Infinite horizon MPC with non-minimal state space feedback

In the MPC literature, stability is usually assured under the assumption that the state is measured. Since the closed-loop system may be nonlinear because of the constraints, it is not possible to apply the separation principle to prove global stability for the Output feedback case. It is well known that, a nonlinear closed-loop system with the state estimated via an exponentially converging observer combined with a state feedback controller can be unstable even when the controller is stable. One alternative to overcome the state estimation problem is to adopt a non-minimal state space model, in which the states are represented by measured past inputs and outputs [P.C. Young, M.A. Behzadi, C.L. Wang, A. Chotai, Direct digital and adaptative control by input-output, state variable feedback pole assignment, International journal of Control 46 (1987) 1867-1881; C. Wang, P.C. Young, Direct digital control by input-output, state variable feedback: theoretical background, International journal of Control 47 (1988) 97-109]. In this case, no observer is needed since the state variables can be directly measured. However, an important disadvantage of this approach is that the realigned model is not of minimal order, which makes the infinite horizon approach to obtain nominal stability difficult to apply. Here, we propose a method to properly formulate an infinite horizon MPC based on the output-realigned model, which avoids the use of an observer and guarantees the closed loop stability. The simulation results show that, besides providing closed-loop stability for systems with integrating and stable modes, the proposed controller may have a better performance than those MPC controllers that make use of an observer to estimate the current states. (C) 2008 Elsevier Ltd. All rights reserved.

Robust model predictive control with zone control

Model predictive control (MPC) is usually implemented as a control strategy where the system outputs are controlled within specified zones, instead of fixed set points. One strategy to implement the zone control is by means of the selection of different weights for the output error in the control cost function. A disadvantage of this approach is that closed-loop stability cannot be guaranteed, as a different linear controller may be activated at each time step. A way to implement a stable zone control is by means of the use of an infinite horizon cost in which the set point is an additional variable of the control problem. In this case, the set point is restricted to remain inside the output zone and an appropriate output slack variable is included in the optimisation problem to assure the recursive feasibility of the control optimisation problem. Following this approach, a robust MPC is developed for the case of multi-model uncertainty of open-loop stable systems. The controller is devoted to maintain the outputs within their corresponding feasible zone, while reaching the desired optimal input target. Simulation of a process of the oil re. ning industry illustrates the performance of the proposed strategy.

A stable MPC with zone control

Several MPC applications implement a control strategy in which some of the system outputs are controlled within specified ranges or zones, rather than at fixed set points [J.M. Maciejowski, Predictive Control with Constraints, Prentice Hall, New Jersey, 2002]. This means that these outputs will be treated as controlled variables only when the predicted future values lie outside the boundary of their corresponding zones. The zone control is usually implemented by selecting an appropriate weighting matrix for the output error in the control cost function. When an output prediction is inside its zone, the corresponding weight is zeroed, so that the controller ignores this output. When the output prediction lies outside the zone, the error weight is made equal to a specified value and the distance between the output prediction and the boundary of the zone is minimized. The main problem of this approach, as long as stability of the closed loop is concerned, is that each time an output is switched from the status of non-controlled to the status of controlled, or vice versa, a different linear controller is activated. Thus, throughout the continuous operation of the process, the control system keeps switching from one controller to another. Even if a stabilizing control law is developed for each of the control configurations, switching among stable controllers not necessarily produces a stable closed loop system. Here, a stable M PC is developed for the zone control of open-loop stable systems. Focusing on the practical application of the proposed controller, it is assumed that in the control structure of the process system there is an upper optimization layer that defines optimal targets to the system inputs. The performance of the proposed strategy is illustrated by simulation of a subsystem of an industrial FCC system. (C) 2008 Elsevier Ltd. All rights reserved.

Valve friction and nonlinear process model closed-loop identification

Among several process variability sources, valve friction and inadequate controller tuning are supposed to be two of the most prevalent. Friction quantification methods can be applied to the development of model-based compensators or to diagnose valves that need repair, whereas accurate process models can be used in controller retuning. This paper extends existing methods that jointly estimate the friction and process parameters, so that a nonlinear structure is adopted to represent the process model. The developed estimation algorithm is tested with three different data sources: a simulated first order plus dead time process, a hybrid setup (composed of a real valve and a simulated pH neutralization process) and from three industrial datasets corresponding to real control loops. The results demonstrate that the friction is accurately quantified, as well as ""good"" process models are estimated in several situations. Furthermore, when a nonlinear process model is considered, the proposed extension presents significant advantages: (i) greater accuracy for friction quantification and (ii) reasonable estimates of the nonlinear steady-state characteristics of the process. (C) 2010 Elsevier Ltd. All rights reserved.

A Separation Principle for the Continuous-Time LQ-Problem With Markovian Jump Parameters

In this paper, we devise a separation principle for the finite horizon quadratic optimal control problem of continuous-time Markovian jump linear systems driven by a Wiener process and with partial observations. We assume that the output variable and the jump parameters are available to the controller. It is desired to design a dynamic Markovian jump controller such that the closed loop system minimizes the quadratic functional cost of the system over a finite horizon period of time. As in the case with no jumps, we show that an optimal controller can be obtained from two coupled Riccati differential equations, one associated to the optimal control problem when the state variable is available, and the other one associated to the optimal filtering problem. This is a separation principle for the finite horizon quadratic optimal control problem for continuous-time Markovian jump linear systems. For the case in which the matrices are all time-invariant we analyze the asymptotic behavior of the solution of the derived interconnected Riccati differential equations to the solution of the associated set of coupled algebraic Riccati equations as well as the mean square stabilizing property of this limiting solution. When there is only one mode of operation our results coincide with the traditional ones for the LQG control of continuous-time linear systems.

Hardware/firmware implementation of a soft sensor using an improved version of a fuzzy identification algorithm

This paper presents the design and implementation of an embedded soft sensor, i. e., a generic and autonomous hardware module, which can be applied to many complex plants, wherein a certain variable cannot be directly measured. It is implemented based on a fuzzy identification algorithm called ""Limited Rules"", employed to model continuous nonlinear processes. The fuzzy model has a Takagi-Sugeno-Kang structure and the premise parameters are defined based on the Fuzzy C-Means (FCM) clustering algorithm. The firmware contains the soft sensor and it runs online, estimating the target variable from other available variables. Tests have been performed using a simulated pH neutralization plant. The results of the embedded soft sensor have been considered satisfactory. A complete embedded inferential control system is also presented, including a soft sensor and a PID controller. (c) 2007, ISA. Published by Elsevier Ltd. All rights reserved.

A new technique to control brushless motor for blood pump application

This article presents a back-electromotive force (BEMF)-based technique of detection for sensorless brushless direct current motor (BLDCM) drivers. The BLDCM has been chosen as the energy converter in rotary or pulsatile blood pumps that use electrical motors for pumping. However, in order to operate properly, the BLDCM driver needs to know the shaft position. Usually, that information is obtained through a set of Hall sensors assembled close to the rotor and connected to the electronic controller by wires. Sometimes, a large distance between the motor and controller makes the system susceptible to interference on the sensor signal because of winding current switching. Thus, the goal of the sensorless technique presented in this study is to avoid this problem. First, the operation of BLDCM was evaluated on the electronic simulator PSpice. Then, a BEMF detector circuitry was assembled in our laboratories. For the tests, a sensor-dependent system was assembled where the direct comparison between the Hall sensors signals and the detected signals was performed. The obtained results showed that the output sensorless detector signals are very similar to the Hall signals at speeds of more than 2500 rpm. Therefore, the sensorless technique is recommended as a responsible or redundant system to be used in rotary blood pumps.

A calibration approach based on Takagi-Sugeno fuzzy inference system for digital electronic compasses

Recently, the development of industrial processes brought on the outbreak of technologically complex systems. This development generated the necessity of research relative to the mathematical techniques that have the capacity to deal with project complexities and validation. Fuzzy models have been receiving particular attention in the area of nonlinear systems identification and analysis due to it is capacity to approximate nonlinear behavior and deal with uncertainty. A fuzzy rule-based model suitable for the approximation of many systems and functions is the Takagi-Sugeno (TS) fuzzy model. IS fuzzy models are nonlinear systems described by a set of if then rules which gives local linear representations of an underlying system. Such models can approximate a wide class of nonlinear systems. In this paper a performance analysis of a system based on IS fuzzy inference system for the calibration of electronic compass devices is considered. The contribution of the evaluated IS fuzzy inference system is to reduce the error obtained in data acquisition from a digital electronic compass. For the reliable operation of the TS fuzzy inference system, adequate error measurements must be taken. The error noise must be filtered before the application of the IS fuzzy inference system. The proposed method demonstrated an effectiveness of 57% at reducing the total error based on considered tests. (C) 2011 Elsevier Ltd. All rights reserved.

Dynamic positioning systems: An experimental analysis of sliding mode control

Vessel dynamic positioning (DP) systems are based on conventional PID-type controllers and an extended Kalman filter. However, they present a difficult tuning procedure, and the closed-loop performance varies with environmental or loading conditions since the dynamics of the vessel are eminently nonlinear. Gain scheduling is normally used to address the nonlinearity of the system. To overcome these problems, a sliding mode control was evaluated. This controller is robust to variations in environmental and loading conditions, it maintains performance and stability for a large range of conditions, and presents an easy tuning methodology. The performance of the controller was evaluated numerically and experimentally in order to address its effectiveness. The results are compared with those obtained from conventional PID controller. (c) 2010 Elsevier Ltd. All rights reserved.

Closed-loop model re-identification of processes under MPC with zone control

This work deals with a procedure for model re-identification of a process in closed loop with ail already existing commercial MPC. The controller considered here has a two-layer structure where the upper layer performs a target calculation based on a simplified steady-state optimization of the process. Here, it is proposed a methodology where a test signal is introduced in a tuning parameter of the target calculation layer. When the outputs are controlled by zones instead of at fixed set points, the approach allows the continuous operation of the process without an excessive disruption of the operating objectives as process constraints and product specifications remain satisfied during the identification test. The application of the method is illustrated through the simulation of two processes of the oil refining industry. (c) 2008 Elsevier Ltd. All rights reserved.

Atmospheric versus vegetation controls of Amazonian tropical rain forest evapotranspiration: Are the wet and seasonally dry rain forests any different?

This study analyzes evapotranspiration data for three wet and two seasonally dry rain forest sites in Amazonia. The main environmental (net radiation, vapor pressure deficit, and aerodynamic conductance) and vegetation (surface conductance) controls of evapotranspiration are also assessed. Our research supports earlier studies that demonstrate that evapotranspiration in the dry season is higher than that in the wet season and that surface net radiation is the main controller of evapotranspiration in wet equatorial sites. However, our analyses also indicate that there are different factors controlling the seasonality of evapotranspiration in wet equatorial rain forest sites and southern seasonally dry rain forests. While the seasonality of evapotranspiration in wet equatorial forests is driven solely by environmental factors, in seasonally dry forests, it is also biotically controlled with the surface conductance varying between seasons by a factor of approximately 2. The identification of these different drivers of evapotranspiration is a major step forward in our understanding of the water dynamics of tropical forests and has significant implications for the future development of vegetation-atmosphere models and land use and conservation planning in the region.

Strain variation in ppGpp concentration and RpoS levels in laboratory strains of Escherichia coli K-12

Laboratory strains and natural isolates of Escherichia coli differ in their level of stress resistance due to strain variation in the level of the sigma factor sigma(S) (or RpoS), the transcriptional master controller of the general stress response. We found that the high level of RpoS in one laboratory strain (MC4100) was partially dependent on an elevated basal level of ppGpp, an alarmone responding to stress and starvation. The elevated ppGpp was caused by two mutations in spoT, a gene associated with ppGpp synthesis and degradation. The nature of the spoT allele influenced the level of ppGpp in both MC4100 and another commonly used K-12 strain, MG1655. Introduction of the spoT mutation into MG1655 also resulted in an increased level of RpoS, but the amount of RpoS was lower in MG1655 than in MC4100 with either the wild-type or mutant spoT allele. In both MC4100 and MG1655, high ppGpp concentration increased RpoS levels, which in turn reduced growth with poor carbon sources like acetate. The growth inhibition resulting from elevated ppGpp was relieved by rpoS mutations. The extent of the growth inhibition by ppGpp, as well as the magnitude of the relief by rpoS mutations, differed between MG1655 and MC4100. These results together suggest that spoT mutations represent one of several polymorphisms influencing the strain variation of RpoS levels. Stress resistance was higher in strains with the spoT mutation, which is consistent with the conclusion that microevolution affecting either or both ppGpp and RpoS can reset the balance between self-protection and nutritional capability, the SPANC balance, in individual strains of E coli.

A Parallel Hardware Architecture for Scale and Rotation Invariant Feature Detection

This paper proposes a parallel hardware architecture for image feature detection based on the Scale Invariant Feature Transform algorithm and applied to the Simultaneous Localization And Mapping problem. The work also proposes specific hardware optimizations considered fundamental to embed such a robotic control system on-a-chip. The proposed architecture is completely stand-alone; it reads the input data directly from a CMOS image sensor and provides the results via a field-programmable gate array coupled to an embedded processor. The results may either be used directly in an on-chip application or accessed through an Ethernet connection. The system is able to detect features up to 30 frames per second (320 x 240 pixels) and has accuracy similar to a PC-based implementation. The achieved system performance is at least one order of magnitude better than a PC-based solution, a result achieved by investigating the impact of several hardware-orientated optimizations oil performance, area and accuracy.

EXPLOITING A GENERIC APPROACH TO CONSTRUCT COMPONENT-BASED SYSTEMS SOFTWARE IN LINUX ENVIRONMENTS

Component-based software engineering has recently emerged as a promising solution to the development of system-level software. Unfortunately, current approaches are limited to specific platforms and domains. This lack of generality is particularly problematic as it prevents knowledge sharing and generally drives development costs up. In the past, we have developed a generic approach to component-based software engineering for system-level software called OpenCom. In this paper, we present OpenComL an instantiation of OpenCom to Linux environments and show how it can be profiled to meet a range of system-level software in Linux environments. For this, we demonstrate its application to constructing a programmable router platform and a middleware for parallel environments.

TOWARD SEMANTIC WEB SERVICES AS MVC APPLICATIONS: FROM OWL-S VIA UML

OWL-S is an application of OWL, the Web Ontology Language, that describes the semantics of Web Services so that their discovery, selection, invocation and composition can be automated. The research literature reports the use of UML diagrams for the automatic generation of Semantic Web Service descriptions in OWL-S. This paper demonstrates a higher level of automation by generating complete complete Web applications from OWL-S descriptions that have themselves been generated from UML. Previously, we proposed an approach for processing OWL-S descriptions in order to produce MVC-based skeletons for Web applications. The OWL-S ontology undergoes a series of transformations in order to generate a Model-View-Controller application implemented by a combination of Java Beans, JSP, and Servlets code, respectively. In this paper, we show in detail the documents produced at each processing step. We highlight the connections between OWL-S specifications and executable code in the various Java dialects and show the Web interfaces that result from this process.