A note on trajectory sensitivity of optimal control systems

Sannuti, P.; Cruz, J.B.; Lee, Imsong; Bradt, A.

doi:10.1109/tac.1968.1098782

Cited by 20 publications

(4 citation statements)

References 2 publications

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“…where H is the same as in (12) and Pi = Pi > 0 is the unique solution of A; TP; + P;A; + W; = 0 ( 60)…”

Section: Optimal Controller Gain With Both Trajectory and Model Feedbackmentioning

confidence: 99%

Robust quadratic optimization of systems with large parameter variations

Eslami

1991

Optim Control Appl Methods

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SUMMARYI n this paper, two new algorithms to synthesize robust controllers which tolerate large parameter variations in the system are presented. The new analytical procedure that has led to the establishment o f these algorithms can be easily used to solve similar perturbational problems when small-sensitivity measures are used. These algorithms are applied to a numerical example to show the system performance under these design methodologies and to compare the final outcomes of these algorithms.

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“…where H is the same as in (12) and Pi = Pi > 0 is the unique solution of A; TP; + P;A; + W; = 0 ( 60)…”

Section: Optimal Controller Gain With Both Trajectory and Model Feedbackmentioning

confidence: 99%

Robust quadratic optimization of systems with large parameter variations

Eslami

1991

Optim Control Appl Methods

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“…However, the sensitivity reduction is not significant. Several authors (2)(3)(4)(5)(6)(7)(8) attempted to reduce the sensitivity by augmenting the trajectory sensitivity equation with the state equation and/or output equation and by modifying the performance index to include a weighting on the quadratic trajectory sensitivity term, and/or defined the control law by output feedback. All back owing to the fact that the variation of control due to the changes system paramters, (dti/das), is not known a priori.…”

Section: Introductionmentioning

confidence: 99%

Design of low sensitivity controllers: Preregulator approach

Liou¹,

1985

Journal of the Chinese Institute of Engineers

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Since the trajectory sensitivity can not be modeled accurately in a closed-loop formulation, the linear preregulator design techniques are investigated to approximate the sensitivity information, and two methods leading to transfer-function matrix form are developed. The obtained control systems have shown much improvement in reducing the sensitivity, specially for Method B. The advantages of Method B over Method A are that the difficulty of obtaining the steady state control law in terms of the reference input can be prevented and that the controller containing an integral action can be obtained to ensure that there is no steady-state error.

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“…Since then it has been used in a variety of applications including sensitivity and performance optimization and robustness analysis [18]. Early research focused on LQR problems [19,20] and proposed augmenting the sensitivity dynamics with the system equations.…”

Section: Robust Feedback Linearizationmentioning

confidence: 99%

“…20 shows the performance of the nominal controller, which is nearly equivalent to the robust controller, thus the robust input adjustment for sensitivity has no noticeable impact on the header height tracking performance.…”

mentioning

confidence: 99%

Header height control of combine harvester via robust feedback linearization

Kassen

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A note on trajectory sensitivity of optimal control systems

Cited by 20 publications

References 2 publications

Robust quadratic optimization of systems with large parameter variations

Robust quadratic optimization of systems with large parameter variations

Design of low sensitivity controllers: Preregulator approach

Header height control of combine harvester via robust feedback linearization

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