Rotational Inverted Pendulum Controller Design using Indirect Adaptive Fuzzy Model Predictive Control

Eini, Roja; Abdelwahed, Sherif

doi:10.1109/fuzz-ieee.2019.8859014

Cited by 14 publications

(6 citation statements)

References 7 publications

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“…To solve the problem of 𝜑 1 (x 1 , 𝑘) and 𝜑 2 (x 1 , x 2 , 𝑘) , we approximate them by a two-interval type-2 fuzzy adaptive systems. A fuzzy system that uses fuzzy type 2 sets and inference is a fuzzy type 2 system [10][11][12][13][14][15][16]. Type-1 fuzzy set has a crisp membership degree, while type-2 fuzzy set (T2FS) has a fuzzy membership degree.…”

Section: Interval Type-2 Fuzzy Adaptive Controlmentioning

confidence: 99%

“…Due to the nonlinearity, it is difficult to formulate an accurate mathematical model. Therefore, to improve the behavior of adaptive controllers through identifying the unknown nonlinear functions, intelligent approaches such as, artificial neural networks and fuzzy logic [8][9][10][11] have been addressed.…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive fuzzy control is mainly categorized into two classes, direct adaptive fuzzy control that its parameters are regulated via the adaptation mechanism [10], while indirect adaptive fuzzy control that its parameters are regulated via the estimated plant based on fuzzy system [11]. In addition, based on the (T-S) models for approximating uncertain nonlinear system in the fuzzy IF-THEN rules, the design of an adaptive fuzzy feedback controller for uncertain nonlinear systems has been reported [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel Hybrid Interval Type-2 Fuzzy Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems

Boukhalfa¹,

Khettab²,

Essounbouli³

2021

JESA

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A Novel hybrid backstepping interval type-2fuzzy adaptive control (HBT2AC) for uncertain discrete-time nonlinear systems is presented in this paper. The systems are assumed to be defined with the aid of discrete equations with nonlinear uncertainties which are considered as modeling errors and external unknown disturbances, and that the observed states are considered disturbed. The adaptive fuzzy type-2 controller is designed, where the fuzzy inference approach based on extended single-input rule modules (SIRMs) approximate the modeling errors, non-measurable states and adjustable parameters are estimated using derived weighted simplified least squares estimators (WSLS). We can prove that the states are bounded and the estimation errors stand in the neighborhood of zero. The efficiency of the approach is proved by simulation for which the root mean squares criteria are used which improves control performance.

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Section: Interval Type-2 Fuzzy Adaptive Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Hybrid Interval Type-2 Fuzzy Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems

Boukhalfa¹,

Khettab²,

Essounbouli³

2021

JESA

View full text Add to dashboard Cite

show abstract

“…In a smart building, after the equipment information is collected through sensors and meters, a library of benchmark data is applied, analytics are performed and potential actions toward performance improvement are identified [3]. To automate insights into actions as they optimize assets with IoT, we can utilize the predictive analytics to artificial intelligence or learning systems [4], [5]. By taking advantage of predictive algorithms, building owners can significantly cut energy consumption and achieve ambitious cost-saving targets.…”

Section: Smart Building and Challengesmentioning

confidence: 99%

“…As a matter of fact, by applying innovative IOT and AI embedded platforms in today smart buildings, the occupants satisfaction is improved significantly. Moreover, the new smart buildings make it possible to radically reduce costs through management, automation, and optimization of operations in a smart environment [1][2][3][4][5][6][7]. A comprehensive building management system leverages all aspects of building facilities; e.g., [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Secure Controls for Smart Cities; Applications in Intelligent Transportation Systems and Smart Buildings

Hyman¹,

Alisha²,

Gordon³

2019

IJSEA

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Internet of Things (IoT) is an emerging concept in smart infrastructures describing a wide ecosystem where interconnected devices and services collect, exchange and process data in order to adapt dynamically to a context. IoT is tightly bound to cyberphysical systems and in this respect is an enabler of Smart Infrastructures by enhancing their quality of service provisioning. Smart infrastructure, enabled by technologies like IoT, offer numerous of advantages bringing serious cost savings and efficiencies. These kinds of data-driven environments, fueled by connected devices and network connectivity, become a new attack surface for cyber threats. Cyber security approaches develop guidance to secure IoT and Smart Infrastructures from cyber threats, by highlighting good security practices and proposing recommendations to operators, manufacturers and decision makers. In this paper, cyber security approaches are analyzed in a smart city infrastructure.

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Enhancing stability control of inverted pendulum using Takagi–Sugeno fuzzy model with disturbance rejection and input–output constraints

Nguyen,

Dong,

BUI

2023

Sci Rep

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The Takagi–Sugeno (T–S) fuzzy model is a versatile approach widely used in system control, often in combination with other strategies. This paper addresses key control challenges linked to the T–S system and presents important considerations to ensure its successful application using the Lyapunov theorem. One crucial aspect is determining the optimal number of premise variables and selecting accurate fuzzy rules for the T–S model. Additionally, the theorem based on Linear Matrix Inequality (LMI) is developed to enable effective disturbance rejection. To enhance stability control, constraints are imposed on the output angle and control input of a rotary inverted pendulum (RIP). By integrating T–S fuzzy control, disturbance rejection, and input/output constraints, robust stability in controlling the RIP is achieved. Extensive simulations are performed to showcase the efficiency of the suggested method, and the simulation results are thoroughly discussed and analyzed to verify the efficacy of the control method.

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Rotational Inverted Pendulum Controller Design using Indirect Adaptive Fuzzy Model Predictive Control

Cited by 14 publications

References 7 publications

Novel Hybrid Interval Type-2 Fuzzy Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems

Novel Hybrid Interval Type-2 Fuzzy Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems

Secure Controls for Smart Cities; Applications in Intelligent Transportation Systems and Smart Buildings

Enhancing stability control of inverted pendulum using Takagi–Sugeno fuzzy model with disturbance rejection and input–output constraints

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