by fast operating-point changes and the multitude of air-path configurations that are available. In this paper, a novel model-based approach for air-path control based on cascaded control is proposed. The resulting multi-variable controller has a low sensitivity to the non-linearity of the plant and the disturbances caused by sudden changes in the operating point. Furthermore, the controller is applicable to various types of air-path configuration. This flexibility is demonstrated by a system analysis of the models of two engines which span a broad range of air-path configurations. The performance of the proposed controller is compared experimentally with that of a conventional model-based multi-variable controller, by implementing both on an engine test bench. This comparison confirms that the cascaded controller presented herein can handle the cross-couplings of the system and that it exhibits a lower sensitivity to the non-linearity and the disturbances than the conventional controller does.

Identification test design for multivariable model-based control: An industrial perspective

The design of plant tests to generate data for identification of dynamic models is critically important for development of model-based process control systems. Multivariable process identification tests in industry continue to rely on uncorrelated input signals, even though investigations have shown the benefits of other input designs which lead to correlated, higher-amplitude input signals. This is partly due to difficulties in formulating and solving computationally tractable problems for identification test design. In this work, related results are summarized and extended. Connections between different designs that target D-optimality or integral controllability are established. Related concepts are illustrated through simulation case studies.

Assessment of the controlling envelope of a model-based multivariable controller for vapor compression refrigeration systems

Abstract This paper explores the controlling characteristics of a first-principles model-based controller specially developed for vapor compression refrigeration systems. Mathematical sub-models were put forward for each of the system components: heat exchangers (condenser and evaporator), variable-speed compressor and variable-orifice electric expansion device. The dynamic simulation model was then used to design a multivariable controller based on the linear-quadratic-Gaussian technique using a Kalman filter for the estimator design. A purpose-built testing apparatus comprised of a variable-speed compressor and a pulse-width modulated expansion valve was used to collect data for the system identification, and model and controller validation exercises. It was found that the model reproduces the experimental trends of the working pressures and power consumption in conditions far from the nominal point of operation (±30%) with a maximum deviation of ±5%. Additional experiments were also performed to verify the ability of the controller of tracking reference changes and rejecting thermal load disturbances. It was found that the controller is able to keep the refrigeration system running properly when the thermal load was changed from 340 to 580 W (460 W nominal), and the evaporator superheating degree was varied from 9.5 °C to 22 °C (16.6 °C nominal).

Model-Based Multivariable Control of a Secondary Air System Using Controlled Finite Markov Chains

Controlled finite Markov chains are used for solving a multivariable control problem. The controlled system was the secondary air flow system in a fluidized-bed combustion power plant. A four-input four-output system control problem was formulated as a CFMC, and solved using dynamic programming. A non-linear system model, developed in earlier studies, was used in this model-based design. Three filters were applied to smoothen the (finite) control actions, for feasible plant control. These were based on averaging over finite state-space, references, and averaging over time. Simulations were conducted to illustrate the approach, and compare it with respect to SISO PID design. The simulations indicated that system control can be succesfully designed based on CFMC techniques, even for a 4×4 MIMO system.

Control of the fluidised bed in the pellet softening process

Multiple fluidised bed reactors for water softening (crystallisation of calcium carbonate) have been in operation at drinking water treatment plants since the late 1980s. Research on the operation of these pellet reactors has been focussed on investigating crystallisation under constant fluidised bed composition. However, in practice the bed composition varies frequently, despite large effort of plant operators. An improvement in the control of the fluidised bed can be achieved by using model-based multivariable control. Due to the nonlinear behaviour of the reactor, caused by water temperature variation during the year, a nonlinear control approach is used. A particle filter, based on a first-principles model, estimates the state of the softening reactor and a nonlinear model-predictive controller determines the values of the manipulated variables. We show in a simulation experiment, that it is possible to keep the reactor at desired operational parameters (pellet size and bed height) under varying operational conditions. In this way, the cost of pellet softening can be reduced and irregularities prevented.

The role of process system engineering (PSE) in integrated circuit (IC) manufacturing

The manufacture of integrated circuits is driven by a demand for faster calculation capabilities and lower costs, which will require the development of a new generation of manufacturing tools to increase yield productivity, spearheaded by improved measurement devices and advanced process control. The objectives of this paper are to review of the challenges in applying two areas of expertise in process systems engineering (PSE), namely process monitoring and control, and to motivate more academics working in PSE to get actively involved. PSE solutions appropriate for these challenges involve harnessing multivariate statistics, automated modeling approaches like genetic programming, and multivariable model-based control. The paper is illustrated with several example applications, all tested in fabrication facilities in Israel.

The multivariable model-based control of the non-alumina electrolyte variables in aluminum smelting cells

This paper investigates the application of multivariable model-based control to improve the regulatory control of electrolyte temperature, aluminum fluoride concentration, liquidus temperature, superheat, and electrolyte height. Also examined are therappropriateness of different control structures and the possible inclusion of recently developed sensors for alumina concentration and individual cell duct flowrate, temperature, and heat loss. For the smelter in this study, the maximum improvement possible with a multivariable model-based controller is predicted to be 30–40% reduction in standard deviation in electrolyte temperature, aluminum fluoride concentration, liquidus temperature, and superheat, and around half this for electrolyte height. Three control structures were found to be appropriate; all are different than the existing control structure, which was found to be suboptimal. Linear Quadratic Gaussian controllers were designed for each control structure and their predicted performance compared.

MORE PROCESS SYSTEM ENGINEERING (PSE) APPLICATIONS IN IC MANUFACTURING

The manufacture of integrated circuits is driven by a demand for faster calculation capabilities and lower costs, which will require the development of a new generation of manufacturing tools to increase yield productivity, spearheaded by improved measurement devices and advanced process control. The objectives of this paper are review of the challenges in two main PSE areas: process monitoring and process control. PSE solutions appropriate for these challenges involve harnessing multivariate statistics, automated modeling approaches like genetic programming, and multivariable model-based control. The paper is illustrated with several example applications, all tested in fabrication facilities in Israel.

Methods for performance monitoring and diagnosis of multivariable model-based control systems

Methods for performance monitoring and diagnosis of multivariable closed loop systems have been proposed aiming at application to model predictive control systems for industrial processes. For performance monitoring, the well-established traditional statistical process control method is empolyed. To meet the underlying premise that the observed variable is univariate and statistically independent, a temporal and spatial decorrelation procedure for process variables has been suggested. For diagnosis of control performance deterioration, a method to estimate the model-error and disturbance signal has been devised. This method enables us to identify the cause of performance deterioration among the controller, process, and disturbance. The proposed methods were evaluated through numerical examples.

Implementation of model-based multivariable control on DIII–D

A model-based multivariable controller for plasma shape control has been successfully tested on the DIII–D tokamak. Good steady-state control of plasma boundary shape and X-point position was demonstrated in lower single-null ohmic plasmas. Quality of control for rapid plasma shape variation was mixed, but was robustly stable for all degrees of freedom explored. The control design was based on a linear plasma response model derived from fundamental physics assumptions, which was extensively validated against DIII–D experimental data. A linear controller produced with robust control design methods was tested and improved using results of closed loop simulations prior to experimental tests. A modification of the linear controller which addressed one of several practical DIII–D nonlinear constraints was tested during the experimental discharges.

Multivariable control of nutrient-removing activated sludge systems

The aim of this paper is to evaluate several multivariable model-based control algorithms for controlling nitrogen removal in activated sludge processes. We contend that the control of nutrient removal in activated sludge systems is a multivariable control problem, rather than a multiloop problem. The work is a simulation study of a predenitrification activated sludge plant. Several model-based controllers are evaluated and compared against a base-case and multiloop controller. We introduce a quantitative measure to aid in controller evaluation. Model based control algorithms are shown to be able to provide tight control of N removal, thereby offering significant benefits in terms of deferred capital expenditure. This is in return for increased operating costs.

Nonlinear model based multivariable control of a debutanizer

The debutanizer column control is a major economic incentive to a refinery owing to the importance of the product quality. The non-linear model-based multivariable control on a debutanizer has been shown to provide improved control performance of the top product and the bottom stream qualities using a steady-state process model with approximate dynamics. The control performance is much better than that achieved by the traditional PID-type control system. This chapter outlines the non-linear control method applied to an industrial debutanizer and discusses the use of inferential models to predict the iso-pentane contents in the overhead stream and the Reid vapour pressure in the bottom stream, in non-linear control applications.

Diagnostic Tools for Multivariable Model-Based Control Systems

In this paper we propose a set of diagnostic tools that can be used for detecting and estimating the causes for performance degradation in multivariable model-based control systems. Two simple diagnostic tests are proposed, on the basis of prediction error analysis, to detect the performance degradation and bifurcate the potential causes for the degradation. These tests are useful for narrowing down the list of potential causes for performance degradation. For detailed fault estimation, we propose to use a stochastic model based on Gaussian-sum statistics. The optimal estimation for this model requires a bank of a growing number of filters and is computationally intractable. To propose a practical solution, we combine the different suboptimal approaches in the literature. The results of the diagnosis can be used for alerting the operators about the potential faults and for adapting the controllers.