The Experts below are selected from a list of 9136485 Experts worldwide ranked by ideXlab platform
Alireza Behbahani - One of the best experts on this subject based on the ideXlab platform.
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gain scheduling control of gas turbine engines stability by computing a single quadratic lyapunov function
Volume 4: Ceramics; Concentrating Solar Power Plants; Controls Diagnostics and Instrumentation; Education; Electric Power; Fans and Blowers, 2013Co-Authors: Mehrdad Pakmehr, Nathan Fitzgerald, Jeff S. Shamma, Eric Feron, Alireza BehbahaniAbstract:We develop and describe a stable gain scheduling controller for a gas turbine engine that drives a variable pitch propeller. A stability proof is developed for gain scheduled closed-loop system using global Linearization and linear matrix inequality (LMI) techniques. Using convex optimization tools, a single quadratic Lyapunov function is computed for multiple Linearizations near equilibrium and non-equilibrium points of the nonlinear closed-loop system. This approach guarantees stability of the closed-loop gas turbine engine system. Simulation results show the developed gain scheduling controller is capable of regulating a turboshaft engine for large thrust commands in a stable fashion with proper tracking performance.Copyright © 2013 by ASME
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Gain Scheduled Control of Gas Turbine Engines: Stability and Verification
Journal of Engineering for Gas Turbines and Power, 2013Co-Authors: Mehrdad Pakmehr, Nathan Fitzgerald, Eric M. Feron, Jeff S. Shamma, Alireza BehbahaniAbstract:A stable gain scheduled controller for a gas turbine engine that drives a variable pitch propeller is developed and described. A stability proof is developed for gain scheduled closed-loop system using global Linearization and linear matrix inequality (LMI) techniques. Using convex optimization tools, a single quadratic Lyapunov function is computed for multiple Linearizations near equilibrium and nonequilibrium points of the nonlinear closed-loop system. This approach guarantees stability of the closed-loop gas turbine engine system. To verify the stability of the closed-loop system on-line, an optimization problem is proposed, which is solvable using convex optimization tools. Simulation results show that the developed gain scheduled controller is capable to regulate a turboshaft engine for large thrust commands in a stable fashion with proper tracking performance.
Matthias Althoff - One of the best experts on this subject based on the ideXlab platform.
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reachability analysis of nonlinear systems using conservative polynomialization and non convex sets
International Conference on Hybrid Systems: computation and control, 2013Co-Authors: Matthias AlthoffAbstract:A new technique for computing the reachable set of hybrid systems with nonlinear continuous dynamics is presented. Previous work showed that abstracting the nonlinear continuous dynamics to linear differential inclusions results in a scalable approach for reachability analysis. However, when the abstraction becomes inaccurate, Linearization techniques require splitting of reachable sets, resulting in an exponential growth of required Linearizations. In this work, the nonlinearity of the dynamics is more accurately abstracted to polynomial difference inclusions. As a consequence, it is no longer guaranteed that reachable sets of consecutive time steps are mapped to convex sets as typically used in previous works. Thus, a non-convex set representation is developed in order to better capture the nonlinear dynamics, requiring no or much less splitting. The new approach has polynomial complexity with respect to the number of continuous state variables when splitting can be avoided and is thus promising when a Linearization technique requires splitting for the same problem. The benefits are presented by numerical examples.
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Reachability analysis of nonlinear systems with uncertain parameters using conservative Linearization
2008 47th IEEE Conference on Decision and Control, 2008Co-Authors: Matthias Althoff, Olaf Stursberg, Martin BussAbstract:Given an initial set of a nonlinear system with uncertain parameters and inputs, the set of states that can possibly be reached is computed. The approach is based on local Linearizations of the nonlinear system, while Linearization errors are considered by Lagrange remainders. These errors are added as uncertain inputs, such that the reachable set of the locally linearized system encloses the one of the original system. The Linearization error is controlled by splitting of reachable sets. Reachable sets are represented by zonotopes, allowing an efficient computation in relatively high-dimensional space.
Martin Buss - One of the best experts on this subject based on the ideXlab platform.
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Reachability analysis of nonlinear systems with uncertain parameters using conservative Linearization
2008 47th IEEE Conference on Decision and Control, 2008Co-Authors: Matthias Althoff, Olaf Stursberg, Martin BussAbstract:Given an initial set of a nonlinear system with uncertain parameters and inputs, the set of states that can possibly be reached is computed. The approach is based on local Linearizations of the nonlinear system, while Linearization errors are considered by Lagrange remainders. These errors are added as uncertain inputs, such that the reachable set of the locally linearized system encloses the one of the original system. The Linearization error is controlled by splitting of reachable sets. Reachable sets are represented by zonotopes, allowing an efficient computation in relatively high-dimensional space.
Mehrdad Pakmehr - One of the best experts on this subject based on the ideXlab platform.
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gain scheduling control of gas turbine engines stability by computing a single quadratic lyapunov function
Volume 4: Ceramics; Concentrating Solar Power Plants; Controls Diagnostics and Instrumentation; Education; Electric Power; Fans and Blowers, 2013Co-Authors: Mehrdad Pakmehr, Nathan Fitzgerald, Jeff S. Shamma, Eric Feron, Alireza BehbahaniAbstract:We develop and describe a stable gain scheduling controller for a gas turbine engine that drives a variable pitch propeller. A stability proof is developed for gain scheduled closed-loop system using global Linearization and linear matrix inequality (LMI) techniques. Using convex optimization tools, a single quadratic Lyapunov function is computed for multiple Linearizations near equilibrium and non-equilibrium points of the nonlinear closed-loop system. This approach guarantees stability of the closed-loop gas turbine engine system. Simulation results show the developed gain scheduling controller is capable of regulating a turboshaft engine for large thrust commands in a stable fashion with proper tracking performance.Copyright © 2013 by ASME
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Gain Scheduled Control of Gas Turbine Engines: Stability and Verification
Journal of Engineering for Gas Turbines and Power, 2013Co-Authors: Mehrdad Pakmehr, Nathan Fitzgerald, Eric M. Feron, Jeff S. Shamma, Alireza BehbahaniAbstract:A stable gain scheduled controller for a gas turbine engine that drives a variable pitch propeller is developed and described. A stability proof is developed for gain scheduled closed-loop system using global Linearization and linear matrix inequality (LMI) techniques. Using convex optimization tools, a single quadratic Lyapunov function is computed for multiple Linearizations near equilibrium and nonequilibrium points of the nonlinear closed-loop system. This approach guarantees stability of the closed-loop gas turbine engine system. To verify the stability of the closed-loop system on-line, an optimization problem is proposed, which is solvable using convex optimization tools. Simulation results show that the developed gain scheduled controller is capable to regulate a turboshaft engine for large thrust commands in a stable fashion with proper tracking performance.
Isaac Elishakoff - One of the best experts on this subject based on the ideXlab platform.
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a novel local stochastic Linearization method via two extremum entropy principles
International Journal of Non-linear Mechanics, 2002Co-Authors: Giuseppe Ricciardi, Isaac ElishakoffAbstract:The classical Gaussian stochastic Linearization method for non-linear random vibration problems is reinterpreted on the basis of the maximum entropy principle. Starting from this theoretical result, the maximum entropy principle allows to formulate a local stochastic Linearization method, based on the substitution of the original non-linear system by an equivalent locally linear one. The expressions of the equivalent coefficients are derived. The equivalence of this method with a non-Gaussian closure based on the maximum entropy method for stochastic dynamics is evidenced. In addition, an alternative stochastic Linearization method is proposed, based on the minimum cross-entropy principle. Numerical applications show the superiority of the two proposed local stochastic Linearization methods over the Gaussian one.
