model predictive controller is widely used in industrial plants. uncertainty is one of the critical issues in real systems. in this paper, the direct adaptive simplified model predictive control (smpc) is proposed for unknown or time varying plants with uncertainties. by estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...

This chapter is a continuation of our chapters “Simulation Optimization Via Metamodeling Approach” and “Simulation Optimization For Finite Parameter Space” found elsewhere in this volume. In this chapter, we investigate gradientbased simulation optimization (GBSO) methods, including Perturbation Analysis (PA), Likelihood Ratio (LR), Finite Difference (FD), and Frequency Domain Analysis (FDA). W...

in this paper, a new robust controller based on geometric homogeneity and adaptive integral sliding mode is proposed for a class of second order systems. the upper bound of the system disturbances is not required. fully unknown parameters have been considered in the described model and its finite–time convergence to zero equilibrium point is proved. moreover, the controller is developed in the ...

We propose an optimization-via-simulation algorithm, called COMPASS, for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables are integer ordered. We prove that COMPASS converges to the set of local optimal solutions with probability 1 for both terminating and steady-state simulation, and for both fully constrained problems and pa...

VapCyc is a vapor compression refrigeration simulation tool which features charge estimates, component inter-changeability and is specifically geared towards optimization of system level variables using gradient based and genetic optimization routines. System level variables currently accounted for are: system refrigerant charge, COP, weight, capacity, cost and selection of individual component...

Bayesian optimization methods are often used to optimize unknown functions that are costly to evaluate. Typically, these methods sequentially select inputs to be evaluated one at a time based on a posterior over the unknown function that is updated after each evaluation. In many applications, however, it is desirable to perform multiple evaluations in parallel, which requires selecting batches ...

This paper extends the application of Subset Simulation (SS), an advanced Monte Carlo algorithm for reliability analysis, to solve constrained optimization problems encountered in engineering. The proposed algorithm is based on the idea that an extreme event (optimization problem) can be considered as a rare event (reliability problem). The Subset Simulation algorithm for optimization is a popu...