The tuning of Linear Quadratic regulator (LQR) controllers is a challenge for researchers and plant operators. This paper presents a optimization and comparison of time response specification between Traditional ZN Tuning & Modified ZN Tuning controllers with Linear Quadratic Regulator (LQR) for a speed control of a separately excited DC motor. The goal is to determine which control strategy de...

although many methods exist for intensity modulated radiotherapy (imrt) fluence map optimization for crisp data, based on clinical practice, some of the involved parameters are fuzzy. in this paper, the best fluence maps for an imrt procedure were identifed as a solution of an optimization problem with a quadratic objective function, where the prescribed target dose vector was fuzzy. first, a d...

this study concerns with a trust-region-based method for solving unconstrained optimization problems. the approach takes the advantages of the compact limited memory bfgs updating formula together with an appropriate adaptive radius strategy. in our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-newt...

ch4, c2h6 and c2h4 are the most important outlet gaseous of oxidative couple methane (ocm) reaction and this process is a new technology for conversion of natural gas to ethane and ethylene products. in this study, adsorption of ocm outlet hydrocarbons over 10x zeolite has been examined at equilibrium conditions. temperature and pressure are the most effective operational parameters in the batc...

Vapnik et al. recently introduced a new learning paradigm called Learning Using Privileged Information (LUPI). In this paradigm, along with standard training data, the teacher provides the student privileged (additional) information, yet not available at test time. The paradigm is realized by implementation of SVM+ algorithm. In this report, we give the proof of the SVM+ algorithm and show impl...

Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trust-region feasibility-perturbed sequential quadratic programming algorithm (developed in a companion report), then discuss its adaptation to the problems arising in nonlinear model predictive control. Computation...

In this paper, we aim at minimizing the actuator torques of robots working on production lines by adding to the mechanism dynamic equilibrators based on nonlinear springs, that work in parallel with the joints. We propose a method to simultaneously optimize the trajectory of the robot and the force profiles of the nonlinear springs to minimize the actuator torques. First, we express the traject...

We establish alternative theorems for quadratic inequality systems. Consequently, we obtain Lagrange multiplier characterizations of global optimality for classes of non-convex quadratic optimization problems. We present a generalization of Dine’s theorem to a system of two homogeneous quadratic functions with a regular cone. The class of regular cones are cones K for which (K∪−K) is a subspace...

We consider the problem of approximating the global maximum of a quadratic program (QP) subject to convex non-homogeneous quadratic constraints. We prove an approximation quality bound that is related to a condition number of the convex feasible set; and it is the currently best for approximating certain problems, such as quadratic optimization over the assignment polytope, according to the bes...

This paper considers the problem of designing a state feedback control law which minimizes multiple quadratic performance objectives in the presence of uncertain linear plant dynamics Vec tor optimization techniques are used to minimize upper bounds on each performance objective guaranteed cost By exploiting the linear uncertainty bound introduced by Bernstein the vector optimization problem is...