Adaptive locally-linear-models-based fault detection and diagnosis for unmeasured states and unknown faults

Soltanian, Farzad; Alvanagh, Ahmad Akbari; Khosrowjerdi, Mohammad Javad

doi:10.1109/icciautom.2011.6356710

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Looking ahead, the study opens avenues for future research. The development of an adaptive strategy is envisioned, drawing inspiration from observer-based output tracking control methods for managing unmeasurable state variables, as demonstrated in references [30,31]. In addition, in [32] future VOLUME XX, 2023 directions involve extending the study to switched neural networks with time-varying delays.…”

Section: Discussionmentioning

confidence: 99%

“…In this subsection, for the TSFM ( 26)-( 27), using LMI characterization, an 𝐻 2 performance is presented from the disturbance w to the output 𝑧. Lemma 2: For the nonlinear system described by TSFM dynamics ( 26)-( 27), the following statements are equivalent: a) ∃𝐾 𝑗 , 𝑗 = 1, … , 𝑟, such that 𝐴 𝑖 + 𝐵 where 𝑌 𝑗 = 𝐾 j 𝑋. From the Schur complement, (42) is equivalent to (30). The proof is completed.…”

Section: Iiiii Optimal Sliding Gain Design Using Generalized Partial ...mentioning

confidence: 99%

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Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

Soltanian,

Shasadeghi,

Mobayen

et al. 2024

IEEE Access

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This paper introduces an innovative optimal control approach to achieve output tracking while incorporating 𝐻 2 -performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying 𝐻 2 -performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems.

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Section: Discussionmentioning

confidence: 99%

Section: Iiiii Optimal Sliding Gain Design Using Generalized Partial ...mentioning

confidence: 99%

Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

Soltanian,

Shasadeghi,

Mobayen

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sliding mode control is a well-established approach in robust control theory, particularly for handling known but bounded-matched uncertainty [8][9][10]. To extend its applicability to scenarios with unknown upper bounds [11,12], adaptive methods have been explored for estimating uncertainty boundaries [13][14][15]. An example is presented in [16], which introduces an adaptive SMC design for a nonlinear suspension system with unknown bound uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optimal Terminal Sliding Mode Control for T-S Fuzzy-Based Nonlinear Systems

Soltanian,

Valadbeigi,

Tavoosi

et al. 2024

Complexity

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This study utilizes the Takagi–Sugeno fuzzy model to represent a subset of nonlinear systems and presents an innovative adaptive approach for optimal dynamic terminal sliding mode control (TSMC). The systems under consideration encompass bounded uncertainties in parameters and actuators, as well as susceptibility to external disturbances. Performance evaluation entails the design of an adaptive terminal sliding surface through a two-step process. Initially, a state feedback gain and controller are developed using Linear Matrix Inequality (LMI) techniques, grounded on H2-performance and partial eigenstructure assignment. Dynamic sliding gain is subsequently attained via convex optimization, leveraging the derived state feedback gain and the designed terminal sliding mode (TSM) controller. This approach diverges from conventional methods by incorporating control effort and estimating actuator uncertainty bounds, while also addressing sliding surface and TSM controller design intricacies. The TSM controller is redefined into a strict feedback form, rendering it suitable for addressing output-tracking challenges in nonlinear systems. Comparative simulations validate the effectiveness of the proposed TSM controller, emphasizing its practical applicability.

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Temperature Control In A Rotary Hearth Furnace Using AI

Badea,

Mureşan,

Abrudean

et al. 2024

2024 International Conference on Electrical, Computer and Energy Technologies (ICECET

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Adaptive locally-linear-models-based fault detection and diagnosis for unmeasured states and unknown faults

Cited by 4 publications

References 14 publications

Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

Adaptive Optimal Terminal Sliding Mode Control for T-S Fuzzy-Based Nonlinear Systems

Temperature Control In A Rotary Hearth Furnace Using AI

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