Fuzzy computational control for real Chua circuit

We preserit a computational procedure to control art experimental chaotic system by applying the occasional proportional feedback (OPF) method. The method implementation uses the fuzzy theory to relate the variable correction to the necessary adjustment in the control parameter. As an application We control the chaotic attractors of the Chua circuit. We present file developed circuits and algorithms to implement this control in real time. To simplify the used procedure, we use it low resolution analog to digital converter compensated for a lowpass filter that facilitates similar applications to control other systems. (C) 2007 Elsevier Ltd. All rights reserved.

A simple and effective inverse projection scheme for void distribution control in topology optimization

The ability to control both the minimum size of holes and the minimum size of structural members are essential requirements in the topology optimization design process for manufacturing. This paper addresses both requirements by means of a unified approach involving mesh-independent projection techniques. An inverse projection is developed to control the minimum hole size while a standard direct projection scheme is used to control the minimum length of structural members. In addition, a heuristic scheme combining both contrasting requirements simultaneously is discussed. Two topology optimization implementations are contributed: one in which the projection (either inverse or direct) is used at each iteration; and the other in which a two-phase scheme is explored. In the first phase, the compliance minimization is carried out without any projection until convergence. In the second phase, the chosen projection scheme is applied iteratively until a solution is obtained while satisfying either the minimum member size or minimum hole size. Examples demonstrate the various features of the projection-based techniques presented.

Robust integration of real time optimization with linear model predictive control

Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.

Real time optimization (RTO) with model predictive control (MPC)

This paper studies a simplified methodology to integrate the real time optimization (RTO) of a continuous system into the model predictive controller in the one layer strategy. The gradient of the economic objective function is included in the cost function of the controller. Optimal conditions of the process at steady state are searched through the use of a rigorous non-linear process model, while the trajectory to be followed is predicted with the use of a linear dynamic model, obtained through a plant step test. The main advantage of the proposed strategy is that the resulting control/optimization problem can still be solved with a quadratic programming routine at each sampling step. Simulation results show that the approach proposed may be comparable to the strategy that solves the full economic optimization problem inside the MPC controller where the resulting control problem becomes a non-linear programming problem with a much higher computer load. (C) 2010 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.

THE VANISHING DISCOUNT APPROACH FOR THE AVERAGE CONTINUOUS CONTROL OF PIECEWISE DETERMINISTIC MARKOV PROCESSES

This work is concerned with the existence of an optimal control strategy for the long-run average continuous control problem of piecewise-deterministic Markov processes (PDMPs). In Costa and Dufour (2008), sufficient conditions were derived to ensure the existence of an optimal control by using the vanishing discount approach. These conditions were mainly expressed in terms of the relative difference of the alpha-discount value functions. The main goal of this paper is to derive tractable conditions directly related to the primitive data of the PDMP to ensure the existence of an optimal control. The present work can be seen as a continuation of the results derived in Costa and Dufour (2008). Our main assumptions are written in terms of some integro-differential inequalities related to the so-called expected growth condition, and geometric convergence of the post-jump location kernel associated to the PDMP. An example based on the capacity expansion problem is presented, illustrating the possible applications of the results developed in the paper.

Singular Perturbation for the Discounted Continuous Control of Piecewise Deterministic Markov Processes

This paper deals with the expected discounted continuous control of piecewise deterministic Markov processes (PDMP`s) using a singular perturbation approach for dealing with rapidly oscillating parameters. The state space of the PDMP is written as the product of a finite set and a subset of the Euclidean space a""e (n) . The discrete part of the state, called the regime, characterizes the mode of operation of the physical system under consideration, and is supposed to have a fast (associated to a small parameter epsilon > 0) and a slow behavior. By using a similar approach as developed in Yin and Zhang (Continuous-Time Markov Chains and Applications: A Singular Perturbation Approach, Applications of Mathematics, vol. 37, Springer, New York, 1998, Chaps. 1 and 3) the idea in this paper is to reduce the number of regimes by considering an averaged model in which the regimes within the same class are aggregated through the quasi-stationary distribution so that the different states in this class are replaced by a single one. The main goal is to show that the value function of the control problem for the system driven by the perturbed Markov chain converges to the value function of this limit control problem as epsilon goes to zero. This convergence is obtained by, roughly speaking, showing that the infimum and supremum limits of the value functions satisfy two optimality inequalities as epsilon goes to zero. This enables us to show the result by invoking a uniqueness argument, without needing any kind of Lipschitz continuity condition.

A positive growth response to NaCl applications in Eucalyptus plantations established on K-deficient soils

Contrasting responses of Eucalyptus trees to K fertilizer applications have been reported on soils with low K contents. A complete randomized block experiment was set up in Brazil to test the hypothesis that large atmospheric deposits of NaCl in coastal regions might lead to a partial substitution of K by Na in Eucalyptus physiology and enhance tree growth. Treatments with application of 1.5, 3.0, 4.5 kmol K ha(-1) (K(1.5), K(3.0), 1(4.5, respectively) as KCl, 3.0 kmol K ha(-1) applied as K(2)SO(4), 3.0 kmol Na ha(-1) (Na(3.0)) as NaCl commercialized for cattle feeding, and a mixture of 1.5 kmol K + 1.5 kmol Na ha(-1) (K(1.5) + Na(1.5)) were compared to a control treatment (C) with no K and Na applications. All the plots were fertilized with large amounts of the other nutrients. A positive effect of NaCl applications on the growth of E. grandis trees was observed. NaCl and KCl additions in treatments Na(3.0) and K(3.0) increased above-ground biomass by 56% and 130% three years after planting, respectively, in comparison with the C treatment. By contrast, accumulated litterfall up to age 3 years was not significantly modified. NaCl applications in the Na(3.0) treatment significantly increased Na accumulation in above-ground tree components but did not modify K accumulation, whatever the sampling age. A partial substitution of K by Na in tree physiology, as observed for various agricultural crops, might explain this behaviour. Our results suggest the possibility of applying inexpensive K fertilizers, which are less purified in Na, and explain why high yields are achieved without K fertilizer applications in areas with large dry depositions of marine aerosols. Further investigations are necessary to identify the processes involving Na in Eucalyptus tree physiology. (C) 2009 Elsevier B.V. All rights reserved.

Drip fungigation in early blight control of tomato

The aim was to verify if the fungigation via drip irrigation is an alternative to the conventional method of spraying on tomato for controlling early blight. Tomato plants (variety Santa Clara) were grown in pots inside a greenhouse. Fifty days after transplanting, the plants were inoculated with Alternaria solani and treated with four different fungicides: azoxystrobin (8 g 100 L(-1)), difeconazole (50 mL 100 L(-1)), metiram+piraclostrobin (200 g 100 L(-1)) and tebuconazole (100 mL 100 L(-1)) using two applications methods: conventional spraying and fiingigation dripping. The control plants did not receive fungicide application. To assess the severity of the disease, we used a rating scale expressed as the area under the disease progress curve (AUDPC) and production factors, such as number, weight and average diameter of the fruit and its productivity. The experimental design was completely randomized in factorial scheme 4 x 2 + 1 with eight replicates. Each plot had one plant in one pot. A 27% reduction in disease severity was observed when compared with the control plants, with no significant difference noted regarding the application method. The number of fruits did not statistically differ between the treatments. The average weight and diameter of the fruits were superior in the plants that had fungicide application compared to the control plant, reflecting an increase in productivity. Fungigation through water dripping is an alternative to the conventional method of spraying cultured tomatoes.

Sterilization of Medical Devices by Ethylene Oxide, Determination of the Dissipation of Residues, and Use of Green Fluorescent Protein as an Indicator of Process Control

Ethylene oxide (EO) is used to sterilize Oxygenator and Tubing applied to heart surgery. Residual levels of EO and its derivatives, ethylene chlorohydrin (ECH) and ethylene glycol (EG), may be hazardous to the patients. Therefore, it must be removed by the aeration process. This study aimed to estimate the minimum aeration time for these devices to attain safe limits for use (avoiding excessive aeration time) and to evaluate the Green Fluorescent Protein (GFP) as a biosensor capable of best indicating the distribution and penetration of EO gas throughout the sterilization chamber. Sterilization cycles of 2, 4, and 8 h were monitored by Bacillus atrophaeus ATCC 9372 as a biological indicator (131) and by the GFP. Residual levels of EO, ECH, and EG were determined by gas chromatography (GC), and the residual dissipation was studied. Safe limits were reached right after the sterilization process for Oxygenator and after 204 h of aeration for Tubing. In the 2 h cycle, the GFP concentration decreased from 4.8 (+/- 3.2)% to 7.5 (+/- 2.5)%. For the 4 h cycle, the GFP concentration decreased from 17.4 (+/- 3.0)% to 21.5 (+/- 6.8)%, and in the 8 h cycle, it decreased from 22.5 (+/- 3.2)% to 23.9 (+/- 3.9)%. This finding showed the potentiality for GFP applications as an EO biosensor. (C) 2009 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 9113: 626-630, 2009

Decentralized control design for nonlinear plants: a v-metric approach

In this paper, a new v-metric based approach is proposed to design decentralized controllers for multi-unit nonlinear plants that admit a set of plant decompositions in an operating space. Similar to the gap metric approach in literature, it is shown that the operating space can also be divided into several subregions based on a v-metric indicator, and each of the subregions admits the same controller structure. A comparative case study is presented to display the advantages of proposed approach over the gap metric approach. (C) 2000 Elsevier Science Ltd. All rights reserved.

Almost automorphic mild solutions to some partial neutral functional-differential equations and applications

The paper considers the existence and uniqueness of almost automorphic mild solutions to some classes of first-order partial neutral functional-differential equations. Sufficient conditions for the existence and uniqueness of almost automorphic mild solutions to the above-mentioned equations are obtained. As an application, a first-order boundary value problem arising in control systems is considered. (C) 2007 Elsevier Ltd. All fights reserved.

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.

Environmental control of potential yield of sunflower in the subtropics

A simple framework was used to analyse the determinants of potential yield of sunflower (Helianthus annuus L.) in a subtropical environment. The aim was to investigate the stability of the determinants crop duration, canopy light interception, radiation use efficiency (RUE), and harvest index (HI) at 2 sowing times and with 3 genotypes differing in crop maturity and stature. Crop growth, phenology, light interception, yield, prevailing temperature, and radiation were recorded and measured throughout the crop cycle. Significant differences in grain yield were found between the 2 sowings, but not among genotypes within each sowing. Mean yields (0% moisture) were 6 . 02 and 2 . 17 t/ha for the first sowing, on 13 September (S1), and the second sowing, on 5 March (S2), respectively. Exceptionally high yields in S1 were due to high biomass assimilation associated with the high radiation environment, high light interception owing to a greater leaf area index, and high RUE (1 . 47-1 . 62 g/MJ) across genotypes. It is proposed that the high RUE was caused by high levels of available nitrogen maintained during crop growth by frequent applications of fertiliser and sewage effluent as irrigation. In addition to differences in the radiation environment, the assimilate partitioned to grain was reduced in S2 associated with a reduction in the duration of grain-filling. Harvest index was 0 . 40 in S1 and 0 . 25 in S2. It is hypothesised that low minimum temperatures experienced in S2 reduced assimilate production and partitioning, causing premature maturation.