Model predictive control (MPC) is a very popular controller design method in the process industry. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. Usually MPC uses linear or nonlinear discrete-time models. In this paper we give an overview of some results in connection with model predictive control (MPC) approaches for some tractable classes of discrete-...

In this paper we present pddl+, a planning domain description language for modelling mixed discrete-continuous planning domains. We describe the syntax and modelling style of pddl+, showing that the language makes convenient the modelling of complex timedependent effects. We provide a formal semantics for pddl+ by mapping planning instances into constructs of hybrid automata. Using the syntax o...

The Cox (1972) regression model is extended to include discrete and mixed continuous/discrete failure time data by retaining the multiplicative hazard rate form of the absolutely continuous model. Application of martingale arguments to the regression parameter estimating function show the Breslow(1974) estimator to be consistent and asymptotically Gaussian under this model. A computationally co...

Mixed discrete-continuous systems are hybrid systems that exhibit both discrete changes of state, describable in terms of their logical and metric properties, and continuous numeric change describable in terms of differential equations. Continuous change occurs within a state as a consequence of one or more continuous processes being active in that state, whilst discrete change results in state...

Many today’s engineering tasks use approximation of their expensive objective function. Surrogate models, which are frequently used for this purpose, can save significant costs by substituting some of the experimental evaluations or simulations needed to achieve an optimal or near-optimal solution. This paper presents a surrogate model based on RBF networks. In contrast to the most of the surro...

Copulas allow a flexible and simultaneous modeling of complicated dependence structures together with various marginal distributions. Especially if the density function can be represented as product functions copula function, this leads to both an intuitive interpretation conditional distribution convenient estimation procedures. However, is no longer case for models mixed discrete continuous d...

We present a rigorous, component-based analysis of six widespread metaphor-based algorithms for tackling continuous optimization problems. In addition to deconstructing the into their components and relating them with equivalent proposed in well-established techniques, such as particle swarm evolutionary algorithms, we analyze use metaphors that inspired these understand whether usage has broug...