PUMA publications for /group/simtech/optimizationTue Jul 19 11:10:29 CEST 2022Frontiers in Physiology05Exhaustion of Skeletal Muscle Fibers Within Seconds: Incorporating Phosphate Kinetics Into a Hill-Type Model112020biomechanics analysis endurance estimation fatigue optimization dynamics parameter sensitivity time first-order Tue Dec 07 20:40:52 CET 2021Int. J. Control5851-864{R}obust $l_1$ performance analysis for linear systems with parametric uncertainties812008approach lyapunov functions relaxations imng matrix inequality optimization EXC310 pn4 peerReviewed programs dependent In this contribution, a computational approach for analysing the robust e.-gain (or the robust l(1) performance) of uncertain linear systems is developed. In particular, the system's state-space matrices may have a rational dependence on structured parametric time-invariant or time-varying uncertainties. The computation is based on robust semi-definite programming and provides a trade-off between accuracy and computational effort. A novel matrix inequality condition to determine the star-norm of discrete-time systems is derived as an auxiliary result.Tue Dec 07 20:40:52 CET 2021Automatica0771115-1127{R}obust output-feedback controller design via local {BMI} optimization402004robust multiobjective synthesis control imng feasibility h-infinity inequalities bilinear algorithms formulas linear-systems global h-2 matrix uncertainty output-feedback optimization design parameter peerReviewed lmis structured dynamic order The problem of designing a globally optimal full-order output-feedback controller for polytopic uncertain systems is known to be a non-convex NP-hard optimization problem, that can be represented as a bilinear matrix inequality optimization problem for most design objectives. In this paper a new approach is proposed to the design of locally optimal controllers. It is iterative by nature, and starting from any initial feasible controller it performs local optimization over a suitably defined non-convex function at each iteration. The approach features the properties of computational efficiency, guaranteed convergence to a local optimum, and applicability to a very wide range of problems. Furthermore, a fast (but conservative) LMI-based procedure for computing an initially feasible controller is also presented. The complete approach is demonstrated on a model of one joint of a real-life space robotic manipulator. (C) 2004 Elsevier Ltd. All rights reserved.Tue May 18 15:30:24 CEST 2021IEEE Control Systems Letters4743-748Derivative-Free Optimization Algorithms Based on Non-Commutative Maps22018programming;Switches;Convergence;Commutation;Adaptive optimization control;Optimization algorithms;adaptive control algorithms;Optimization;Linear maps;Approximation algorithms;noncommutative convergence;optimisation;derivative-free