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This paper focuses on a challenging problem in the internal model control (IMC) strategy: the model inversion to design the IMC controller for non-square systems. Several existing approaches for the synthesis of a specific inversion of the identified model will be presented in this paper to deal with the differences between the system's inputs and outputs numbers. The non-square effective relative gain is firstly presented. It consists of the measurement of interactions between the loops of the system in order to square the system and make it invertible. The equivalent transfer function method is presented as well. It is based on tuning the pseudo-inverse of the process to design the internal model controller. These methods are then compared with a novel proposed model inversion approach based on virtual outputs method. Virtual adding is considered in order to obtain an invertible square transfer matrix to design the internal model controller. This simple yet effective method ensures robust control performance. Its efficiency and availability, as compared with other presented methods, is illustrated through simulations on an overactuated system with three inputs/two outputs. [ABSTRACT FROM AUTHOR]

In spite of the wide improvements in computer simulation packages, many complex simulation models, particularly under uncertainty, may be inefficient to run in terms of time, computation, and resources. To address such a challenge, integrating metamodels and robust design optimization has been applied. In the current paper, a systematic comparative study is implemented to evaluate the performance of three common metamodels, namely polynomial regression, kriging, and radial basis function. The required experiments are designed by different space-filling methods including the orthogonal array design and three forms of Latin hypercube sampling such as randomized, maximin, and correlation approaches. Although, the impact of sample size on the performance of metamodels in robust optimization results are investigated. All methods are analyzed using five two-dimensional test problems and one engineering problem while all of them are considered in two forms that are expensive (with a small sample size) and semi-expensive (with a large sample size). Uncertainty is assumed in all problems as a source of variability, so all test problems are conducted in the format of robust optimization in the class of dual response surface in order to estimate robust Pareto frontier. The performances of methods are studied in two terms of accuracy and robustness. Finally, the results of comparison, an applicable guideline is provided to aid the practitioners in selecting the appropriate combination of metamodels and sampling design methods for investigating set of robust optimal points (estimated Pareto frontier) in simulation–optimization problems under uncertainty. [ABSTRACT FROM AUTHOR]

This paper explores the social dynamics of so-called intercomparison projects. An intercomparison project is a type of collaborative project that takes place in a number of simulation-based research areas such as astrophysics and climate modelling. Intercomparison projects can be seen as form of metrological practice in which the participants compare the results of numerical simulations of the ‘same’ scientific problem in order to ensure their reliability and validity. The paper is based on case studies of astrophysics, meteorology and oceanography, and the focus is on the organization and coordination of intercomparison projects. I argue that such projects have the effect of defining which scientists work on a particular problem and that they also serve as organizational vehicles for creating and presenting a dominant view of — and a standard result for — that problem. These types of projects are important for understanding numerical simulation-based research, because they show that expectations about desirable results are generated within the group. [ABSTRACT FROM PUBLISHER]

With the rapid improvement of hardware, the constant evaluation of simulation development methodologies and environments has brought us a new challenge: simulations modernization. This paper presents a simulation knowledge discovery metamodel that extends the Object Management Group’s knowledge discovery metamodel by adding a simulation model package for enabling architecture-driven simulation modernization. While there are some endeavors that propose integration methods, modernization of legacy simulations has not been investigated. Architecture-driven modernization has been introduced as a process of comprehending and transforming existing software assets. It advocates a model-based approach to software modernization in which the knowledge extracted from software assets is captured in models that conform to a metamodel, namely the knowledge discovery metamodel. It specifies an ontology of software assets. Model transformations are then recommended as means of modernization of legacy assets. But diversity in methodologies and approaches to specify simulation modeling assets prohibits the Knowledge Discovery Metamodel from providing adequate meta definitions to capture knowledge in simulations. System Entity Structure has long been used for knowledge representation by the simulation community. It provides formalism for composition, taxonomy and coupling relations. Hence, System Entity Structure is proposed as an intermediate metamodel to capture the meta-constructs and their relations. It is then made available to define particular metamodels for particular simulation modeling methodologies or approaches. The promoted methodology is exercised with samples in order to collect evidence of its applicability. Metamodel segments are presented for knowledge discovery for discrete event systems specification and Modelica simulation programming language. A complete metamodel is then introduced for the real-time distributed simulation domain model of the German Aerospace Center (DLR) Institute of Flight Systems. Finally, this metamodel is used to introduce discovery artifacts. These samples provide valuable indications that the methodology works. [ABSTRACT FROM AUTHOR]

The guidance law design for the missile–target interception problem in the presence of system uncertainty is investigated in this paper. To improve the disturbance rejection property, two kinds of non-smooth guidance laws based on a finite-time control technique are developed. The first is a high-gain finite-time guidance law, which can be derived by assuming that the system uncertainty is bounded by a constant. The second one is a composite guidance law including a disturbance observer and a finite-time state feedback, where the disturbance observer is introduced to estimate the system uncertainty and the finite-time state feedback is to stabilize the guidance system. Under both of the proposed guidance laws, it can be proved that the line-of-sight angle rate will converge to the origin or a small region of the origin in a finite time. Simulation comparisons show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a feedforward-plus-sliding mode controller is proposed. The combination uses a feedforward controller to achieve predefined process output and the sliding mode controller is combined to assure the robustness despite uncertainty, nonlinear dynamics, and external disturbances. The effectiveness of proposed method is shown by simulations on nonlinear models of a coupled tanks system and then validated by performing a real-time experiment on the system. The studies show the improved setpoint tracking of the proposed controller compared with the classical PI controller. [ABSTRACT FROM AUTHOR]

This paper describes a model capable of simulating large-scale traffic in an urban environment. The goal is of this work is the realistic and detailed simulation of the traffic, reproducing the behavior of each vehicle involved in the environment individually. This model has been developed in order to be integrated in an immersive driving simulator, where the driving position is the center of the simulation and the traffic model reproduces what happens around them. The general behavior of the traffic model is based on the following theory. Depending on the size of the urban environment to be simulated, the number of vehicles involved, and the traffic density, the environment can be studied as a whole or segmented in adjacent areas. Each vehicle model has two components. First, the behavior of the vehicle is simulated individually, modeling acceleration and braking, depending on the type and characteristics of each vehicle (mass, power, size, etc.). Second, the behavior of the drivers is also modeled, by type (passive, moderate, aggressive), playing various maneuvers also common in urban traffic circulation, such as lane changes, behavior at crossings and intersections, etc. A traffic light regulation model and the complete signposting of the urban environment are also included. As a result, the developed traffic model is applicable to large-scale traffic simulation integrated in an immersive driving simulator and is very useful when investigating complex behaviors of these environments. The model has been validated comparing it with results obtained from various references and very satisfactory results have been obtained. [ABSTRACT FROM AUTHOR]

This paper presents an agent-based model to analyse behaviour produced under noisy and deceptive information conditions. A simple yet powerful simulation environment is developed where adaptive agents act and adapt to varying levels of information quality that they sense about their environment. The simulation environment consists of two types of agents moving in a bounded two-dimensional continuous plane: a neuro-evolutionary learning agent that adapts its manoeuvreing strategies to escape a pre-programmed deceptive agent; and a pre-programmed agent, whose goal is to capture the adaptive agent, that acts on noisy information about the adaptive agent’s manoeuvres that it senses from the environment. The pre-programmed agent is also able to produce deceptive actions to confuse the adaptive agent. The behaviour is represented in terms of the manoeuvreing strategies that the agents adopt as their actions to the environmental changes. A behaviour analysis methodology is developed to compare agent actions under different information conditions, that elicits interesting relationships between behaviour and the studied information conditions. The framework is easily extendable to analyse human behaviour in similar environments by replacing the adaptive agent with an interactive human–machine interface. [ABSTRACT FROM AUTHOR]

Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients. Inverse gamma prior distributions are placed on the penalty parameters. We treat the hyperparameters of the inverse gamma prior as unknowns and estimate them along with the other parameters. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of a prostate cancer dataset, we compare the performance of the BALQR method proposed with six existing Bayesian and non-Bayesian methods. The simulation studies and the prostate cancer data analysis indicate that the BALQR method performs well in comparison to the other approaches. [ABSTRACT FROM AUTHOR]

When a global position system (GPS) is combined with an inertial navigation system (INS) in the ultra-tight GPS/INS integrated navigation system, their performances are both optimized. However, different from loose and tight integration, in the ultra-tight integration, the non-linearity of Kalman measurements will seriously affect the filtering result. As the performance of the Kalman filter is radically affected by the modelling strategies, this paper will discuss it in depth. Firstly, the modelling of a traditional Kalman filter for ultra-tight integration is introduced by derivation and simulation experiments based on a single-frequency GPS software receiver. Then, the theoretical analysis of its performances on observability, controllability and stability is given. According to that, a novel extended Kalman filter for the ultra-tight integration is presented, using a Q (quadrature) signal from the GPS receiver correlator and a position and velocity from both the GPS and INS to form the measurements of the extended Kalman filter. New simulation experiments under the same initial conditions are carried out, and the comparison results show that the new method can provide better precision of all navigation parameters. However, it is noted that the curves of position and velocity errors have slight oscillation; the reason for this will be studied in the future. [ABSTRACT FROM PUBLISHER]

This paper reports an experiment that demonstrates the superiority of groups over the best individuals using various instructions for strategies to solve letters-to-numbers problems. Simulations provide baseline statistics to compare performances. Letters-to-numbers problems require identification of the random coding of the ten letters A, B, C, D, E, F, G, H, I, J to the ten numbers 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 by (a) proposing an expression in letters ( A + B ); (b) receiving the value of the expression in letters ( A + B = DG); (c) proposing codings of the numbers for the letters; and (d) receiving feedback on the correctness of the proposed codings, on each trial. Three-person groups and individuals solved the problems under five instruction conditions to use different strategies. As predicted, the groups generally had fewer trials to solution than the best of an equivalent number of individuals, and the best performance was achieved with the most information-rich instruction, to add all ten letters. [ABSTRACT FROM PUBLISHER]