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There has been a considerable amount of work done on tracking the boundaries of specified regions. The present paper focuses on the same problem. However, unlike previous works, this paper uses the cyclic pursuit scheme and its variants to locate the boundaries of specified regions, when the agents are initially deployed away from the region, and subsequently track the boundary continuously. The number of agents required under this paradigm is also considerably lesser than in existing strategies. In almost all cases, the algorithm described in this paper guarantees the convergence to the boundary. Simulation results have been presented to substantiate the efficacy of the proposed scheme.

In Model Predictive Control (MPC), an optimization problem has to be solved at each sampling time, and this has traditionally limited the use of MPC to systems with slow dynamic. In this paper, we propose an efficient solution strategy for the unconstrained sub-problems that give the search-direction in Interior-Point (IP) methods for MPC, and that usually are the computational bottle-neck. This strategy combines a Riccati-like solver with the use of high-performance computing techniques: in particular, in this paper we explore the performance boost given by the use of single precision computation, and techniques such as inexact search direction and mixed precision computation. Finally, we test our HPMPC toolbox, a family of high-performance solvers tailored for MPC and implemented using these techniques, that is shown to be several times faster than current state-of-the-art solvers for linear MPC.

This paper introduces a correlation analysis between the structural changes occurring in the lungs and the corresponding variations in the fractional order value of an impedance model. It was discussed how variations in the wall thickness, cross-sectional area and elastic moduli in distal airways involved in respiratory process affect changes in the fractional order value. Two lumped models were discussed: i) a theoretical model derived from morphological information and ii) a lumped parametric model. Results indicate that a correlation analysis is possible for various degrees of obstruction and effects may be directly related to the identified fractional order value.

In this paper, we study the variance optimization problem in Markov decision processes (MDP). The objective is to find the optimal policy which has the minimal average variance of the system rewards. As the variance function is quadratic and the variance of rewards are correlated mutually, the associated variance minimization problem is not a linear program. The traditional approaches of classical MDP theory, which are good at solving linear problems, are inapplicable to this problem. In this paper, we define a fundamental quantity called variance potential and derive a variance difference equation which quantifies the difference of variances of Markov systems under any two policies. Based on the variance difference equation, we propose an iterative algorithm, which is similar to the policy iteration in classical MDP theory, to reduce the reward variance of Markov systems. Although this algorithm converges to a local optimum, it is very efficient compared with the traditional gradient-based algorithms. Numerical experiments demonstrate the main idea of this paper.

There are two kinds of processes: the deterministic ones, in which the behavior in time is known, and the stochastic ones, which is governed by density probabilities functions. A stochastic process is defined as a Markov Process if the future state depends only on the current state and not on the previous ones. Markov Chains are stochastic processes with Markov properties. They are applicable in several areas, including Logistics. This paper is concerned with the problem of pest behavior for agriculture. It will be presented the basic concepts of Markov Chains, as well as their analysis and properties. After that, it will be assessed some stochastic Markov processes found in literature. Finally this paper presents a proposal for a modeling of a pest behavior for an agriculture production system.

In this paper, we show that the controller synthesis of delayed systems can be formulated and solved in a convex manner through the use of a duality transformation, a structured class of operators, and the Sum-of-Squares (SOS) methodology. The contributions of this paper are as follows. We show that a dual stability condition can be formulated in terms of Lyapunov operators which are positive, self-adjoint and preserve the structure of the state-space. Second, we provide a class of such operators which can be parameterized using Sum-of-Squares. Next, we show how any operator in this class can be inverted using simple operations on the SOS variables which can be performed in Matlab. Next we use SOS and semidefinite programming to formulate a dual stability test for time-delay systems. Next, we use the dual stability results to formulate a convex test for stabilizability and show how SOS can be used to solve this test and recover the controller. Finally, we give a numerical example. The results of this paper are significant in that they open the way for dynamic output H∞ optimal control of infinite-dimensional systems by giving the first truly convex, numerically realizable full-state feedback controller synthesis criterion.

International audience; In this paper, a nonlinear conservation law is proposed for the goal of optimal location of a mobile sensor continuum. The monitoring of pollution on a 2D domain is used throughout the paper to illustrate the effectiveness of the proposed approach.

In this paper we consider repeated coalitional games with transferable utilities (TU) over networks. Namely, we consider a set of n players that have to distribute among themselves a vector of rewards (one for each player). In our network version there is no coordinator allocating the rewards, but the agents have to agree on a common time-averaged vector by updating the local estimates of the reward vector. The common time-averaged reward vector has to approach a suitable constraint set, called core of the game, that guarantees that no agents benefit from quitting the grand coalition. We propose a doubly (over time and space) averaging distributed algorithm. At every iteration, each agent first computes a weighted average of its own time-averaged estimate and those of his neighbors and then generates a new reward vector in order to drive the time-averaged estimate towards a pre-assigned set. The main contribution of the paper is to prove that under certain assumptions, i) all agents' estimates reach consensus on the true time-averaged reward vector, and ii) the estimates (and thus the true time-averaged reward vector) approach the pre-assigned set. Conditions for this to happen are related to the connectivity over time of the communication topology and to the approachability principle.

This paper presents a Basis Pursuit DeNoising (BPDN) sparse estimation approach as a regularization technique in a predictor-based subspace method for the identification of Linear Parameter-Varying (LPV) state-space systems. It is known that in this identification method, the choice of the past window of a state predictor factorization will influence the conditioning of the main parameter estimation problem. Therefore, prior knowledge of the system order may be needed to choose the past window in such a way that this problem is well-conditioned. It will be demonstrated that sparse estimation through BPDN can reduce the sensitivity of the conditioning with respect to the past window parameter. In this way, we can simplify the task of choosing the past window to an extend that the need for prior knowledge of the system order is eliminated. Also, this paper will pay attention to the synthesis of stabilizing observer gain matrices in the identified LPV innovation-type state-space model.

This paper proposes and explores the properties of a solution algorithm designed to solve network simulation problems to optimality. Emphasis is put on the system being divisible into components, as this underlying assumption motivates the algorithm in its entirety in that rather simple relations between the system components are modeled as explicit structural constraints, while the significantly more complex relations within each component are approximated based on the underlying simulator data. The Real-Time petroleum Production Optimization (RTPO) problem is used as an example of such a network simulation problem that can benefit from using the solution approach outlined. The paper also includes the mathematical formulation of the RTPO, followed by a description of the algorithm and a comprehensive study of the algorithm's properties on a real RTPO application. Finally, the results are compared to a reference case and conclusions are reached about the method.

This paper describes modeling steps for presenting energy buffers as convex models in power control applications. Except obtaining the optimal control, the paper also shows how convex optimization can be used to simultaneously size the energy buffer while optimally controlling a trajectory following system. The energy buffers are capacitors and batteries with quadratic power losses, while the resulting convex problem is a semidefinite program. The convex modeling steps are described through a problem of optimal buffer sizing and control of a hybrid electric vehicle. The studied vehicle is a city bus driven along a perfectly known bus line. The paper also shows modeling steps for alternative convex models where power losses and power limits of the energy buffer are approximated. The approximated models show significant decrease in computation time without visible impact on the optimal result.

In predictive control a nonlinear optimization problem has to be solved at each sample instant. Solving this optimization problem in a computationally efficient and numerically reliable fashion on an embedded system is a challenging task. This paper presents results to reduce the computational requirements for solving fundamental problems that arise when implementing predictive controllers in finite precision arithmetic. By employing novel formulations and tailor-made optimization algorithms, this paper shows that computational resources can be reduced using very low precision arithmetic. We also present new mathematical results that enable computational savings to be made in the most numerically critical part of an optimization solver, namely the linear algebra kernel, using fixed-point arithmetic. Our theoretical results are supported by numerical results from implementations on a Field Programmable Gate Array (FPGA).

With the topic of plug-in HEV city buses, this paper studies the highly coupled optimization problem of finding the most cost efficient compromise between investing in onboard electric powertrain components and installing a charging infrastructure along the bus line. The paper describes how convex optimization can be used to find the optimal battery sizing for a series HEV with fixed engine and generator unit and a fixed charging infrastructure along the bus line. The novelty of the proposed optimization approach is that both the battery sizing and the energy management strategy are optimized simultaneously by solving a convex problem. In the optimization approach the power haracteristics of the engine-generator unit are approximated by a convex, second order polynomial, and the convex battery model assumes quadratic losses. The paper also presents an example for a specific bus line, showing the dependence between the optimal battery sizing and the number of charging stations on the bus line.

This paper describes a new algorithm for initializing and estimating Wiener-Hammerstein models. The algorithm makes use of the best linear model of the system which is split in all possible ways into two linear sub-models. For all possible splits, a Wiener-Hammerstein model is initialized which means that a nonlinearity is introduced in between the two sub-models. The linear parameters of this nonlinearity can be estimated using least-squares. All initialized models can then be ranked with respect to their fit. Typically, one is only interested in the best one, for which all parameters are fitted using prediction error minimization. The paper explains the algorithm and the consistency of the initialization is stated. Computational aspects are investigated, showing that in most realistic cases, the number of splits of the initial linear model remains low enough to make the algorithm useful. The algorithm is illustrated on an example where it is shown that the initialization is a tool to avoid many local minima.

The airport transportation network structure and its performance are key factors to benefit the local area or national transportation capability and economic development. This paper presents a method based on the weighted algebraic connectivity to optimize and analyze the network. The indispensability of the weighted adjacent matrix and weighted Laplacian matrix in airport transportation network performance study is emphasized in this paper. According to practical operational viability, we formulate the weighted algebraic connectivity application as the flight routes addition/deletion problems. The greedy perturbation heuristic by Ghosh and Boyd is analyzed and extended into the Modified Greedy Perturbation Algorithm (MGP) to provide a local optimum in a fast iterative manner. The unweighted tabu search applying algebraic connectivity to airport transportation network by Vargo and Kincaid is re-designed as the Weighted Tabu Search (WTS) to fit the new weighted flight routes addition/deletion problems, potentially offering a global optimum with much longer running time. The simulation results indicate the trade-off between the Modified Greedy Perturbation and the Weighted Tabu Search, with which we can decide the appropriate algorithm to be used for enhancing the performance of an airport transportation network.

In this paper, the problems of delay-dependent stability analysis and stabilization are investigated for linear continuous-time systems with distributed delay. By introducing an integral partitioning technique, a new form of Lyapunov-Krasovskii functional (LKF) is constructed and improved distributed delay dependent stability conditions are established in terms of linear matrix inequalities (LMIs). Based on the criteria, a design algorithm for a state feedback controller is proposed. The results developed in this paper are less conservative than existing ones in the literature, which is illustrated by several examples.

This paper proposes a new probabilistic load flow (PLF) method that considers the non-Gaussianity and the correlation of nodal input variables. The load flow calculation plays an important role to evaluate power system conditions. In recent years, power systems become more complicated due to renewable energy such as wind power generators and PV systems. As a result, the impotence of PLF has been reevaluated as one of the promising techniques for handling the network uncertainties. This paper focuses on the applications of Monte Carlo Simulation (MCS) to PLF so that the effect of the correlations of input variables and the network uncertainties is evaluated through the nonlinear equation. In this paper, new techniques are introduced into MCS-PLF to evaluate more accurate solutions. The maximum likelihood estimation for a probability density function (PDF) of input variables is carried out to construct the non-Gaussian distribution model of input variables with the correlation by the Deterministic Annealing Expectation Maximization (DAEM) algorithm. Also, the Metropolis-Hastings sampling is used to generate random numbers that correspond to complicated multivariate probability density functions. The proposed method is successively applied to a system with wind power generators.

Partially observable Markov decision processes(POMDPs) provide a framework for the optimization of Markov systems when there exist multiple sources of uncertainty: besides the system's stochastic dynamics, there are also observation noises. While POMDPs have many real applications, existing approaches to searching global optimal policy is computationally intractable even for systems with small sizes. In this paper, we apply the idea of the recently developed event-based optimization approach to study POMDP problems with infinite horizon setting. Based on this approach, a perturbation analysis based algorithm can be proposed to search for a local optimal policy. Further more, under a certain condition, a policy iteration type algorithm can be developed. We find that such a condition is satisfied for some partially observable systems in the financial engineering area. As an example, we discuss a portfolio management problem at the end of the paper.

The computation of envelopes enclosing the possible states and/or outputs of a class of uncertain linear dynamical systems is the subject of this paper. The resulting algorithm can be useful in several areas of control systems such as verification of safety properties and fault diagnosis (in order to choose suitable thresholds on some residuals, for instance). A particular class of polytopes, zonotopes, can be used to implicitly represent the computed sets. The related reachability algorithms have shown to be well suited to control the wrapping effect in the case of linear dynamical systems. However, parametric uncertainties, when taken into account, are often modeled by interval matrices which lead to a loss of parametric dependencies and result in the computation of rather pessimistic sets. The main contribution of this paper consists in extending an existing algorithm based on zonotopes so that it can efficiently propagate structured parametric uncertainties. A 6th order oscillating mass-spring system illustrates the resulting control of the wrapping effect by comparison with Monte-Carlo simulations.

In this paper, we study the convergence of the pheromone levels of Ant Colony Learning (ACL) in the setting of discrete state spaces and noiseless state transitions. ACL is a multi-agent approach for learning control policies that combines some of the principles found in ant colony optimization and reinforcement learning. Convergence of the pheromone levels in expected value is a necessary requirement for the convergence of the learning process to optimal control policies. In this paper, we derive upper and lower bounds for the pheromone levels and relate those to the learning parameters and the number of ants used in the algorithm. We also derive upper and lower bounds on the expected value of the pheromone levels.

The paper addresses the realization and identification problem for a subclass of piecewise-affine hybrid systems. The paper provides necessary and sufficient conditions for existence of a realization by a piecewise-affine hybrid system. The paper also proposes an identification algorithm for this subclass of hybrid systems. The considered system class and the identification problem are motivated by applications in systems biology.

Max-plus linear systems belong to a special class of discrete-event systems that consists of systems with synchronization but no choice. Our focus in this paper is on stochastic max-plus linear systems, i.e., perturbed systems that are linear in the max-plus algebra. One interesting topic is the identification of such systems. Previous works report on a method for identifying the parameters of a state space model for a stochastic max-plus linear system from measured data. However, due to the structure of such systems, this method results in a complex identification problem. Therefore, the aim of this paper is to decrease the computational complexity and the computation time of this problem. To this end, we use an approximation approach that is based on the higher-order raw moments of a random variable. This method results in a less complex problem that can be solved efficiently using gradient search techniques.

This paper presents a concept for converting a discrete event model, modeled with Extended Finite Automata (EFA), to mixed-integer linear constraints. The conversion handles the structure of modular EFAs, synchronization of EFAs using shared events and EFA execution order due to logical transition conditions. The paper also presents methods to reduce the number of variables and constraints by automatically analyzing the EFA model and the resulting problem formulation. An example of this is the special case of transition conditions used to model mutual exclusion of shared resources, where the conversion results in a significantly reduced problem formulation. The objective function is then built by summarizing weighted state cost functions and the result is a Mixed-Integer Nonlinear Programming problem. The main contribution of this paper is hence the combination of the simplicity in modeling a system with EFAs and an efficient formulation of the optimization problem that can be solved by standard optimization software.

As reported in earlier work, methods for hybrid optimal control can be used for optimal path planning of multi-agent systems. This paper considers a multi-vehicle transport scenario in which a hybrid model is used to represent the continuous dynamics of the vehicle motion and the dynamics arising from the docking-events between vehicles and a transported object. The decisions on the vehicles’ heading and speed as well as on the docking sequence are strongly coupled and require a tailored numeric solution. The paper proposes a hierarchical solution strategy with three layers: on the upper layer a discrete event sequencing problem is solved, the middle layer determines docking positions and times, and the lower layer provides optimal vehicle paths. By a combination of graph search and embedded continuous optimal control, the efficiency of obtaining (sub-)optimal solutions is improved. The algorithm is illustrated by a scenario in which three vehicles transport one object.

The paper investigates the case where N agents solve a complex search problem by communicating to each other their relative successes in solving the task. The problem consists in identifying a set of unknown points distributed in an n–dimensional space. The interaction rule causes the agents to organize themselves so that, asymptotically, each agent converges to a different point. The emphasis of this paper is on analyzing the collective dynamics resulting from nonlinear interactions and, in particular, to prove convergence of the search process.

International audience; This paper addresses production systems optimisation in uncertain context. A standard framework for solving such type of problems is depicted in a 3-step approach. The two first steps are addressed in this paper. They consist of off-line characterisation of the problem and calculation of solutions with some desired performance. A generic approach to implement these off-line steps is introduced in this paper. This approach relies on calculation of robust off-line solutions. A generic framework of robustness is defined. Then five standard optimisation problems are derived and related to the so called stability and sensitivity analysis. This generic approach is then applied to a multi-purpose machines problem.

Marginal costs in traffic networks are the extra costs incurred to the system as the result of extra traffic. Marginal costs are required frequently e.g. when considering system optimal traffic assignment or tolling problems. When explicitly considering spillback in a traffic flow model, one can use a numerical derivative or resort to heuristics to calculate the marginal costs. Numerical derivatives are computationally demanding, restricting its use to simple networks. Heuristic approaches in most cases approximate the marginal costs by only considering the extra costs on the links which are traveled by the extra traffic, excluding the possibly external costs incurred on other links due to spillback. This paper proposes a novel way to estimate the true marginal costs of traffic in a dynamic discrete LWR model which correctly deals with congestion onset, spillback and dissolution. The proposed methodology tracks virtual changes in density through the network by means of particles which travel along with the characteristics of traffic. By using density based cost functions, the virtual changes in density can be directly related to the marginal costs. The computational efficiency of the methodology stems from the fact that only local conditions are considered when propagating the virtual change in density. The paper discusses the methodology and necessary model extensions, provides a numerical validation experiment illustrating the exact detail of the solution by comparison to a numerical derivative and discusses some generalizations.

International audience; This paper deals with the consensus problem under network induced communication delays. It is well-known that introducing a delay generally leads to a reduce of the performance or to instability. Thus, investigating the impact of time-delays in the consensus problem is an important issue. Another important issue is to obtain an estimate of the convergence rate, which is not straightforward when delays appear in the network. In this paper, the agents are modelled as double integrator systems. It is assumed that each agent receives instantaneously its own output information but receives the information from its neighbors after a constant delay. A stability criterion is provided based on Lyapunov-Krasovskii techniques and is expressed in terms of LMI. An expression of the consensus equilibrium which depends on the delay and on the initial conditions taken in an interval is derived. The results are supported through several simulations for different network symmetric communication schemes.

International audience; The Two-Stage Algorithm (TSA) has been extensively used and adapted for the identification of Hammerstein systems. It is essentially based on a particular formulation of Hammerstein systems in the form of bilinearly parameterized linear regressions. This paper has been motivated by a somewhat contradictory fact: though the optimality of the TSA has been established by Bai in 1998 only in the case of some special weighting matrices, the unweighted TSA is usually used in practice. It is shown in this paper that the unweighted TSA indeed gives the optimal solution of the weighted nonlinear least-squares problem formulated with a particular weighting matrix. This provides a theoretical justification of the unweighted TSA, and leads to a generalization of the obtained result to the case of colored noise with noise whitening. Numerical examples of identification of Hammerstein systems are presented to validate the theoretical analysis.

The objective of the paper is to investigate relationships among different convex relaxations for quadratic distance problems. The main motivation is that a number of problems in robust control can be cast as minimum distance problems from a point to a polynomial surface. It is proven that two families of relaxations proposed in the literature, both based on sum of squares, are equivalent: the former exploits properties of homogeneous forms, while the latter relies on the Positivstellensatz theorem. It is also shown that two different relaxations based on Positivstellensatz present different levels of conservativeness. The results presented in the paper provide useful insights on the trade off between computational burden and conservativeness of the considered relaxations.

In the paper a two-phase MPC controller is described that uses a non-quadratic terminal cost. The terminal cost is computed with respect to a predefined control strategy, and it can therefore be treated as the second phase of the method. Because the problem of optimizing the MPC objective is not a quadratic programming problem, medium-scale, gradient-based optimization methods are used for the MPC implementation. The method is efficient for problems which have a lot of soft constraints, because it avoids the introduction of slack variables. The stability of the MPC is established in the paper, and the method is compared with an unconstrained MPC in the grinding plant optimization problem.

This paper presents the formulation of a flight clearance criterion as a convex optimization problem. The criterion is the stability margins criterion which is specified as an allowable phase and gain margin of a certain loop transfer function. The satisfaction of the criterion amounts to the Nichols plot of the loop transfer function being outside a specified trapezoidal region. It was shown previously that the exclusion condition from an ellipsoidal region is implied by using the generalized stability margin bPC and its calculation was performed frequency-wise by solving a sequence of convex optimization problems. In this paper we formulate the calculation of a lower bound on bPC as a convex optimization problem using Integral Quadratic Constraints (IQCs) and avoid the gridding procedure in the frequency domain. Furthermore, we formulate the problem of obtaining a lower bound on the perturbed stability margin, which is defined as the worst-case bPC over variations in real uncertain parameters.

This paper addresses the issue of the practical implementation of closed-form Model Predictive Controllers (MPC) to processes with very short sampling times. Such questions come in consideration when the solution to MPC problems is expressed in a so-called parametric or closed-form fashion. The underlying idea of this paper is to approximate the optimal control law defined over state space regions by a higher degree polynomial which then guarantees closed-loop stability, constraint satisfaction, and a bounded performance decay. The advantage of the proposed scheme lies in faster controller evaluation and lower storage demand compared to currently available techniques.

This paper considers the structure of uncertain linear systems building on concepts of robust unobservability and possible controllability which were introduced in previous papers. The paper presents a new geometric characterization of the possibly controllable states. When combined with previous geometric results on robust unobservability, the results of this paper lead to a general Kalman type decomposition for uncertain linear systems which can be applied to the problem of obtaining reduced order uncertain system models., Comment: To appear in Automatica

In this paper, the optimal filtering problem for polynomial system states over polynomial observations is studied proceeding from the general expression for the stochastic Ito differentials of the optimal estimate and the error variance. In contrast to the previously obtained results, the paper deals with the general case of nonlinear polynomial states and observations. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state over observations with any polynomial drift is then established. In the example, the obtained optimal filter is applied to solve the optimal third order sensor filtering problem for a quadratic state, assuming a Gaussian initial condition for the extended third order state vector. The simulation results show that the designed filter yields a reliable and rapidly converging estimate.

This paper presents a collision avoidance method to improve the safety conditions in scenarios with multiple UAVs sharing the same aerial space with other aircrafts or mobile obstacles. The method modies the velocity prole of the UAVs under control maintaining the paths initially planned. It is based on the combination of both a Search Tree algorithm which nds a solution if it exists, and the minimization of a cost function which tries to nd the nearest solution to the initially planned trajectories for the UAVs. The Search Tree algorithm provides an initial valid order of pass for the vehicles involved in a given conict and allows to formulate the minimization problem as a LP problem that can be eciently solved. A model for the UAVs has been considered in the paper and several simulations are presented with promising results.

This paper presents LQ control problem based on numerical computation with guaranteed accuracy. By using the proposed methods, it is possible to guarantee numerical quality about design of control system. This paper also proposes a problem which finds the numerical optimal controller from a set of solutions solved by verified LQ control problem.

The paper proposes an approach to designing the hybrid estimation algorithm/module (HEA) with moving measurements window for Wastewater Treatment Plant (WWTP) Robust Model Predictive Control (RMPC) purposes at medium time scale. The RMPC uses a dedicated grey-box model of biological reactor for the system outputs prediction purposes. The grey-box model parameters are dependant on the plant operating point. Hence, these parameters and grey-box model state should be estimated according to the current operating plant condition. The needed parameters and state estimates at medium time scale are provided by the HEA algorithm. The HEA consist of the set-bounded parameter and state estimation algorithm cooperated with the Extended Kalman Filter (EKF) algorithm. The HEA algorithm is used to ensure robustness of grey-box model parameter and state estimates in situation of limited amount of hard measurements on the WWTP. The state estimates provided by the EKF are used as pseudo measurements of the states and the set-bounded estimation algorithm produce sets bounding the grey-box model parameters and states. There are two hybrid estimation algorithms presented and described in the paper: algorithm of explicit method and algorithm of implicit method. Both hybrid estimation algorithms are validated by simulation base on real data records and calibrated model of Kartuzy WWTP in northern Poland.

In this paper, initial estimates of the attraction region for a class of nonlinear time delay systems are proposed. The approach is constructive, and make use of an appropiate Lyapunov-Krasovskii functional. Several illustrative examples (delayed logistic equation, congestion networks, hereditary phenomena in physics) complete the paper.

This paper addresses the challenging problem of finding collision-free trajectories for many robots moving to individual goals within a common environment. Most popular algorithms for multi-robot planning manage the complexity of the problem by planning trajectories for robots sequentially; such decoupled methods may fail to find a solution even if one exists. In contrast, this paper describes a multi-phase approach to the planning problem that guarantees a solution by creating and maintaining obstacle-free paths through the environment as required for each robot to reach its goal. Using a topological graph and spanning tree representation of a tunnel or corridor environment, the multi-phase planner is capable of planning trajectories for up to r = L – 1 robots, where the spanning tree includes L leaves. Monte Carlo simulations in a large environment with varying number of robots demonstrate that the algorithm can solve planning problems requiring complex coordination of many robots that cannot be solved with a decoupled approach, and is scalable with complexity linear in the number of robots.

6 pages; International audience; In order to reduce cost and to optimize the use of resources, hospitals are prompted to regroup facilities and human resources, especially in the surgical suite. This paper focuses on the anaesthesiology nurse scheduling problem (ANSP) of a French public hospital, where the anaesthesiology nurses constitute one of the most shared resources. They work in a cross way over surgical specialities and assume various activities. Two methods are proposed to solve the ANSP based on integer programming and constraint programming. The objective is to maximize the fairness of the schedule. Theses two techniques are tested in order to be compared.

This paper is concerned with regional stabilization: a domain of attraction for anti-windup control systems by a polytopic approach and a multiplier one respectively. In particular, the paper considers integrated stabilizing synthesis problems of dynamic output feedback controllers and anti-windup ones depending on the two approaches. The paper indicates that both synthesis problems can be recast as linear matrix inequality (LMI) optimization problems, where, for the synthesis, the outputs of the saturation elements are assumed to be available for the control. Finally, the paper clarifies the inclusion relation between the two synthesis conditions and systematical meanings of the conditions.

This paper proposes a probabilistic solution framework for robust control analysis and synthesis problems that can be expressed in the form of minimization of a linear objective subject to convex constraints parameterized by uncertainty terms. This includes for instance the wide class of NP-hard control problems representable by means of parameter-dependent linear matrix inequalities (LMIs). It is shown in this paper that by appropriate sampling of the constraints one obtains a standard convex optimization problem (the scenario problem) whose solution is approximately feasible for the original (usually infinite) set of constraints, i.e. the measure of the set of original constraints that are violated by the scenario solution rapidly decreases to zero as the number of samples is increased. We provide an explicit and efficient bound on the number of samples required to attain a-priori specified levels of probabilistic guarantee of robustness. A rich family of control synthesis problems which are in general hard to solve in a deterministically robust sense is therefore amenable to polynomial-time solution, if robustness is intended in the proposed risk-adjusted sense.

This paper describes the application of differential evolution algorithm (DEA) to the simple assembly line balancing problem (SALBP). DEA is an evolutionary algorithm similar to a real-coded genetic algorithm for global optimization over continues spaces. The paper is concerned with SALBP type-1 whose objective is to minimize the number of workstations required to manufacture a product in an assembly line within a given fixed cycle time. Extensive experimental work over public benchmarks test problems show the effectiveness of the proposed approach.

Linear Quadratic Differential games have been used to model economic and other conflict situations. A salient feature of these analyses was that the interval of play was fixed (possibly inifinite), and known to both players. This paper introduces a linear quadratic differential game where only the probability density of the terminal time is known to the two players. The paper's results are used to derive a differential game based guidance law for uncertain time of intercept.

In this paper, a robust scheduling method is suggested in the optimization of batch plant with uncertainties considering not only expected value but also variance. Many papers treating scenario-based stochastic programming took expected values as objective functions. However, the meaning of expected value itself is sum of the probability of each scenario times each objective value. It implies nothing but currently calculated biggest value. It doesn't work when unexpected event happens. Therefore it is required to consider an additional criterion. I will choose variance and standard deviation of objective function.

The modern trend in industrial application problem deserves modeling of all relevant vague or fuzzy information involved in a real decision making problem. In this paper the usefulness of the proposed S-curve membership function is established using a real life industrial production planning of a chocolate manufacturing unit. The unit produces 8 products using 8 raw materials; mixed in various proportions by 9 different processes under 29 constraints. A solution to this problem establishes the usefulness of the suggested membership function for decision making in industrial production planning. The objective of this paper is to find the optimal cost to produce 8 products using modified S-curve membership function as a methodology. The fuzzy linear programming approach is used to solved this problem. The optimal cost function is obtained respect to two major factors of degree of satisfaction and vagueness.

This paper investigates an application of neural networks to the guaranteed cost control problem of decentralized robust control for a class of discrete-time uncertain large-scale systems. Based on the Linear Matrix Inequality (LMI) design approach, a class of decentralized local state feedback controllers with additive gain perturbations is established. The novel contribution of this paper is that to reduce the large cost caused by the LMI conditions Neural Networks (NNs) are substituted for the additive gain perturbations. Although the NNs are included in the uncertain large-scale systems, the closed-loop system is asymptotically stable. Furthermore, it is shown that the closed-loop cost function is not more than the specified upper bound for all admissible uncertainties.

The paper addresses efficient methods for parameter sensitivity analysis and ranking in large, nonlinear, mechanistic models requiring examination of many points in the parameter space. The paper shows how orthogonal decomposition and permutation of the sensitivity derivative is an intuitive and structured method for automatic ranking of the parameters within a candidate set. Provided the model error is Gaussian, and with the problem on a triangularized form, the additional variance associated with each parameter can easily be found. Ranking according to additional variance is therefore another option. The methods are tested on an industrially used simulator model.

In this paper, we will establish a framework that can convert a robust output regulation problem for discrete-time nonlinear systems into a robust stabilization problem for an appropriately augmented system consisting of the given plant and a specific dynamic system called internal model. We then apply this framework to solve the local robust output regulation problem for a general class of discrete-time nonlinear systems. The results of this paper gives a discrete-time counterpart of the recent results on the continuous-time robust output regulation problem.