A stable one-step-ahead predictive control of non-linear systems

In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence of plant uncertainties and input–output constraints. There is no requirement that the plant should be open-loop stable and the analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectiveness of controllers designed according to the algorithm analyzed in this paper is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis function neural network is employed as the non-linear system model.

Reliability comparison of transmit/receive diversity and error control coding in low-power medium access control protocols

Low-power medium access control (MAC) protocols used for communication of energy constraint wireless embedded devices do not cope well with situations where transmission channels are highly erroneous. Existing MAC protocols discard corrupted messages which lead to costly retransmissions. To improve transmission performance, it is possible to include an error correction scheme and transmit/receive diversity. It is possible to add redundant information to transmitted packets in order to recover data from corrupted packets. It is also possible to make use of transmit/receive diversity via multiple antennas to improve error resiliency of transmissions. Both schemes may be used in conjunction to further improve the performance. In this study, the authors show how an error correction scheme and transmit/receive diversity can be integrated in low-power MAC protocols. Furthermore, the authors investigate the achievable performance gains of both methods. This is important as both methods have associated costs (processing requirements; additional antennas and power) and for a given communication situation it must be decided which methods should be employed. The authors’ results show that, in many practical situations, error control coding outperforms transmission diversity; however, if very high reliability is required, it is useful to employ both schemes together.

Environmental Objectives, Cost Efficiency, and Multivariate Stochastic Control

This paper is concerned with the cost efficiency in achieving the Swedish national air quality objectives under uncertainty. To realize an ecologically sustainable society, the parliament has approved a set of interim and long-term pollution reduction targets. However, there are considerable quantification uncertainties on the effectiveness of the proposed pollution reduction measures. In this paper, we develop a multivariate stochastic control framework to deal with the cost efficiency problem with multiple pollutants. Based on the cost and technological data collected by several national authorities, we explore the implications of alternative probabilistic constraints. It is found that a composite probabilistic constraint induces considerably lower abatement cost than separable probabilistic restrictions. The trend is reinforced by the presence of positive correlations between reductions in the multiple pollutants.

Drought tolerance is associated with rooting depth and stomatal control of water use in clones of Coffea canephora

center dot Background and Aims Drought is a major environmental constraint affecting growth and production of Coffea canephora. Selection of C. canephora clones has been largely empirical as little is known about how clones respond physiologically to drought. Using clones previously shown to differ in drought tolerance, this study aimed to identify the extent of variation of water use and the mechanisms responsible, particularly those associated morphological traits.center dot Methods Clones (14 and 120, drought-tolerant; 46 and 109A, drought-sensitive, based on their abilities to yield under drought) were grown in 120-L pots until they were 12-months old, when an irrigation and a drought treatment were applied; plants were droughted until the pressure potential (Psi(x)) before dawn (pre-dawn) reached -3.0 MPa. Throughout the drought period, Psi(x) and stomatal conductance (g(s)) were measured. At the end of the experiment, carbon isotope ratio and parameters from pressure-volume curves were estimated. Morphological traits were also assessed.center dot Key Results and Conclusions With irrigation, plant hydraulic conductance (K-L), midday Psi(x) and total biomass were all greater in clones 109A and 120 than in the other clones. Root mass to leaf area ratio was larger in clone 109A than in the others, whereas rooting depth was greater in drought-tolerant than in drought-sensitive clones. Predawn Psi(x) of -3.0 MPa was reached fastest by 109A, followed progressively by clones 46, 120 and 14. Decreases in g(s) with declining Psi(x), or increasing evaporative demand, were similar for clones 14, 46, and 120, but lower in 109A. Carbon isotope ratio increased under drought; however, it was lower in 109A than in other clones. For all clones, Psi(x), g(s) and KL recovered rapidly following re-watering. Differences in root depth, KL and stomatal control of water use, but not osmotic or elastic adjustments, largely explained the differences in relative tolerance to drought stress of clones 14 and 120 compared with clones 46 and 109A.

Necessary conditions for optimal impulsive control problems

A Maximum Principle is derived for a class of optimal control problems arising in midcourse guidance, in which certain controls are represented by measures and, the state trajectories are functions of bounded variation. The optimality conditions improves on previous optimality conditions by allowing nonsmooth data, measurable time dependence, and a possibly time varying constraint set for the conventional controls.

Economic design of Shewhart control charts for monitoring autocorrelated data with skip sampling strategies

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The benefits of soft sensor and multi-rate control for the implementation of wireless networked control systems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Solving the multiobjective environmental/economic dispatch problem using weighted sum and ε-constraint strategies and a predictor-corrector primal-dual interior point method

This paper proposes a technique for solving the multiobjective environmental/economic dispatch problem using the weighted sum and ε-constraint strategies, which transform the problem into a set of single-objective problems. In the first strategy, the objective function is a weighted sum of the environmental and economic objective functions. The second strategy considers one of the objective functions: in this case, the environmental function, as a problem constraint, bounded above by a constant. A specific predictor-corrector primal-dual interior point method which uses the modified log barrier is proposed for solving the set of single-objective problems generated by such strategies. The purpose of the modified barrier approach is to solve the problem with relaxation of its original feasible region, enabling the method to be initialized with unfeasible points. The tests involving the proposed solution technique indicate i) the efficiency of the proposed method with respect to the initialization with unfeasible points, and ii) its ability to find a set of efficient solutions for the multiobjective environmental/economic dispatch problem.

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.

Modified constraint-induced movement therapy and modified forced-use therapy for stroke patients are both effective to promote balance and gait improvements

Background: Previous studies show that chronic hemiparetic patients after stroke, presents inabilities to perform movements in paretic hemibody. This inability is induced by positive reinforcement of unsuccessful attempts, a concept called learned non-use. Forced use therapy (FUT) and constraint induced movement therapy (CIMT) were developed with the goal of reversing the learned non-use. These approaches have been proposed for the rehabilitation of the paretic upper limb (PUL). It is unknown what would be the possible effects of these approaches in the rehabilitation of gait and balance. Objectives: To evaluate the effect of Modified FUT (mFUT) and Modified CIMT (mCIMT) on the gait and balance during four weeks of treatment and 3 months follow-up. Methods: This study included thirty-seven hemiparetic post-stroke subjects that were randomly allocated into two groups based on the treatment protocol. The non-paretic UL was immobilized for a period of 23 hours per day, five days a week. Participants were evaluated at Baseline, 1st, 2nd, 3rd and 4th weeks, and three months after randomization. For the evaluation we used: The Stroke Impact Scale (SIS), Berg Balance Scale (BBS) and Fugl-Meyer Motor Assessment (FM). Gait was analyzed by the 10-meter walk test (T10) and Timed Up & Go test (TUG). Results: Both groups revealed a better health status (SIS), better balance, better use of lower limb (BBS and FM) and greater speed in gait (T10 and TUG), during the weeks of treatment and months of follow-up, compared to the baseline. Conclusion: The results show mFUT and mCIMT are effective in the rehabilitation of balance and gait. Trial Registration ACTRN12611000411943.

Modelling and analysis of networked control strategies in smart power distribution grids: development of co-simulation tools

This thesis is focused on Smart Grid applications in medium voltage distribution networks. For the development of new applications it appears useful the availability of simulation tools able to model dynamic behavior of both the power system and the communication network. Such a co-simulation environment would allow the assessment of the feasibility of using a given network technology to support communication-based Smart Grid control schemes on an existing segment of the electrical grid and to determine the range of control schemes that different communications technologies can support. For this reason, is presented a co-simulation platform that has been built by linking the Electromagnetic Transients Program Simulator (EMTP v3.0) with a Telecommunication Network Simulator (OPNET-Riverbed v18.0). The simulator is used to design and analyze a coordinate use of Distributed Energy Resources (DERs) for the voltage/var control (VVC) in distribution network. This thesis is focused control structure based on the use of phase measurement units (PMUs). In order to limit the required reinforcements of the communication infrastructures currently adopted by Distribution Network Operators (DNOs), the study is focused on leader-less MAS schemes that do not assign special coordinating rules to specific agents. Leader-less MAS are expected to produce more uniform communication traffic than centralized approaches that include a moderator agent. Moreover, leader-less MAS are expected to be less affected by limitations and constraint of some communication links. The developed co-simulator has allowed the definition of specific countermeasures against the limitations of the communication network, with particular reference to the latency and loss and information, for both the case of wired and wireless communication networks. Moreover, the co-simulation platform has bee also coupled with a mobility simulator in order to study specific countermeasures against the negative effects on the medium voltage/current distribution network caused by the concurrent connection of electric vehicles.

Neuronal polarity selection by topography-induced focal adhesion control

Interaction between differentiating neurons and the extracellular environment guides the establishment of cell polarity during nervous system development. Developing neurons read the physical properties of the local substrate in a contact-dependent manner and retrieve essential guidance cues. In previous works we demonstrated that PC12 cell interaction with nanogratings (alternating lines of ridges and grooves of submicron size) promotes bipolarity and alignment to the substrate topography. Here, we investigate the role of focal adhesions, cell contractility, and actin dynamics in this process. Exploiting nanoimprint lithography techniques and a cyclic olefin copolymer, we engineered biocompatible nanostructured substrates designed for high-resolution live-cell microscopy. Our results reveal that neuronal polarization and contact guidance are based on a geometrical constraint of focal adhesions resulting in an angular modulation of their maturation and persistence. We report on ROCK1/2-myosin-II pathway activity and demonstrate that ROCK-mediated contractility contributes to polarity selection during neuronal differentiation. Importantly, the selection process confined the generation of actin-supported membrane protrusions and the initiation of new neurites at the poles. Maintenance of the established polarity was independent from NGF stimulation. Altogether our results imply that focal adhesions and cell contractility stably link the topographical configuration of the extracellular environment to a corresponding neuronal polarity state.

Development of Dynamic Constraint Models for a Model Based Transient Calibration Process

Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control. Parameters for this second order system have been extracted from real transient data. The model has been shown to be the best choice relative to a list of appropriate candidates such as neural networks and first order models. The selected second order model was used in conjunction with transient emission models to predict emissions over the FTP cycle. It has been shown that emission predictions based on air-handing parameters predicted by the dynamic constraint model do not differ significantly from corresponding emissions based on measured air-handling parameters.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

Ocean acidification reduces the crystallographic control in juvenile mussel shells

Global climate change threatens the oceans as anthropogenic carbon dioxide causes ocean acidification and reduced carbonate saturation. Future projections indicate under saturation of aragonite, and potentially calcite, in the oceans by 2100. Calcifying organisms are those most at risk from such ocean acidification, as carbonate is vital in the biomineralisation of their calcium carbonate protective shells. This study highlights the importance of multi-generational studies to investigate how marine organisms can potentially adapt to future projected global climate change. Mytilus edulis is an economically important marine calcifier vulnerable to decreasing carbonate saturation as their shells comprise two calcium carbonate polymorphs: aragonite and calcite. M. edulis specimens were cultured under current and projected pCO2 (380, 550, 750 and 1000 µatm), following 6 months of experimental culture, adults produced second generation juvenile mussels. Juvenile mussel shells were examined for structural and crystallographic orientation of aragonite and calcite. At 1000 µatm pCO2, juvenile mussels spawned and grown under this high pCO2 do not produce aragonite which is more vulnerable to carbonate under-saturation than calcite. Calcite and aragonite were produced at 380, 550 and 750 µatm pCO2. Electron back scatter diffraction analyses reveal less constraint in crystallographic orientation with increased pCO2. Shell formation is maintained, although the nacre crystals appear corroded and crystals are not so closely layered together. The differences in ultrastructure and crystallography in shells formed by juveniles spawned from adults in high pCO2 conditions may prove instrumental in their ability to survive ocean acidification.