H>inf<∞>/inf<parameter-dependent state feedback control of linear time-varying systems in polytopic domains

2006 ◽  
Author(s):  
V.F. Montagner ◽  
P.L.D. Peres
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
Linear Time ◽  
State Feedback Control ◽  
Time Varying ◽  
Time Varying Systems ◽  
Parameter Dependent ◽  
Dependent State

scholarly journals Switched H2-state-feedback control with application to a fighter aircraft

2019 ◽  
Vol 233 (14) ◽  
pp. 5428-5437
Author(s):  
Emre Kemer ◽  
Hasan Başak ◽  
Emmanuel Prempain
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
Linear Time ◽  
State Feedback Control ◽  
Time Varying ◽  
Fighter Aircraft ◽  
Control Laws ◽  
Use Of Time ◽  
External Signals ◽  
Parameter Dependent

This paper proposes two different [Formula: see text]-state-feedback controller synthesis algorithms for uncertain linear, time-varying, switched systems. The synthesis algorithms are based on a dwell-time approach, which makes use of time-varying parameter-dependent Lyapunov functions. The control laws consist of state-feedback controllers that are switched according to external signals. The proposed synthesis algorithms are then employed to design switched [Formula: see text]-state-feedback control laws for the longitudinal dynamics of the ADMIRE fighter benchmark model. The results obtained in simulation show the merits of the proposed approach.

scholarly journals An Offline Formulation of MPC for LPV Systems Using Linear Matrix Inequalities

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
P. Bumroongsri
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Linear Matrix ◽  
Control Law ◽  
Time Varying ◽  
Control Laws ◽  
Lpv Systems ◽  
Parameter Dependent ◽  
Dependent State

An offline model predictive control (MPC) algorithm for linear parameter varying (LPV) systems is presented. The main contribution is to develop an offline MPC algorithm for LPV systems that can deal with both time-varying scheduling parameter and persistent disturbance. The norm-bounding technique is used to derive an offline MPC algorithm based on the parameter-dependent state feedback control law and the parameter-dependent Lyapunov functions. The online computational time is reduced by solving offline the linear matrix inequality (LMI) optimization problems to find the sequences of explicit state feedback control laws. At each sampling instant, a parameter-dependent state feedback control law is computed by linear interpolation between the precomputed state feedback control laws. The algorithm is illustrated with two examples. The results show that robust stability can be ensured in the presence of both time-varying scheduling parameter and persistent disturbance.

Stabilization of linear time-varying systems using proportional-derivative state feedback

2017 ◽  
Vol 40 (7) ◽  
pp. 2100-2115 ◽  
Author(s):  
Taha HS Abdelaziz
Keyword(s):  
Degrees Of Freedom ◽  
State Feedback ◽  
Linear Time ◽  
State Feedback Control ◽  
Time Varying ◽  
Closed Loop Systems ◽  
Continuous Time Systems ◽  
Time Varying Systems ◽  
Small Gain ◽  
Time Systems

This paper presents the stabilization approach for linear time-varying continuous-time systems using proportional-derivative (PD) state feedback control. The solvability conditions for the problem are considered. The general analytical expressions for the PD controller gains are derived, which describe the available degrees of freedom offered by PD state feedback. The non-uniqueness of the controller gains is utilized to obtain closed-loop systems with small gain elements. Two numerical examples are introduced to demonstrate the effectiveness of the proposed approach.

NN approach to state-feedback control of high-order non-linear time-delay systems with time-varying control coefficient

2016 ◽  
Vol 40 (1) ◽  
pp. 163-170 ◽  
Author(s):  
Min Huifang ◽  
Duan Na
Keyword(s):  
Neural Network ◽  
Feedback Control ◽  
State Feedback ◽  
Linear Time ◽  
High Order ◽  
State Feedback Control ◽  
Delay Systems ◽  
Time Varying ◽  
Control Coefficient ◽  
Non Linear

This paper considers the adaptive state-feedback control problem for a class of high-order non-linear systems with unknown control coefficient and time delays. By applying the neural network approximation method and the Nussbaum function approach, the restrictions on non-linear functions and the conditions on the time-varying control coefficient are largely relaxed. In addition, an adaptive neural network state-feedback controller with only one adaptive parameter is successfully constructed by introducing proper Lyapunov–Krasovskii functionals and using the backstepping technique. The proposed scheme guarantees the closed-loop system to be semi-globally uniformly ultimately bounded. Finally, a simulation example demonstrates the effectiveness of the controller.

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