Input Requirements and Parametric Errors for System Identification Under Periodic Excitation

1972 ◽  
Vol 94 (4) ◽  
pp. 296-302 ◽  
Author(s):  
L. L. Hoberock ◽  
G. W. Stewart
Keyword(s):  
Linear System ◽  
Input State ◽  
Matrix Elements ◽  
Periodic Excitation ◽  
Input Output ◽  
Input Matrix ◽  
Multiple Input ◽  
Minimum Number ◽  
Single Input ◽  
Work Done

This paper provides the conditions on periodic system excitation necessary for unique identification using a multiple input state model of a dynamic system. Results include the minimum number of input frequencies necessary to uniquely determine all state and input matrix elements of an n dimensional linear system. It is shown that this development encompasses earlier work done on single input-output systems. A technique is provided for predicting parametric errors to be expected from identification under periodic excitation, and several examples are used to illustrate these errors.

SINGLE-INPUT MULTI-OUTPUT STATE-FEEDBACK CHAOTIFICATION OF GENERAL DISCRETE SYSTEMS

2004 ◽  
Vol 14 (09) ◽  
pp. 3317-3323 ◽  
Author(s):  
HUAIZHOU ZHANG ◽  
GUANRONG CHEN
Keyword(s):  
Discrete Time ◽  
State Feedback ◽  
Discrete Systems ◽  
Input State ◽  
Output State ◽  
Discrete Time Systems ◽  
Multiple Input ◽  
Single Input ◽  
Time Systems

This Letter improves the Chen–Lai chaotification algorithm for discrete-time systems from multiple-input to single-input state feedback, while preserving its mathematical rigor.

An Input-Output Criterion for Linear Model Deduction

1996 ◽  
Vol 122 (3) ◽  
pp. 507-513 ◽  
Author(s):  
Douglas G. Walker ◽  
Jeffrey L. Stein ◽  
A. Galip Ulsoy
Keyword(s):  
Multiple Input Multiple Output ◽  
Input Output ◽  
Minimum Order ◽  
New Model ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Single Input ◽  
Mimo Model ◽  
Controllability And Observability ◽  
Output Criterion

Model order deduction algorithms have been developed in an effort to automate the production of accurate, minimal complexity models of dynamic systems in order to aid in the design of these systems. Previous algorithms, MODA and Extended MODA, deduce models independent of system inputs and outputs. FD-MODA uses frequency response methods to deduce models of a single input-output pair. In this paper, an input-output criterion based on controllability and observability is combined with the frequency based criterion used by MODA. The new model deduction algorithm, IO-MODA, compares the ratio of the adjacent diagonal values of the system gramian to a user specified threshold. The gramian is computed from a balanced realization of the system. IO-MODA generates an accurate multiple-input multiple-output model of minimum order with physically meaningful states. This model is called a proper MIMO model. An example problem is used to demonstrate this new model deduction algorithm. [S0022-0434(00)02103-1]

CHAOTIFYING THE CONTROLLABLE LINEAR SYSTEM VIA SINGLE INPUT STATE FEEDBACK

2006 ◽  
Vol 17 (07) ◽  
pp. 1027-1035
Author(s):  
ZHENG MAO WU ◽  
JUN GUO LU ◽  
JIAN YING XIE ◽  
JIE LI
Keyword(s):  
Linear System ◽  
Chaotic Dynamics ◽  
State Feedback ◽  
Closed Loop ◽  
Loop System ◽  
Input State ◽  
Closed Loop System ◽  
Single Input ◽  
Simulation Results ◽  
System States

An approach for chaotifying a stable controllable linear system via single input state-feedback is presented. The feedback controller designed is a sawtooth function of the system states, which can make the fixed point of the closed-loop system to be a snap-back repeller, thereby yielding chaotic dynamics. Based on the Marotto theorem, it is proven theoretically that the closed-loop system is chaotic in the sense of Li and Yorke. Finally, the simulation results are used to illustrate the effectiveness of the proposed theory.

General Theory for Multiple Input-Output Perturbations in Complex Molecular Systems. 1. Linear QSPR Electronegativity Models in Physical, Organic, and Medicinal Chemistry

2013 ◽  
Vol 13 (14) ◽  
pp. 1713-1741 ◽  
Author(s):  
Humberto Gonzalez-Diaz ◽  
Sonia Arrasate ◽  
Asier Gomez-SanJuan ◽  
Nuria Sotomayor ◽  
Esther Lete ◽  
...  
Keyword(s):  
General Theory ◽  
Medicinal Chemistry ◽  
Input Output ◽  
Molecular Systems ◽  
Physical Organic ◽  
Multiple Input

scholarly journals Robust Synchronization of Delayed Chaotic FitzHugh-Nagumo Neurons under External Electrical Stimulation

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Rehan ◽  
Keum-Shik Hong
Keyword(s):  
Electrical Stimulation ◽  
Nonlinear Control ◽  
Control Law ◽  
Input Control ◽  
Control Schemes ◽  
Multiple Input ◽  
Cognitive Diseases ◽  
Single Input ◽  
Error Dynamics ◽  
Nonlinear Control Law

Synchronization of chaotic neurons under external electrical stimulation (EES) is studied in order to understand information processing in the brain and to improve the methodologies employed in the treatment of cognitive diseases. This paper investigates the dynamics of uncertain coupled chaotic delayed FitzHugh-Nagumo (FHN) neurons under EES for incorporated parametric variations. A global nonlinear control law for synchronization of delayed neurons with known parameters is developed. Based on local and global Lipschitz conditions, knowledge of the bounds on the neuronal states, the Lyapunov-Krasovskii functional, and theL2gain reduction, a less conservative local robust nonlinear control law is formulated to address the problem of robust asymptotic synchronization of delayed FHN neurons under parametric uncertainties. The proposed local control law guarantees both robust stability and robust performance and provides theL2bound for uncertainty rejection in the synchronization error dynamics. Separate conditions for single-input and multiple-input control schemes for synchronization of a wide class of FHN systems are provided. The results of the proposed techniques are verified through numerical simulations.

Estimation Design Using Youla Parametrization With Automotive Applications

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Francis Assadian ◽  
Alex K. Beckerman ◽  
Jose Velazquez Alcantar
Keyword(s):  
Kalman Filtering ◽  
Feedback Stabilization ◽  
Estimation Technique ◽  
Single Input Single Output ◽  
Normal Forces ◽  
Single Output ◽  
Multiple Input ◽  
Single Input ◽  
Established Technique ◽  
Better Than

Youla parametrization is a well-established technique in deriving single-input single-output (SISO) and, to a lesser extent, multiple-input multiple-ouput (MIMO) controllers (Youla, D., Bongiorno, J. J., Jr., and Lu, C., 1974, “Singleloop Feedback-Stabilization of Linear Multivariable Dynamical Plants,” Automatica, 10(2), pp. 159–173). However, the utility of this methodology in estimation design, specifically in the framework of controller output observer (COO) (Ozkan, B., Margolis, D., and Pengov, M., 2008, “The Controller Output Observer: Estimation of Vehicle Tire Cornering and Normal Forces,” ASME J. Dyn. Syst., Meas., Control, 130(6), p. 061002), is not established. The fundamental question to be answered is as follows: is it possible to design a deterministic estimation technique using Youla paramertization with the same robust performance, or better, than well-established stochastic estimation techniques such as Kalman filtering? To prove this point, at this stage, a comparative analysis between Youla parametrization in estimation and Kalman filtering is performed through simulations only. In this paper, we provide an overview of Youla parametrization for both control and estimation design. We develop a deterministic SISO and MIMO Youla estimation technique in the framework of COO, and we investigate the utility of this method for two applications in the automotive domain.

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