Analog study of dead-beat posicast control

Tallman, G.; Smith, Otto J. M.

doi:10.1109/tac.1958.1104844

Cited by 118 publications

(40 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Input shaping was originated by Singer and Seering in [19] based on the Posicast control of Smith in [20] and Tallman and Smith in [21]. Excellent review of the input shaping technique can be found in [22].…”

Section: Robust Input Shapingmentioning

confidence: 99%

Model Reference Input Shaping Using Quantitative Feedforward-Feedback Controller

Chatlatanagulchai¹

2017

View full text Add to dashboard Cite

Abstract. Input shaping convolutes the reference signal with a sequence of impulses, whose amplitudes and timings are designed to produce a shaped reference that avoids exciting lightly-damped modes to reduce residual vibration from a quick movement. The input shaper can be made robust to uncertain mode parameters by adding more impulses, which delays the reference signal, resulting in longer move time. Instead of using more impulses, in this paper, a feedforward-feedback control system, based on the quantitative feedback theory, is placed in the loop to match the closed-loop system, with uncertain plant, to a known reference model. The feedforward-feedback system handles the uncertainty, so the input shaper, placed outside the loop, needs not be robust. The closed-loop system emphasizes on selected frequencies and reduces the cost of feedback. It is shown that the proposed feedforward-feedback system is less conservative than the pure-feedback system. Other sources of vibration such as external disturbances and noise can be handled by the feedforward-feedback system as well. Simulation shows that the proposed technique can withstand large plant uncertainty with fast move time when compared to traditional robust input shaper.

show abstract

Section: Robust Input Shapingmentioning

confidence: 99%

Model Reference Input Shaping Using Quantitative Feedforward-Feedback Controller

Chatlatanagulchai¹

2017

View full text Add to dashboard Cite

show abstract

“…Consequently, the electrical power system is modelled as a DC grid connected to a resistive load. Equations (8) and (9) show the torque of each machine and the voltage equation of the power system, with , the airgap torque of the generators, , the machines constant, , the current on each power system, , the terminal voltage of each generator,…”

Section: ) Electrical System Modellingmentioning

confidence: 99%

“…The aim of the paper is to minimize the EMI between the electrical power system and the shaft of the generator of an aircraft system. This paper proposed a PMS that uses strategies based on the Posicast compensation method [7], [8]. In the PMS, the electrical loads are categorized as Time Critical Loads and Time Non-critical Loads with respect to its connection properties.…”

Section: Introductionmentioning

confidence: 99%

Minimization of electro-mechanical interaction with posicast strategies for more-electric aircraft applications

Ahumada

Garvey

Yang

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

Abstract-This paper studies strategies to minimize the electromechanical interaction (EMI) within aircraft power systems. With the growth of electrical power on-board aircraft, the interaction between the electrical systems and the engine core will become significant. The behaviour of electrical loads (on/off, transient etc.) will have significant impacts on the engine shaft, such as producing transient vibrations, creating stability problems and reducing the efficiency etc. To avoid these problems, an advanced electrical power management system (PMS) is required. This paper introduces novel loading methods for PMS applications to minimize the interactions between electrical and mechanical systems. The strategies, referred as Single Level Multi-edge Switching Loads (SLME), Multilevel Loading (MLL), and Multiload Single Level Multi-edge Switching Loads (MSLME) are developed based on the Posicast method. An insight look of the developed technique has been studied using the zero-pole root locus. It is demonstrated that the excited poles in the system are cancelled by the addition of zeros, and thus supressed the EMI vibrations.

show abstract

“…The technique is based on destructive interference of impulse responses, that is, an impulse response can be cancelled by another impulse response, given appropriate impulse amplitudes and applied times. The input shaping was originally proposed under the name Posicast control by Smith in [1]; Smith in [2]; and Tallman and Smith in [3]. The first application of the Posicast control to robot transport of suspended objects was performed by Starr in [4] and Starr in [5], whose method was further generalized by Strip in [6].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Backstepping System to Increase Input Shaping Performance in Suppressing Residual Vibration of a Flexible-Joint Robot Manipulator

Chatlatanagulchai¹,

Nithi-Uthai²

2017

View full text Add to dashboard Cite

Abstract. Input shaping technique can be used to suppress residual vibration, occurring from moving rapidly a flexible system from one point to another point. An input shaping filter produces a shaped input signal that avoids exciting the flexible modes of the flexible system. The technique requires accurate knowledge of mode parameters. When the plant model is not accurate, performance of the input shaper degrades. Several robust input shapers were proposed to handle this inaccuracy at the expense of longer move time. The purpose of this paper is, for the first time, to present an application of an intelligent backstepping system to matching of the resulting closed-loop system with a reference model. The input shaper can then be designed from the mode parameters of the reference model. Because the reference model is accurate even when the plant model is not, the input shaper needs not be robust, resulting in shorter move time. The intelligent backstepping system consists of a three-layer neural network, a variable structure controller, and a backstepping controller. The neural network is used as a black-box model in case when the plant model is unknown, making the proposed system model-independent. The adaptive property of the neural network also makes the proposed system suitable for nonlinear, time-varying, or configuration-dependent systems. The variable structure controller handles the uncertainty arisen in the system. The backstepping controller, through its virtual controls, provides a means for the control authority to reach the unmatched uncertainty in the system. This study contains simulation and experimental results on a flexible-joint robot manipulator. The results showed that this proposed intelligent input shaping system outperformed previously proposed robust input shapers in terms of allowable uncertainty amount and move time. The proposed system is also relatively easy to apply because it does not require the plant model.

show abstract

Analog study of dead-beat posicast control

Cited by 118 publications

References 3 publications

Model Reference Input Shaping Using Quantitative Feedforward-Feedback Controller

Model Reference Input Shaping Using Quantitative Feedforward-Feedback Controller

Minimization of electro-mechanical interaction with posicast strategies for more-electric aircraft applications

Intelligent Backstepping System to Increase Input Shaping Performance in Suppressing Residual Vibration of a Flexible-Joint Robot Manipulator

Contact Info

Product

Resources

About