Reduced-order controllers for the discrete-time H/sub ∞/ control problem with unstable invariant zeros

2004 ◽  
Author(s):  
Xin Xin
Keyword(s):  
Control Problem ◽  
Discrete Time ◽  
Reduced Order

LQG Control for Nonstandard Singularly Perturbed Discrete-Time Systems

2004 ◽  
Vol 126 (4) ◽  
pp. 860-864 ◽  
Author(s):  
Beom-Soo Kim ◽  
Young-Joong Kim ◽  
Myo-Taeg Lim
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Discrete Time ◽  
Singularly Perturbed ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Reduced Order ◽  
Discrete Time Systems ◽  
Time Systems

In this paper we present a control method and a high accuracy solution technique in solving the linear quadratic Gaussian problems for nonstandard singularly perturbed discrete time systems. The methodology that exists in the literature for the solution of the standard singularly perturbed discrete time linear quadratic Gaussian optimal control problem cannot be extended to the corresponding nonstandard counterpart. The solution of the linear quadratic Gaussian optimal control problem is obtained by solving the pure-slow and pure-fast reduced-order continuous-time algebraic Riccati equations and by implementing the pure-slow and pure-fast reduced-order Kalman filters. In order to show the effectiveness of the proposed method, we present the numerical result for a one-link flexible robot arm.

Reduced order positive filter design for positive uncertain discrete-time linear systems*

2021 ◽  
pp. 1-1
Author(s):  
Amanda Spagolla ◽  
Cecilia F. Morais ◽  
Ricardo C. L. F. Oliveira ◽  
Pedro L. D. Peres
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Filter Design ◽  
Reduced Order ◽  
Time Linear

scholarly journals Event-triggered Discrete-time Sliding Mode Control for High-order Systems via Reduced-order Model Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 6207-6212
Author(s):  
Kiran Kumari ◽  
Bijnan Bandyopadhyay ◽  
Johann Reger ◽  
Abhisek K. Behera
Keyword(s):  
Sliding Mode Control ◽  
Discrete Time ◽  
Sliding Mode ◽  
High Order ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Mode Control ◽  
Event Triggered ◽  
Model Approach

Robust reduced-order l2−l∞ filtering for network-based discrete-time linear systems

2015 ◽  
Vol 109 ◽  
pp. 110-118 ◽  
Author(s):  
Zhuo Zhang ◽  
Zexu Zhang ◽  
Shichun Yang
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Reduced Order ◽  
Time Linear

The discrete-time H/sub ∞/ control problem with strictly proper measurement feedback

1994 ◽  
Vol 39 (9) ◽  
pp. 1936-1939 ◽  
Author(s):  
A.A. Stoorvogel ◽  
A. Saberi ◽  
B.M. Chen
Keyword(s):  
Control Problem ◽  
Discrete Time ◽  
Measurement Feedback

A Fast, Memory-Efficient Discrete-Time Realization Algorithm for Reduced-Order Li-Ion Battery Models

2017 ◽  
Vol 14 (1) ◽  
Author(s):  
Krishnakumar Gopalakrishnan ◽  
Teng Zhang ◽  
Gregory J. Offer
Keyword(s):  
Discrete Time ◽  
Porous Electrode ◽  
Hankel Matrix ◽  
Lithium Ion ◽  
Computation Time ◽  
Power Sources ◽  
Reduced Order ◽  
The Time Domain ◽  
Computational Bottleneck ◽  
Value Decomposition

Research into reduced-order models (ROM) for Lithium-ion batteries is motivated by the need for a real-time embedded model possessing the accuracy of physics-based models, while retaining computational simplicity comparable to equivalent-circuit models. The discrete-time realization algorithm (DRA) proposed by Lee et al. (2012, “One-Dimensional Physics-Based Reduced-Order Model of Lithium-Ion Dynamics,” J. Power Sources, 220, pp. 430–448) can be used to obtain a physics-based ROM in standard state-space form, the time-domain simulation of which yields the evolution of all the electrochemical variables of the standard pseudo-2D porous-electrode battery model. An unresolved issue with this approach is the high computation requirement associated with the DRA, which needs to be repeated across multiple SoC and temperatures. In this paper, we analyze the computational bottleneck in the existing DRA and propose an improved scheme. Our analysis of the existing DRA reveals that singular value decomposition (SVD) of the large Block–Hankel matrix formed by the system's Markov parameters is a key inefficient step. A streamlined DRA approach that bypasses the redundant Block–Hankel matrix formation is presented as a drop-in replacement. Comparisons with existing DRA scheme highlight the significant reduction in computation time and memory usage brought about by the new method. Improved modeling accuracy afforded by our proposed scheme when deployed in a resource-constrained computing environment is also demonstrated.

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