Stable Adaptive Control and Estimation for Nonlinear Systems

Spooner, J.T.; Maggiore, Manfredi; Ordóñez, Raúl; Passino, K.M.

doi:10.1002/0471221139

Cited by 347 publications

(83 citation statements)

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“…Based on the Barbalat's Lemma [15], we can obtain M(t) → 0 as t→∞ (26) That is, e z1 and e z2 will converge to zero as t→∞. Therefore, the proposed controller is stable and robust.…”

Section: Proofmentioning

confidence: 99%

Robust Speed Sensorless Control of Doubly-Fed Induction Machine Based on Input-Output Feedback Linearization Control Using a Sliding-Mode Observer

Payam

Jalalifar

2006

2006 International Conference on Power Electronic, Drives and Energy Systems

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Abstract:In this paper, a nonlinear controller is presented for Doubly-Fed Induction Machine (DFIM) drives. The nonlinear controller is designed based on the input-output feedback linearization control technique, combined with a sliding-mode observer, using the fifth order mo del of the doubly-fed induction machine in fixed stator d-q axis reference frame with stator current and rotor flux components as state variables. The nonlinear controller can perfectly track the torque and flux reference signals in spite of stator and rotor resistance variations. In order to make the drive system capable of operating in the motoring and generating modes both below and above synchronous speed, two level SVM -PWM back-to-back voltage source inverters are employed in the rotor circuit. Computer simulation results obtained, confirm the effectiveness and validity of the proposed control approach.

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Section: Proofmentioning

confidence: 99%

Robust Speed Sensorless Control of Doubly-Fed Induction Machine Based on Input-Output Feedback Linearization Control Using a Sliding-Mode Observer

Payam

Jalalifar

2006

2006 International Conference on Power Electronic, Drives and Energy Systems

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“…In addition, the -modification by Spooner et al in [61] can be used to prevent the domination of the terms leading to poor approximations of the weights.…”

Section: Intelligent Backstepping Input Shapingmentioning

confidence: 99%

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

Chatlatanagulchai¹,

Nithi-Uthai²

2017

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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.

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“…For the given rule base of a control system, the fuzzy controller determines the rule base to be fired for the specific input signal condition and then computes the effective control action (the output fuzzy variable) [Bose B. K. 1994 ;Spooner J.T. et al 2002].…”

Section: Fuzzy Logic Principlementioning

confidence: 99%