The Redundancy of Feedback Gain Matrix and the Derivation of Low Feedback Gain Matrix in Pole Assignmet

Hikita, Hiromitsu; Koyama, Shoichi; Miura, Reiko

doi:10.9746/sicetr1965.11.556

Cited by 18 publications

(19 citation statements)

References 8 publications

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“…Considering z.o.h. with an imperfect dead-time compensator using 1 st order Pade approximation [9] and a damping tuning to double real poles which can be assigned by a dual regulator using a pole assignment method [12], the overshoot in the reference step response is lager than one in OCR and in CAR.…”

Section: Numerical Resultsmentioning

confidence: 99%

Simple robust normalized IP control design for unknown input disturbance

Katoh

2008

2008 SICE Annual Conference

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The present paper proposes a simple UV method derived from the Lagrange approach to obtain control parameters by the loop shaping approach based design policy. In addition, robust loop gain margins of the normalized IP control system were derived and confirmed for second-order CDR with OME in the case of a certain time constant and an uncertain time constant. These margins were designed in a continuous system and could be evaluated and approximated by a CAR using the matrix logarithm conversion for the sampled system approximated in the ADR. The results of numerical evaluation in partial lattice points of damping and uncertain time constant plane clarified the risk of VIP control. Based on space limitations, the results of simulation are not presented herein.

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Section: Numerical Resultsmentioning

confidence: 99%

Simple robust normalized IP control design for unknown input disturbance

Katoh

2008

2008 SICE Annual Conference

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“…This is a well-known problem, and the pole placement methods have been extensively discussed in the literature [1] [2] [3] [4]. In standard pole placement methods, a state space model is assumed to be given by a system identification technique using data from past experiments.…”

Section: Introductionmentioning

confidence: 99%

An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

Shwe¹,

Yamamoto²

2017

ICA

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The recently proposed data-driven pole placement method is able to make use of measurement data to simultaneously identify a state space model and derive pole placement state feedback gain. It can achieve this precisely for systems that are linear time-invariant and for which noiseless measurement datasets are available. However, for nonlinear systems, and/or when the only noisy measurement datasets available contain noise, this approach is unable to yield satisfactory results. In this study, we investigated the effect on data-driven pole placement performance of introducing a prefilter to reduce the noise present in datasets. Using numerical simulations of a self-balancing robot, we demonstrated the important role that prefiltering can play in reducing the interference caused by noise.

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“…In the first, parametrization of K was researched. It is often said that parametrization of K was developed by Fahmy and O'Reilley 121 and Roppenecker 131, but before them Hikita et al [4] refered to parametrization in their own paper. It is an essential problem to judge what obective function should be considered as optimal.…”

Section: Introductionmentioning

confidence: 99%

“…It is an essential problem to judge what obective function should be considered as optimal. Hikita et al [4] considered that optimal K must be of lowest gain, and presented the method to obtain K with minimum value of k:j. The value of xi.…”

Section: Introductionmentioning

confidence: 99%

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Optimal robust pole assignment

Ichikawa

Proceedings of the 37th SICE Annual Conference. International Session Papers

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An optimal robust pole assignment scheme is presented in this paper. Robustness is measured by the spectral condition number of the closed loop eigenstructure. Since the spectral condition number is nondifferentiable, an approximate differentiable objective function, which includes an index p , is introduced. As p tends to infinity, the optimizer for the approximate objective function converges to that for condition number. For each p , a steepest descent method is used to find the optimizer. In our example, the optimizer converges within a very small p .

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The Redundancy of Feedback Gain Matrix and the Derivation of Low Feedback Gain Matrix in Pole Assignmet

Cited by 18 publications

References 8 publications

Simple robust normalized IP control design for unknown input disturbance

Simple robust normalized IP control design for unknown input disturbance

An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

Optimal robust pole assignment

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