Search

This paper deals with the characterization of all the optimal solutions of the twomachine flowshop scheduling problem with maximum completion time criterion. A model is proposed, and maximal sequences are defined. A polynomial time algorithm is presented to determine the first maximal sequence and a tree-search algorithm is proposed to determine all the maximal sequences.

It was of some surprise, after listening to all the contributions, to conclude that nothing had been said about the theory and application of extremum-seeking devices based on Evolutionary Operation EVOP and its associated modifications such as SIMPLEX and COMPLEX. In many real control systems the values of the measured variables are obtained at discrete intervals of time, which may be of the order of minutes, and consequently there may be considerable delay in the evaluation of the Performance Index. Many of the continuous-perturbation methods proposed at the Symposium do not lend themselves to systems of the former type, and it is thus desirable to be aware of alternative methods of control.

The paper introduces an optimal way of path planning in soccer game. A dynamic grids algorithm and a dynamic eluding obstacles algorithm have been proposed, which include the position-prediction, and obstacle detecting and controlling backward methods. An enhanced algorithm, introduced also in the paper based on the two techniques, results in simple and effective controlling in soccer game.

Proper selection of cooking times is one of the most important decision making tasks in batch pulp cooking. Duration of a batch depends on cooking conditions and must be chosen so that the Kappa number - a measure for pulp quality - reaches the desired value at the end of the batch. Due to the lack of appropriate industrial sensors for on-line Kappa measurements, Kappa can be determined only after cooking is over by means of analysis in the laboratory. Therefore, a model based system is developed for on-line Kappa estimation and control. The very heart of the system represents the estimation algorithm based on non-linear optimization. It combines a special purpose routine for approximate (but fast) optimum search and Gauss—Newton method for "fine" optimization. This paper surveys some relevant properties of the scheme such as model simplification, "fine" optimization, analysis of identifiability conditions and related numerical problems. A minimum variance Kappa controller is treated as well. Efficiency of the proposed scheme is confirmed by means of the simulated examples and results of practical implementation.

In this short paper we provide a criterion for the stability robustness of plant-controller families. The plant family and the controller family each consist of convex combinations of a finite number of generating elements. These elements are also allowed to include time-delays. In order to ascertain whether the plant-controller family is robustly stable, it is necessary and sufficient to check the stability of finitely many one-parameter feedback systems. Such a check can be accomplished using a Nyquist-like test.

For a rotary lime kiln used in the recausticizing process of a (kraft) paper mill, R mathematical model is presented. The form of the mathematical description is obtained on the basis of the mass and energy balances. The unknown coefficients in the mathematical model are determined by applying the stochastic approximation method. The identification scheme yields the optimal values of the parameters such that the best fit to the available discrete measurements is attained.

In this paper, we address the problem of identifying a semi-batch olive oil esterification reactor. In fact, this reactor can be considered as a PieceWise AutoRegressive eXogenous (PWARX) system. The Chiu's clustering procedure for the identification of PWARX systems is then applied. It consists in estimating both the parameter vector of each submodel and the coefficients of each partition. The results of the experimental validation illustrate the effectiveness of the proposed method. A comparative study with three existing approaches is also considered in this paper which shows that the proposed approach gives the best results in terms of precision.

Motivated by the difficulty of designing low-order controllers for large-scale plants consisting of numerous interconnected subsystems, this paper addresses the issue of quantifying the ν-gap metric between the plant and a lower-order identified model, using only plant frequency response data. The main result of this paper is the construction of a bound on the ν-gap metric between plant and model that exploits the convergence properties of Chebyshev polynomial interpolants of point-wise in frequency system graph symbols. This bound subsequently informs the design of low-order robust controllers synthesised from the identified model. The techniques developed in this paper are demonstrated upon a semi-discretised 1D heat equation.

This paper investigates the optimal estimation problem for networked control systems, where the control packets are randomly dropped without acknowledgement to the estimator. Most existing results for this setup are concerned with the design of controller, while the optimal estimation and its performance evaluation have not been fully studied. This paper shows, unlike many other cases such as intermittent observations or TCP-like systems, the system state follows a Gaussian mixture distribution with exponentially increasing terms. The optimal estimation is obtained by Gaussian sum filtering, while the computation is time- consuming. By constructing an auxiliary estimator, a fast and stable filtering algorithm is proposed to improve computational efficiency.

In this paper, we discuss the development of an Interior Point Method (IPM) solver for Second Order Cone Programming optimization problems that is capable of producing customized ANSI-C code for embedded, real-time applications. The customized code is generated for a given problem structure and makes use of no dynamic memory allocation, minimizes branching, wastes no mathematical or logical computations, and has minimal dependencies to standard libraries. The resulting software is designed to be easy to implement on embedded hardware with limited computing capabilities, while still providing accurate results rapidly enough for real-time use. The core IPM algorithm is a fairly standard primal-dual IPM, which makes use of Mehrotra predictor-corrector method with Nesterov-Todd scalings and Newton search directions. We make use of the Approximate Minimum Degree heuristic to maximize the sparsity of the Cholesky factorizations that are ultimately used to solve for the search directions. We conclude the paper by presenting the computational performance results from two example problems: a Mars landing optimal control problem and a reaction wheel allocation problem. The code generated for the Mars landing problem was successfully validated in three flights onboard a NASA test rocket, and was used in real-time to generate the optimal landing trajectories that guided the rocket. To the best of our knowledge, this was the first time that a real-time embedded convex optimization algorithm was used to control such a large vehicle, where mission success and safety critically relied on the real-time optimization algorithm.

This paper presents a classical technique based on co-ordination equation to solve Combined Economic and Emission Dispatch (CEED) problem, considering nonlinear characteristics of the generator, such as ramp rate limits and prohibited operating zones. The multi-objective CEED problem is converted into a single objective problem using a modified price penalty factor approach. In this paper, incremental cost is heuristically updated iteratively, which makes the solution independent of the number of generator units. The approach includes the evaluation of trade-off curve between the fuel cost and the emission according to the multi-objective function. The performance of the proposed method is demonstrated using modified IEEE 14 and 30 bus systems with different loading conditions.

This paper is concerned with collision-free vehicle formation control (FC) when the communication between vehicles is model by a graph. Unlike previous FC works (dealing with either non-trivial vehicle dynamics with no consideration of collision avoidance (CA) or trivial first-order vehicle dynamics with consideration of CA), this paper discusses non-trivial (second-order) vehicle FC with consideration of CA. This collision-free vehicle FC is done by manipulating entries of the graph Laplacian and by constructing a proper edge-tension function. Theoretical and numerical evidences are provided to show that the proposed control law effectively address both CA and FC.

This paper presents a modified Q-learning algorithm and provides conditions for convergence to a pure Nash equilibrium in potential games. In general Q-learning schemes, convergence to a Nash equilibrium may require decreasing step-sizes and long learning time. In this paper, we consider a modified Q-learning algorithm based on constant step-sizes, inspired by JSFP. When compared to JSFP, the Q-learning with constant step-sizes requires less information aggregation, but only reaches an approximation of a Nash equilibrium. We show that by appropriately choosing frequency dependent step-sizes, sufficient exploration of all actions is ensured and the estimated equilibrium approaches a Nash equilibrium.

This paper considers discrete-time, uncertain Piecewise Linear (PWL) systems affected by polytopic parameter variations. Classical robust controller for uncertain PWL systems is known to be a complex problem, where the on-line computation becomes computationally burdensome and inapplicable. In this paper we present a new technique to solve robust constrained infinite horizon model predictive control for uncertain PWL systems using Linear Matrix Inequalities (LMI). The controller objective is to regulate the system states to a constant set-point that could be in general different from the origin, despite the uncertainties. Constraints over the control and output signals are taken into account. The proposed controller guarantees the system stability and reduces the computation load. To further reduce the computation load, an algorithm to calculate off-line solutions based on the system state location is proposed; where a pre-computed state-feedback control law is applied once the system states enter the region of PWL system containing the shifted origin. This algorithm reduces considerably the computation time while offering a suboptimal solution. A numerical example to validate the efficiency of the developed techniques is presented.

In this paper, we considered a failure-prone manufacturing system composed by a single-product machine, a stock and a customer who demands a stochastic quantity of product. To describe the proposed manufacturing system, a discrete flow model is adopted and which takes into account machine failure, lost demands and machine degradation. The goal of this paper is to determine the optimal production planning taken into account service level by minimizing the sum of production, inventory, lost sales and degradation costs. Perturbation analysis method is applied to the discrete flow model for optimizing the proposed system. Then the trajectories of production rate, stock level, degradation rate, and lost demands are studied and the perturbation analysis estimators are determined. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm which determines the optimal production planning in the presence of service level.

Robust model predictive control algorithms often suffer from a high computational complexity due to the large number of variables and constraints involved in the optimiztion problems that are solved at every sampling instant. In this paper we propose an approximate control scheme for polytopic systems based on the interpolation of offline computed robust invariant tubes. The feasible set of the control scheme is the convex hull of all the sets in the invariant tubes. Online, the current control input is computed by interpolating between the control laws associated with these tubes. This interpolation requires the solution of an optimization problem. Compared with a direct solution of a robust model predictive control problem, the interpolation approach proposed in this paper requires much less computation time.

This paper describes a method to control the density distribution of a large number of autonomous agents. Our approach leverages from the fact that there are a large number of agents in the system, and hence the time evolution of the probabilistic density distribution of agents can be described as a Markov chain. Once this description is obtained, a Markov chain matrix is synthesized to drive the multi-agent system density to a desired steady-state density distribution, in a probabilistic sense, while satisfying some motion and conflict avoidance constraints. Later, we introduce an adaptive density control method based on real time density feedback to synthesize a time-varying Markov matrix, which leads to better convergence to the desired density distribution. This paper also introduces a decentralized density computation method, which guarantees that all agents will have a best, and common, density estimate in a finite, with an explicit bound, number of communication updates.

This paper presents a method to address the pursuit-evasion problem which incorporates the behaviors of the opponent, in which a continuous-time Markov decision process (CTMDP) model is introduced, where the significant difference from Markov decision process (MDP) is that the influence of the transition time between the states is taken into account. By introducing the concept of situation, the probabilities addressing average behaviors are obtained. Furthermore, these probabilities are introduced to construct the transition matrix in the CTMDP. A policy iteration method for solving the CTMDP is also given. To demonstrate the CTMDP method for pursuit-evasion, examples in a grid environment are computed. The CTMDP-based method presented in this paper offers a new approach to pursuit-evasion modeling and may be extended to similar problems in the sequential decision process.

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 this paper we propose a novel quadratic model predictive control technique that constrains the number of active inputs at each control horizon instant. This problem is known as sparse control. We use an iterative convex optimization procedure to solve the corresponding optimization problem subject to sparsity constraints defined by means of the l 0 -norm. We also derive a sufficient condition on the minimum number of active of inputs that guarantees the exponential stability of the closed-loop system. A simulation example illustrates the benefits of the control design method proposed in the paper.

A sparse parameter estimation method is proposed for identifying a stochastic monomolecular biochemical reaction network system. Identification of a reaction network can be achieved by estimating a sparse parameter matrix containing the reaction network structure and kinetics information. Stochastic dynamics of a biochemical reaction network system is usually modeled by a chemical master equation, which is composed of several ordinary differential equations describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular reaction systems for which an exact analytical solution of the corresponding chemical master equation is available. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized, whereas most of existing studies on sparse reaction network identification use deterministic models for regularized least-square estimation. A simulation result is provided to verify performance improvement of the presented regularized MLE over the least squares (LSE) based on a deterministic mass-average model in the case of a small population size. Improved reaction structure detection is achieved by adding a penalty term for l 1 regularization to the exact maximum likelihood function.

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 addresses the problem of state and parameter estimation for the class of affine systems in the state space representation. The method does not require a specific state representation of the system and consists of designing a switched observer that, under certain conditions given in the paper, allows for the state and parameter estimation errors to converge to zero. Assuming that the parameters to be estimated belong to a given polytope, the idea of the method is to recast the parameter estimation problem as a switching rule design for an auxiliary switched system whose matrices at the equilibrium correspond to the matrices of the system to be estimated. A guaranteed cost is used in the design and the switching rule is based on a max composition of a set of quadratic functions of the observation error. The method is simple and has low computational cost. The main disadvantage regards the amount of information that is needed to have both state and parameters estimated simultaneously. The case when there is no parameter to estimate the method reduces to a standard Luenberger observer with guaranteed cost.

In this paper, the minimization of an unknown but measurable cost function using extremum-seeking control is considered. The main contribution of the paper is to formulate the extremum-seeking problem as a time-varying estimation problem. The concepts of invariant manifold and adaptive parameter update law are used for adaptive estimation of the time-varying gradient of the unknown cost function. The proposed approach is shown to avoid the need for averaging analysis which minimizes the sensitivity of the closed-loop performance to the choice of dither signal.

This paper explains the methodology applied to make a mobile robot explore an unknown environment accurately, with minimum energy dissipations and more speedily. Essentially it focuses on optimization capability of Genetic Algorithms and their convergence property, and how it can be applied in the domain of path planning. Optimization of path planning by mobile robots in environments known and unknown is a hot area of research. This paper is essentially an improvement over a previous paper on target tracking using Direct Competition in terms of lesser energy utilization, better approach of conducting simulations and interpretation of results. Rigorous generation wise experiments actually make the controllers improve a lot from their sub-minimal competent nature thereby overcoming the Bootstrap Problem. Another key point of the research is the observation of behavior in second set of experiment using the evolved weights after the first experiment, how it affects the fitness and how far proves to be successful in achieving the objective.

We study a class of Markov Decision Processes (MDPs) under stationary constraints (particularly but not exclusively, chance constraints). Our model focuses on problems where the state space is finite but the control at each state may take real values. It is straightforward to formulate the problem as a constrained nonlinear optimization problem, but solving it requires either projection, penalty or gradient-based methods that may show slow convergence, particularly because this type of problems may not be strictly convex. In this paper we extend results that are known for the discrete control model and show that the solution of a binary randomized control problem is optimal under the assumption that the dependency on the control variable is linear. If the dependency is piecewise linear with several breakpoints, then the randomized problem is not equivalent to the original problem; however we show that a particular solution always exists that is also the solution to the original continuous problem. This special solution takes the form of a two-action control policy for each state. Solving each two-action randomized problem can be done in polynomial time by linear programming (LP). This leads to an efficient solution when the number of states is small, but for a large state space the complexity can be prohibitive. In a restricted yet interesting special case of piecewise linear, multiple actions setting where the actions are linearly ordered, we show that the problem can again be reduced to a single LP. To overcome the computational complexity in the general case, we propose an alternative approach that involves solving a sequence of linear programs modeling the stationary measure and the optimal solution concurrently. We conclude the paper by discussing future extensions of this framework to application settings where the state space is continuous as well.

Stable and efficient operation of a smart grid requires coordination of the actions of many independent decision makers or agents that must cooperate in globally balancing the demand for power to the partly unpredictable and uncontrollable supply. This paper proposes the use of the correlation coefficient between the renewable supply and the (controllable) demand, measured in a given region over a specified time scale, as a performance measure that is imposed on lower level control agents by a higher level controller in order to achieve coordination between the actions of these individual agents. Using elementary calculations for simple distribution networks this paper illustrates that the expensive peak value of power imported from the grid, and the distribution losses, depend linearly on this correlation coefficient. Operational behavior is improved way by achieving a high correlation coefficient. The correlation coefficient they achieve can be used as a measure for determining the rewards control agents should get for their contribution to the ancillary services. Achieving a high correlation coefficient clearly will contribute to the stability of the smart grid. In this paper we briefly discuss some requirements for implementing a coordination strategy based on the correlation coefficients. Broadcasting of signals from a top level in the control hierarchy to the lower layer controllers only is needed.

This paper proposes a procedure to compute Abstractions with the observer property (OP) for discrete event systems. The procedure is a generalisation of an algorithm proposed before by the authors, which is based on a quadratic algorithm to test whether a given projection has the observer property. The new version proposed in this paper supports systems that have cycles of non-relevant events, thus removing a restriction of the previous version. Nevertheless, it retains its cubic complexity, which means that the method is asymptotically faster than other methods proposed in the literature to solve the same problem.

International audience; This paper deals with the problem of disassembly line balancing where partial disassembly and uncertainty of task times are studied. Few papers have addressed the stochastic disassembly line balancing problem and most of existing work focused on complete disassembly and have not considered AND/OR graphs. In the present work, tasks of the best selected disassembly alternative are to be assigned to a fixed number of workstations while respecting precedence and cycle time constraints. Task times are assumed to be random variables with known probability distributions. An AND/OR graph is used to model the disassembly alternatives and the precedence relationships among tasks and subassemblies. The objective is to balance workstations’ idle times, i.e. differences among stations’ loads are as small as possible. A stochastic binary program is developed. To illustrate the applicability of the solution method proposed, it was performed on on a set of problem instances from the literature.

In previous papers by the use of the architecture generating property of the optimal linear quadratic tracking theory those problems that give the PI, PD and PID controllers had been presented, thus avoiding heuristics and giving a systematic approach to explanation for their excellent performance. This approach has been used also in derivation of the family of generalized PI m D n-1 controllers. In this paper by the combined use of the optimal linear quadratic tracking and system sensitivity theories a problem is formulated and solutions are shown that give a family of robust PID controllers. Examples of the control architectures and structures for first and second order systems are presented.

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.

This paper presents the adaptive optimal control of nonlinear inverted pendulum-cart dynamic system using policy iteration technique based adaptive critic scheme. Inverted pendulum is a highly nonlinear unstable system which is used as a benchmark for implementing the control methods. The optimal control design using Hamilton-Jacobi-Bellman (HJB) equation requires the complete knowledge of the system dynamics with an offline solution. Policy iteration technique which is based on actor-critic structure consists of two-step iteration: policy evaluation and policy improvement. The online policy iteration technique gives online continuous-time adaptive optimal control solution without using the complete knowledge of the system's internal dynamics. In this paper the state regulation control problem of cart position control and stabilization of inverted pendulum in vertically upright position is considered. The simulation results justify the effectiveness of the control scheme.

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.

This paper considers heuristics which can be helpful in reducing a workstations number in assembly line balancing problem. In the last sixty years a large variety of heuristics and exact solution procedures have been proposed to balance different structures of assembly lines. The author of this paper discussed some heuristics which almost lead to the reduction of the number of workstations (parallel single lines or u-line configuration) and productivity improvement. Special attention was given to the quality of final results. These are some measures of solution quality: line efficiency (LE), line time (LT) and smoothness index (SI) are calculated and compared. At the end final remarks are given.

Accurate uncertainty modeling is of key importance in high performance robust control design. The aim of this paper is to develop a new uncertainty modeling procedure that enhances the accuracy of the ℋ∞ norm. A frequency response based approach is adopted. The key novelty of this paper is a new method to address the intergrid error using local parametric modeling methods. These local polynomial and rational models enhance the estimates at the discrete frequency grid. Moreover, the presented methods are shown to enhance the intergrid error estimate. This is illustrated using simulations and experiments on an industrial active vibration isolation system. Compared to the local polynomial models, local rational models are able to handle lightly-damped resonances using far fewer data points.

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, a two-phase heuristic algorithm is proposed for an expanded crane scheduling problem that combines a Resource Constraint Project Problem and a Block World Problem in order to develop a guidance system for minimizing crane handlings in slab yards of steel works. Due to its NP hardness, it is difficult to obtain the theoretically optimal solution of the problem. Therefore, a two-phase heuristic algorithm was developed to obtain an approximate solution in a practical calculation time. The first phase in this approach utilizes a genetic algorithm that is employed to solve a relaxed scheduling problem of rearranging steel slabs in an approximate manner. Next the partial solution is iterated upon by a rule-based algorithm to obtain a feasible solution. Computational experiments are conducted with operation data of JFE Steel, allowing a comparison to be made between actual and theoretical crane handling operations. The resulting data shows that this paper's proposal can reduce the number of handlings by 30%.

In this paper, operational efficiency is defined in terms of how time efficient one field plot is to drive with agricultural machinery, as all turnings at headlands and other non-operational driving are considered to decrease the operational efficiency. The objective of this paper is to study the relationship between operational efficiency and field shapes. The objective is to find a computationally fast method in order to estimate or approximate operational efficiency; fast compared with the complex coverage path planning algorithms. The methodology is divided in to three phases: in the first phase, several shape describing indices are defined for a field plot, and the indices are calculated for a set of real field plots; in the second phase, a complex coverage path planning algorithm (split and merge) is used to calculate accurate solutions for the same set of fields. In the third phase, the correlation between indices and overhead obtained in the second phase is studied and the objective is to develop a model from the index space to the overhead space. Multivariate regression analysis shows correlation between field plot shape indices and operational efficiency, studied with real field plot shapes in southern Finland.

This paper focuses on loop layout problem in flexible manufacturing systems using one load and unload machine. The objective of this problem is to determine the ordering of machines around a loop, to minimize the total cost of transporting parts within each manufacturing cell. The novelty of this study lies on the reformulation of the problem while taking into account new variables generally neglected by recent researches like proximity constraints and machine dimensions. Hence, we aim to place these machines on a grid that represents the surface of the cell, in order to construct a loop layout while respecting proximity constraints. And as objective, we try to minimize the total cost of transporting parts within each manufacturing cell. This new formulation led us to propose a two-stage approach to solve this problem. The first step consists in positioning the machines on a grid while respecting the proximity constraints and machines dimensions. The second step aims to optimize the path betweense machines already positioned in order to minimize number of the loops travelled by parts. In this paper, we are interested in the second step. To solve this problem, we use genetic algorithms. This choice is motivated by the well-known of the efficiency of genetic algorithms to solve quadratic assignment problems. Hence, we proposed three hybrid genetic algorithms. The effectiveness of our approaches is demonstrated through numerical examples.

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.

This paper presents two methods for indirect regulation of cone crusher product-size distribution: Specific energy consumption (SEC)-based control and ratio control of selected product fractions. This simulation study evaluates the performance of the proposed methods against current control methods. The paper will also discuss in detail the implementation of the proposed methods and their suitability for different process layouts. The paper ends by summarizing the strengths and weaknesses of each control method and categorizes the methods according to application-specific suitability.

In this paper, we deal with the problem of pursuing a mobile target by multiple robots in indoor environments embedded with robotic networks. The target is vigilant and its speed can be arbitrarily fast while the speeds of pursuers are limited. Our object in this paper is to design effective pursuit strategies for the robots to track and finally capture the target. By using concepts of tree decomposition from graph theory, we establish an upper bound of the pursuer number that can guarantee successful capture of the mobile target. We then propose a pursuit algorithm, namely, tree-width based graph searching (TWGS), based on the theoretical analysis. Furthermore, we demonstrate the performance of the algorithm for two indoor environments by numerical simulations, which show that TWGS is efficient and the pursuer number given by the theoretical analysis is rather tight.

This paper focuses on the traffic signal control of urban transportation network, and presents an on-line optimal strategy by means of Model Predictive Control (MPC). To achieve this, firstly, the transportation network is modeled into a linear form with time-variant parameters. Then, for evaluating the system performance, this paper compares the transportation system with thermodynamic system, and introduces the entropy notion to measure the system disorder. Furthermore, to guarantee the robust stability of controller, the dissipativity theory is applied to address necessary conditions. By combining all these efforts into the framework of MPC, a traffic signal control strategy is presented to minimize the system disorder in finite horizons of time with respect to the constraints on both state and control. Finally, a network including four intersections is taken as an example to illustrate the results.

This paper investigates distributed leader-following swarm of heterogeneous multi-agent systems. Comparing with the existing works on leader-following swarm of homogeneous multi-agent systems, this paper is much more approaching the practical situation because the agents have different dynamics. We show that the heterogeneous followers will gather with a certain error lever under some assumptions and conditions. The stability properties have been proven by theoretical analysis and verified via numerical simulation. The stability of the heterogeneous multi-agent systems has been achieved based on matrix theory and the Lyapunov stability theorem. Numerical simulation is given to demonstrate the effectiveness of the theoretical result.

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.

This paper presents a new decomposition method for the approximate performance evaluation of buffered multi-stage production systems where machines are modeled as generally complex discrete time Markov chains with reward. The method is based on the exact solution of smaller two-machine sub-systems, also referred as building blocks, with machines that also feature such general characteristics. A decomposition approach is developed that propagates all the possible interruptions of flow due to starvation and blocking conditions throughout the pseudo-machines of each building block. In order to deal with such general settings, new decomposition equations are developed. A new algorithm is proposed for solving these decomposition equations. The proposed method proves to be very fast and accurate over a wide range of test cases, partly reported in this paper. To prove the generality of the framework, reported cases are focused on systems with generally distributed up and down times and systems with degrading machines. This method paves the way to the analysis of a wider class of previously un-investigated systems.

In this paper, the optimization of a decentralized task assignment strategy for heterogeneous UAVs in a probabilistic engagement scenario is investigated. In the engagement scenario, each UAV selects its targets by employing the consensus-based bundle algorithm (CBBA). This paper uses a scoring matrix to reflect heterogeneity among the UAVs and targets with different capabilities. Therefore, a performance improvement of CBBA is closely connected with the scoring matrix and it should be optimally selected. The values of scoring matrix can be obtained by employing an episodic parameter optimization (EPO). The EPO algorithm is performed during the numerous repeated simulation runs of the engagement and the reward of each episode is updated using reinforcement learning. The candidate scoring matrices are selected by using particle swarm optimization. The optimization results show that the team survivability of the UAVs is increased after performing the EPO algorithm and the values of the optimized score matrix are also optimally selected.

This paper considers idle times in two-sided assembly line structure. In the last two decades a large variety of heuristics and exact solutions procedures have been proposed to balance two-sided assembly line. Some measures of solution quality have appeared in line balancing literature: balance delay (BD), line efficiency (LE), line time (LT) and smoothness index (SI). These measures are very important for estimation the balance solution quality. Author of this paper modified and discussed the line time and smoothness index for two-sided assembly line. Special attention was given to idle times in discussed problem. A new measure of delay times is considered and at the end final remarks are presented.

In this paper, a manufacturing system composed by a single-product machine, a buffer and a stochastic demand is considered. A discrete flow model is adopted to describe the system and to take into account the lost demands. The objective of this paper is to determine the optimal production planning taken into account service level. This optimal production planning minimizing the sum of production, inventory, and lost sales costs. Perturbation analysis method is used for optimizing the proposed system. Using the discrete flow model, the trajectories of production rate, buffer level and lost demands are studied and the perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm which determines the optimal production planning in the presence of service level.

This paper studies the effect of short data lengths in system identification. It addresses the question of the minimum required data length that is needed in order to apply the asymptotic results. In this paper, an initial analysis is made in a time domain setting and limited to the simple class of FIR models. The main contribution of the paper is the definition and quantification of the ‘short data set’ loss on the variance of the estimates. A precise description of the theoretical setting is given, and insight is provided in the underlying mechanism that causes the efficiency loss.