Prediction error method for identification of LPV models

This paper is concerned with identification of linear parameter varying (LPV) systems in an input-output setting with Box-Jenkins (BJ) model structure. Classical linear time invariant prediction error method (PEM) is extended to the LPV PEM. Under the new LPV framework, identification of two types of input-output LPV models is considered: one is based on parameter interpolation and the other is based on model interpolation. The effectiveness of the proposed solution is validated by comparison with other existing LPV identification approaches through simulation examples and demonstrated by experiment studies.     

Refined instrumental variable methods for identification of LPV Box-Jenkins models

The identification of linear parameter-varying systems in an input-output setting is investigated, focusing on the case when the noise part of the data generating system is an additive colored noise. In the Box-Jenkins and output-error cases, it is shown that the currently available linear regression and instrumental variable methods from the literature are far from being optimal in terms of bias and variance of the estimates. To overcome the underlying problems, a refined instrumental variable method is introduced. The proposed approach is compared to the existing methods via a representative simulation example.     

Fault detection using interval LPV models in an open-flow canal

This paper proposes a new method of fault detection using Linear Parameter Varying (LPV) interval models and its application to an open-flow canal. The use of such models is motivated because the parameters and transport delay in the canal transfer function model vary with the operating point. LPV models allow to consider these variations by characterizing the parameters/delay variation law with the operating point while intervals are used to bound the parameter/delay uncertainty. Additionally, a LPV parameter estimation algorithm that allows to estimate parameter/delay uncertainty intervals is also proposed. As an application case study, an open-flow canal system based on the Lunax dam-gallery system located in France is used to show the effectiveness of the proposed method to detect faults. The satisfactory results obtained allow to assess the effectiveness of the proposed approach.     

Unbounded parallel-batching scheduling with two competitive agents

We consider the scheduling problems arising when two agents, each with a family of jobs, compete to perform their respective jobs on a common unbounded parallel-batching machine. The batching machine can process any number of jobs simultaneously in a batch. The processing time of a batch is equal to the maximum processing time of the jobs in the batch. Two main categories of batch processing based on the compatibility of job families or agents are distinguished. In the case where job families are incompatible, jobs from different families cannot be placed in the same processing batch while all jobs can be placed in the same processing batch when job families are compatible. The goal is to find a schedule for all jobs of the two agents that minimizes the objective of one agent while keeping the objective of the other agent below or at a fixed value Q. Polynomial-time and pseudo-polynomial-time algorithms are provided to solve various combinations of regular objective functions for the scenario in which job families are either incompatible or compatible.     

Bounded parallel-batching scheduling with two competing agents

We consider a scheduling problem in which two agents, each with a set of non-preemptive jobs, compete to perform their jobs on a common bounded parallel-batching machine. Each of the agents wants to minimize an objective function that depends on the completion times of its own jobs. The goal is to schedule the jobs such that the overall schedule performs well with respect to the objective functions of both agents. We focus on minimizing the makespan or the total completion time of one agent, subject to an upper bound on the makespan of the other agent. We distinguish two categories of batch processing according to the compatibility of the agents. In the case where the agents are incompatible, their jobs cannot be processed in the same batch, whereas all the jobs can be processed in the same batch when the agents are compatible. We show that the makespan problem can be solved in polynomial time for the incompatible case and is NP-hard in the ordinary sense for the compatible case. Furthermore, we show that the latter admits a fully polynomial-time approximation scheme. We prove that the total completion time problem is NP-hard and is polynomially solvable for the incompatible case with a fixed number of job types.     

Optimal scheduling of two competing queues with blocking

Consideration is given to the problem of optimal scheduling for two queues for service at a single station, where the queues have finite capacities and the service rate is class-dependent. The cost structure is linear in the number of holding customers in the queues, combined with blocking costs incurred whenever arrivals encounter a full queue. The authors derive the optimal policy minimizing this criterion when the blocking cost is larger than the holding cost for each queue. Then they show that the optimal policy is of the switching type. Numerical results are included to support the analytical findings     

Optimal LPV control with hard constraints

This paper considers the optimal control of polytopic, discrete-time linear parameter varying (LPV) systems with a guaranteed ?₂ to ?_∞ gain. Additionally, to guarantee robust stability of the closed-loop system under parameter variations, H _∞ performance criterion is also considered as well. Controllers with a guaranteed ?₂ to ?_∞ gain and a guaranteed H _∞ performance (?₂ to ?₂ gain) are a special family of mixed H ₂=H _∞ controllers. Normally, H₂ controllers are obtained by considering a quadratic cost function that balances the output performance with the control input needed to achieve that performance. However, to obtain an optimal controller with a guaranteed ?₂ to ?_∞ gain (closely related to the physical performance constraint), the cost function used in the H₂ control synthesis minimizes the control input subject to maximal singular-value performance constraints on the output. This problem can be efficiently solved by a convex optimization with linear matrix inequality (LMI) constraints. The main contribution of this paper is the characterization of the control synthesis LMIs used to obtain an LPV controller with a guaranteed ?₂ to ?_∞ gain and >H _∞ performance. A numerical example is presented to demonstrate the effectiveness of the convex optimization.     

Multi-queue scheduling of two tasks

Summary Scheduling in the central server queueing model, consisting of a CPU server and m I/O servers, is considered for the case of two customers. Optimal (maximal CPU utilization) CPU and I/O schedules are obtained. The best CPU schedule depends on the I/O schedule in effect; and is either Longest or Shortest-Expected-Remaining-Processing-Time-First. However, for certain I/O schedules the CPU schedule is immaterial. The best I/O schedule is always to process the longer CPU customer first.     

Estimation of global sensitivity indices for models with dependent variables

A novel approach for estimation variance-based sensitivity indices for models with dependent variables is presented. Both the first order and total sensitivity indices are derived as generalizations of Sobol? sensitivity indices. Formulas and Monte Carlo numerical estimates similar to Sobol? formulas are derived. A copula-based approach is proposed for sampling from arbitrary multivariate probability distributions. A good agreement between analytical and numerical values of the first order and total indices for considered test cases is obtained. The behavior of sensitivity indices depends on the relative predominance of interactions and correlations. The method is shown to be efficient and general.     

Domain-dependent distributed models for railway scheduling

Many combinatorial problems can be modelled as Constraint Satisfaction Problems (CSPs). Solving a general CSP is known to be NP-complete, so closure and heuristic search are usually used. However, many problems are inherently distributed and the problem complexity can be reduced by dividing the problem into a set of subproblems. Nevertheless, general distributed techniques are not always appropriate to distribute real-life problems. In this work, we model the railway scheduling problem by means of domain-dependent distributed constraint models, and we show that these models maintained better behaviors than general distributed models based on graph partitioning. The evaluation is focused on the railway scheduling problem, where domain-dependent models carry out a problem distribution by means of trains and contiguous sets of stations.     

Joint order and parameter estimation of multivariate autoregressive models using multi-model partitioning theory

In this paper the multi-model partitioning theory is used for simultaneous order and parameter estimation of multivariate autoregressive models. Simulation experiments show that the proposed method successfully selects the correct model order and estimates the parameters accurately, in very few steps, even with a small sample size. They also show that the proposed method performs equally well when the complexity of the model is increased. The results are compared to those obtained using well-established order selection criteria. Finally, it is shown that the method is also successful in tracking model order changes, in real time.     

Robust optimization models for project scheduling with resource availability cost

We address a project scheduling problem with resource availability cost for which the activity durations are uncertain. The problem is formulated within the robust optimization framework, where uncertainty is modeled via a set of scenarios. The proposed solution method is based on the scatter search methodology and employs advanced strategies, such as dynamic updating of the reference set, a frequency-based memory mechanism, and path relinking. A multistart heuristic was also developed and comparative results are reported. The tradeoffs for risk-averse decision makers are discussed.     

Identification of ARX models with markovian parameters

Many different recursive identification methods for time-varying systems have been suggested in the literature. An assumption that the variations in the system parameters are slow is common for all the methods. When using the methods on systems with faster variations, one is forced to compromise between alertness to parameter variations on one hand and noise sensitivity on the other. The topic of this paper is to investigate if this compromise can be avoided for a special class of systems. The systems considered are such that their dynamic changes between some different typical modes. The philosophy behind the approach taken in the paper is to separate the observations into different sets corresponding to the different modes. The parameters of the different modes can then be estimated using the separated data sets. Technically, this parallel modelling is achieved by describing the system parameters as the realizations of a Markov chain. A parameter-identification algorithm for time-varying ARX models is then given in the paper. The behaviour of the algorithm is then investigated using simulations and some analysis. The analysis and the simulations show that a major problem is the initialization of the algorithm. Based on the analysis, modifications are made to the algorithm that improve the convergence properties.     

Disturbance Attenuation of LPV Systems with Bounded Inputs

A design technique is proposed for disturbance attenuation in linear parameter varying (LPV) systems with bounded inputs. The actuator constraints are handled via a typical LPV approach, in which the time varying parameter is related to the error between the command input and the actual input. Since these two signals are known in most applications, a standard LPV structure for the controller is possible. As a result, the design of controllers with bounded actuators is no more difficult – fundamentally – than the unconstrained control of the LPV system. For example, state feedback problem is convex and the output feedback problem is convex if all of the parameters are available on-line. Cases where the parameters are not available on-line (i.e., traditional robust problems), rate constraint or when the underlying Lyapunov matrix is itself parameter varying are also discussed.     

Point estimate-based importance analysis for structural models with correlated variables

Importance analysis is conducted to find the contributions of the inputs to the output uncertainty. In this work, a point estimate-based importance analysis algorithm is established for models involving correlated input variables, and the variance contribution by an individual correlated input variable is decomposed into correlated contribution and uncorrelated contribution. In the established algorithm, the correlated variables are orthogonalised to generate corresponding independent variables, and the performance function is reconstructed in the independence space. Then, the point estimate is employed to compute the variance-based importance measures in the independence space, by which the variance contribution of the original correlated variables, including the correlated part and uncorrelated part, can be obtained. Different point estimate methods can be employed in the proposed algorithm; thus, the algorithm is adaptable and improvable. The proposed algorithm avoids the sampling procedure, which usually consumes a heavy computational cost. Discussion of numerical and engineering examples in this work has demonstrated that the proposed algorithm provides an effective tool to deal with uncertainty analysis involving correlated inputs.     

Parameter identification for models with bounded errors in all variables

In this paper, the problem of parameter identification for models with bounded measurement errors both on the input and on the output is addressed and some corrections to previously published results are presented. In particular, it is shown that only parameter overbounds can in general be computed for systems of the form y = (φ + δφ)θ + δy when the bounded measurement errors δφ and δy are correlated. Since ARMAX and bilinear systems can be represented in this form, it turns out that tight parameter bounds are in general not available for these systems. Finally, we show that it is possible to check a posteriori whether the obtained bounds are tight or not.