Linear matrix inequality-based robust model predictive control for time-delayed systems

This work addresses the solution to the problem of robust model predictive control (MPC) of systems with model uncertainty. The case of zone control of multi-variable stable systems with multiple time delays is considered. The usual approach of dealing with this kind of problem is through the inclusion of non-linear cost constraint in the control problem. The control action is then obtained at each sampling time as the solution to a non-linear programming (NLP) problem that for high-order systems can be computationally expensive. Here, the robust MPC problem is formulated as a linear matrix inequality problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of a reactor system of the process industry.

Closed-loop stable model predictive control of integrating systems with dead time

Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system. (C) 2012 Elsevier Ltd. All rights reserved.

Optimization of adaptive traffic signal control with transit signal priority at isolated intersections using parallel genetic algorithms

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This dissertation presents a new method that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of transit signal priority (TSP). The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. Unlike the simple genetic algorithm (GA), PGA can provide better and faster solutions needed for real-time optimization of adaptive traffic signal control. ^ An important component in the proposed method involves the development of a microscopic delay estimation model that was designed specifically to optimize adaptive traffic signal with TSP. Macroscopic delay models such as the Highway Capacity Manual (HCM) delay model are unable to accurately consider the effect of phase combination and phase sequence in delay calculations. In addition, because the number of phases and the phase sequence of adaptive traffic signal may vary from cycle to cycle, the phase splits cannot be optimized when the phase sequence is also a decision variable. A "flex-phase" concept was introduced in the proposed microscopic delay estimation model to overcome these limitations. ^ The performance of PGA was first evaluated against the simple GA. The results show that PGA achieved both faster convergence and lower delay for both under- or over-saturated traffic conditions. A VISSIM simulation testbed was then developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer was able to produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles. The VISSIM testbed developed in this research provides a powerful tool to design and evaluate different TSP strategies under both actuated and adaptive signal control. ^

Circadian Phase Control - Genetic Algorithm and Non-linear Control Approach

This paper deals with the phase control for Neurospora circadian rhythm. The nonlinear control, given by tuning the parameters (considered as controlled variables) in Neurospora dynamical model, allows the circadian rhythms tracking a reference one. When there are many parameters (e.g. 3 parameters in this paper) and their values are unknown, the adaptive control law reveals its weakness since the parameters converging and control objective must be guaranteed at the same time. We show that this problem can be solved using the genetic algorithm for parameters estimation. Once the unknown parameters are known, the phase control is performed by chaos synchronization technique.

Experimental investigation on adaptive robust controller designs applied to a free-floating space manipulator

This paper aims to formulate and investigate the application of various nonlinear H(infinity) control methods to a fiee-floating space manipulator subject to parametric uncertainties and external disturbances. From a tutorial perspective, a model-based approach and adaptive procedures based on linear parametrization, neural networks and fuzzy systems are covered by this work. A comparative study is conducted based on experimental implementations performed with an actual underactuated fixed-base planar manipulator which is, following the DEM concept, dynamically equivalent to a free-floating space manipulator. (C) 2011 Elsevier Ltd. All rights reserved.

Optimizing model predictive control of an industrial distillation column

The main scope of this work is the implementation of an MPC that integrates the control and the economic optimization of the system. The two problems are solved simultaneously through the modification of the control cost function that includes an additional term related to the economic objective. The optimizing MPC is based on a quadratic program (QP) as the conventional MPC and can be solved with the available QP solvers. The method was implemented in an industrial distillation system, and the results show that the approach is efficient and can be used, in several practical cases. (C) 2011 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.

Discrete-time mean variance optimal control of linear systems with Markovian jumps and multiplicative noise

In this article, we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noise under three kinds of performance criterions related to the final value of the expectation and variance of the output. In the first problem it is desired to minimise the final variance of the output subject to a restriction on its final expectation, in the second one it is desired to maximise the final expectation of the output subject to a restriction on its final variance, and in the third one it is considered a performance criterion composed by a linear combination of the final variance and expectation of the output of the system. We present explicit sufficient conditions for the existence of an optimal control strategy for these problems, generalising previous results in the literature. We conclude this article presenting a numerical example of an asset liabilities management model for pension funds with regime switching.

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.

A uniform white-noise model for fixed-point roundoff errors in digital systems

Fixed-point roundoff noise in digital implementation of linear systems arises due to overflow, quantization of coefficients and input signals, and arithmetical errors. In uniform white-noise models, the last two types of roundoff errors are regarded as uniformly distributed independent random vectors on cubes of suitable size. For input signal quantization errors, the heuristic model is justified by a quantization theorem, which cannot be directly applied to arithmetical errors due to the complicated input-dependence of errors. The complete uniform white-noise model is shown to be valid in the sense of weak convergence of probabilistic measures as the lattice step tends to zero if the matrices of realization of the system in the state space satisfy certain nonresonance conditions and the finite-dimensional distributions of the input signal are absolutely continuous.

The effect of fractional order in variable structure control

This paper studies fractional variable structure controllers. Two cases are considered namely, the sliding reference model and the control action, that are generalized from integer into fractional orders. The test bed consists in a mechanical manipulator and the effect of the fractional approach upon the system performance is evaluated. The results show that fractional dynamics, both in the switching surface and the control law are important design algorithms in variable structure controllers.

On the robustness of the slotine-Li and the FPT/SVD-based adaptive controllers

A comparative study concerning the robustness of a novel, Fixed Point Transformations/Singular Value Decomposition (FPT/SVD)-based adaptive controller and the Slotine-Li (S&L) approach is given by numerical simulations using a three degree of freedom paradigm of typical Classical Mechanical systems, the cart + double pendulum. The effects of the imprecision of the available dynamical model, presence of dynamic friction at the axles of the drives, and the existence of external disturbance forces unknown and not modeled by the controller are considered. While the Slotine-Li approach tries to identify the parameters of the formally precise, available analytical model of the controlled system with the implicit assumption that the generalized forces are precisely known, the novel one makes do with a very rough, affine form and a formally more precise approximate model of that system, and uses temporal observations of its desired vs. realized responses. Furthermore, it does not assume the lack of unknown perturbations caused either by internal friction and/or external disturbances. Its another advantage is that it needs the execution of the SVD as a relatively time-consuming operation on a grid of a rough system-model only one time, before the commencement of the control cycle within which it works only with simple computations. The simulation examples exemplify the superiority of the FPT/SVD-based control that otherwise has the deficiency that it can get out of the region of its convergence. Therefore its design and use needs preliminary simulation investigations. However, the simulations also exemplify that its convergence can be guaranteed for various practical purposes.

Service-oriented architecture for device lifecycle support in industrial automation

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores Especialidade: Robótica e Manufactura Integrada

New insights into dendritic cell biology and histiocytosis through a transgenic tumor model

SUMMARY The effective development of an immune response depends on the careful interplay and the regulation between innate and adaptive immunity. As the dendritic cells (DCs) are equipped with many receptors, such as Toll-like receptors, which can detect the presence of infection by recognizing different component of bacteria, fungi and even viruses, they are the among the first cells to respond to the infection. Upon pathogen challenge, the DCs interpret the innate system activation as a maturation signal, resulting in the migration of the DCS to a draining lymph node site. There, activated DCs present efficiently antigens to naïve T cells, which are in turn activated and initiate adaptive immunity. Therefore, DCs are the main connectors between innate and adaptive immune systems. In addition to be the most efficient antigen- presenting cells, DCs play a central role in the regulation of immune responses and immune tolerance. Despite extensive research, many aspects related to DC biology are still unsolved and/or controversial. The low frequency of DCs in vivo often hamper study of DC biology and in vitro-derived DCs are not suited to address certain questions, such as the development of DC. We sought of transforming in vivo the DCs through the specific expression of an oncogene, in order to obtain unlimited numbers of these cells. To achieve this goal, transgenic mouse lines expressing the SV40 Large T oncogene under the control of the CD1 1 c promoter were generated. These transgenic mice are healthy until the age of three to four months without alterations in the DC biology. Thereafter transgenic mice develop a fatal disease that shows features of a human pathology, named histiocytosis, involving DCs. We demonstrate that the disease development in the transgenic mice correlates with a massive accumulation of transformed DCs in the affected organs. Importantly, transformed DCs are immature and fully conserve their capacity to mature in antigen presenting cells. We observe hyperproliferation of transformed DCs only in the sick transgenic mice. Surprisingly, transformed DCs do not proliferate in vitro, but transfer of the transformed DCs into immunodeficient or tolerant host leads to tumor formation. Altoghether, the transgenic mouse lines we have generated represent a valuable tumor model for human histiocytosis, and provide excellent tools to study DC biology. RESUME Le développement d'une réponse immunitaire efficace dépend d'une minutieuse interaction et régulation entre l'immunité innée et adaptative. Comme les cellules dendritiques (DCs) sont équipées de nombreux récepteurs, tels que les récepteurs Toll-like, qui peuvent détecter la présence d'une infection en reconnaissant différents composants bactériens, issus de champignons ou même viraux, elles sont parmi les premières cellules à répondre à l'infection. Suite à la stimulation induite par le pathogène, les DCs interprètent l'activation du système immunitaire inné comme un signal de maturation, résultant dans la migration des DCs vers le ganglion drainant le site d'infection. Là, les DCs actives présentent efficacement des antigènes aux cellules T, qui sont à leur tour activées et initient les systèmes d'immunité adaptative. Ainsi, les DCs forment le lien principal entre les réponses immunitaires innées et adaptatives. En plus d'être les cellules présentatrices d'antigènes les plus efficaces, les DCs jouent un rôle central dans la régulation du système immunitaire et dans le phénomène de tolérance. Malgré des recherches intensives, de nombreux aspects liés à la biologie des DCs sont encore irrésolus et/ou controversés. La faible fréquence des DCs in vivo gêne souvent l'étude de la biologie de ces cellules et les DCs dérivées in vitro ne sont pas adéquates pour adresser certaines questions, telles que le développement des DCs. Afin d'obtenir des quantités illimitées de DCs, nous avons songé à transformer in vivo les DC grâce à l'expression spécifique d'un oncogène. Afin d'atteindre ce but, nous avons généré des lignées de souris transgéniques qui expriment l'oncogène SV40 Large T sous le contrôle du promoter CD1 le. Ces souris transgéniques sont saines jusqu'à l'âge de trois à quatre mois et ne présentent pas d'altération dans la biologie des DCs. Ensuite, les souris transgéniques développent une maladie présentant les traits caractéristiques d'une pathologie humaine nommée histiocytose, qui implique les DCs. Nous montrons que le développement de cette maladie corrèle avec une accumulation massive des DCs transformées dans les organes touchés. De plus, les DCs transformées sont immatures et conservent leur capacité à différencier en cellules présentatrices d'antigène. Nous observons une hyper-prolifération des DCs transformées seulement dans les souris transgéniques malades. Etonnament, les DC transformées ne prolifèrent pas in vitro, par contre, le transfert des DCs transformées dans des hôtes immuno-déficients ou tolérant conduit à la formation de tumeurs. Globalement, les lignées de souris transgéniques que nous avons générées représentent un modèle valide pour l'histiocytose humaine, et de plus, offrent d'excellents outils pour étudier la biologie des DCs.

Local model predictive control experiences with differential driven wheeled mobile robots

This work extends a previously developed research concerning about the use of local model predictive control in differential driven mobile robots. Hence, experimental results are presented as a way to improve the methodology by considering aspects as trajectory accuracy and time performance. In this sense, the cost function and the prediction horizon are important aspects to be considered. The aim of the present work is to test the control method by measuring trajectory tracking accuracy and time performance. Moreover, strategies for the integration with perception system and path planning are briefly introduced. In this sense, monocular image data can be used to plan safety trajectories by using goal attraction potential fields