Control-Oriented Modeling of Distributed Parameter Systems

1992 ◽  
Vol 114 (3) ◽  
pp. 339-346 ◽  
Author(s):  
A. J. Helmicki ◽  
C. A. Jacobson ◽  
C. N. Nett
Keyword(s):  
Linear Time ◽  
Distributed Parameter Systems ◽  
Distributed Parameter ◽  
Physical Processes ◽  
Linear Time Invariant ◽  
Time Invariant ◽  
Theoretical Results

In this paper the use of linear, time-invariant, distributed parameter systems (LTI-DPS) as models of physical processes is considered from a control viewpoint. Specifically, recent theoretical results obtained by the authors for the control-oriented modeling of LTI-DPS are concisely reviewed and then a series of applications is given in order to illustrate the practical ramifications of these results.

Variable Structure Control Based Wavelet Transforms for Linear Time-Invariant Distributed Parameter Systems

2012 ◽  
Vol 482-484 ◽  
pp. 1809-1815
Author(s):  
Gui Ge Gao ◽  
Xian Wen Zeng
Keyword(s):  
Control Problem ◽  
Linear Time ◽  
Variable Structure Control ◽  
Variable Structure ◽  
Distributed Parameter Systems ◽  
Distributed Parameter ◽  
Structure Control ◽  
Linear Time Invariant ◽  
Lumped Parameter ◽  
Time Invariant

The distributed parameter systems(DPS) are usually described by the partial differential equations(PDEs). Compared the variable structure control problem of DPS with that of the lumped parameter systems(LPS) , it is more complicated because the problem of variable structure control for DPS is often closely related to the theory of the differential operator or integral operator. Based on Haar wavelets transform, the operational matrixes and their characteristics, this paper attempts to propose a new approach for the variable structure control problem of a class linear time-invariant DPS by converting it into that of lumped parameter systems(LPS) and then makes use of the mature research methods of LPS to design the variable structure control problem so as to solve the variable structure control problem of DPS. The proposed method in this paper has the advantages of the simpler algorithm, less computation and better control effect. The simulation results also prove that it is an efficient algorithm for DPS.

Lumped and Distributed Parameter System Identification Via Shifted Legendre Polynomials

1988 ◽  
Vol 110 (4) ◽  
pp. 436-440 ◽  
Author(s):  
B. M. Mohan ◽  
K. B. Datta
Keyword(s):  
Linear Time ◽  
Operational Matrix ◽  
Distributed Parameter ◽  
Parameter System ◽  
Legendre Series ◽  
Single Input Single Output ◽  
Linear Time Invariant ◽  
Single Output ◽  
Single Input ◽  
Time Invariant

In this paper, one shot operational matrix for repeated integration of the shifted Legendre polynomial basis vector is developed and double-shifted Legendre series is introduced to approximate functions of two independent variables. Then using these, systematic algorithms for the identification of linear time-invariant single input-single output continuous lumped and distributed parameter systems are presented. Illustrative examples are provided with satisfactory results.

Realizing actual feedback control of complex network

Open Physics ◽  
2014 ◽  
Vol 12 (6) ◽  
Author(s):  
Chengyi Tu ◽  
Yuhua Cheng
Keyword(s):  
Feedback Control ◽  
Numerical Simulations ◽  
Linear Time ◽  
Internal State ◽  
Directed Network ◽  
Linear Time Invariant ◽  
Food Network ◽  
Arbitrary Complex ◽  
Time Invariant ◽  
Theoretical Results

AbstractIn this paper, we present the concept of feedbackability and how to identify the Minimum Feedbackability Set of an arbitrary complex directed network. Furthermore, we design an estimator and a feedback controller accessing one MFS to realize actual feedback control, i.e. control the system to our desired state according to the estimated system internal state from the output of estimator. Last but not least, we perform numerical simulations of a small linear time-invariant dynamics network and a real simple food network to verify the theoretical results. The framework presented here could make an arbitrary complex directed network realize actual feedback control and deepen our understanding of complex systems.

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