Controller falsification based on multiple models

Agnoloni, Tommaso; Mosca, E.

doi:10.1002/acs.745

Cited by 12 publications

(6 citation statements)

References 16 publications

(18 reference statements)

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“…It was also referred to as virtual reference in [21,22]. Here we use the name "matching reference signal" since Definition 6 (consistence): The controller set Σ C is said to be consistent with the plant Σ P (for the control system shown in Fig.…”

Section: Remarkmentioning

confidence: 99%

Non-identifier based adaptive control scheme with guaranteed stability

Zhang

Ioannou

2006

2006 American Control Conference

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In this paper, we investigate a non-identifier based adaptive control scheme for single-input single-output (SISO) plants. The overall adaptive control system can be viewed as a feedback interconnection of a SISO plant, a set of candidate controllers and a non-identifier based adaptive scheme that supervises the switching process among the candidate controllers. The adaptive scheme is designed without any explicit assumptions on the plant model, and the closed-loop system is guaranteed to be stable as long as at least one of the candidate controllers is stabilizing. The basic idea has been investigated by Safonov et al. in [1, 2], but the candidate controllers are limited to be "stably causally left invertible" (SCLI). In this paper, we show that stronger stability results can be achieved by properly resetting controller states. Furthermore, the requirement for SCLI is eliminated for the case where all the subsystems are linear time invariant (LTI).

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Section: Remarkmentioning

confidence: 99%

Non-identifier based adaptive control scheme with guaranteed stability

Zhang

Ioannou

2006

2006 American Control Conference

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“…Several kinds of this logic are used in the literature, for example, hysteresis switching 8, and dwell‐time switching 9. These methods are capable of determining in real‐time which candidate controller should be put in feedback with a process in order to achieve the desired closed‐loop performance. Despite the first and the second categories that have much aspects in common, the third logic is a direct method that bypasses plant identification based on controller unfalsification 10–12. In this case, the title is ‘Unfalsified Control’ (UC).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the first and the second categories that have much aspects in common, the third logic is a direct method that bypasses plant identification based on controller unfalsification 10–12. In this case, the title is ‘Unfalsified Control’ (UC).…”

Section: Introductionmentioning

confidence: 99%

Robust multiple model adaptive control: Modified using ν‐gap metric

Mahdianfar

Ozgoli

Momeni

2010

Intl J Robust & Nonlinear

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SUMMARYA new robust adaptive control method is proposed, which removes the deficiencies of the classic robust multiple model adaptive control (RMMAC) using benefits of the -gap metric. First, the classic RMMAC design procedure cannot be used for systematic design for unstable plants because it uses the Baram Proximity Measure, which cannot be calculated for open-loop unstable plants. Next, the %FNARC method which is used as a systematic approach for subdividing the uncertainty set makes the RMMAC structure being always companion with the -synthesis design method. Then in case of two or more uncertain parameters, the model set definition in the classic RMMAC is based on cumbersome ad hoc methods. Several methods based on -gap metric for working out the mentioned problems are presented in this paper. To demonstrate the benefits of the proposed RMMAC method, two benchmark problems subject to unmodeled dynamics, stochastic disturbance input and sensor noise are considered as case studies. The first case-study is a non-minimum-phase (NMP) system, which has an uncertain NMP zero; the second case-study is a mass-spring-dashpot system that has three uncertain real parameters. In the first case-study, five robust controller design methods (H 2 , H ∞ , QFT, H ∞ loop-shaping and -synthesis) are implemented and it is shown via extensive simulations that RMMAC/ /QFT method improves disturbance-rejection, when compared with the classic RMMAC. In the second case-study, two robust controller design methods (QFT and mixed -synthesis) are applied and it is shown that the RMMAC/ /QFT method improves disturbance-rejection, when compared with RMMAC/ /mixed-.

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“…An adaptive-network-based fuzzy inference system (ANFIS) is used to estimate and compensate the unmodeled dynamics. Based on the performance indices (11) and (21), a switching function (47) is introduced to switch between the…”

mentioning

confidence: 99%

“…Recently proposed data-driven controller designs include controller falsification [11], [12], simultaneous perturbation stochastic approximation controlbased control design [13], iterative feedback tuning technique [14], virtual reference feedback tuning method [15], and datadriven simulation and control [16].…”

mentioning

confidence: 99%