Robust Fuzzy Observer-Based Speed Control of Electric Vehicle with Unmeasurable Premise Variables

Echreshavi, Zeinab; Shasadeghi, Mokhtar; Asemani, Mohammad Hassan

doi:10.1109/iccia54998.2022.9737171

Cited by 3 publications

(13 citation statements)

References 19 publications

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“…Remark 2: In Theorem 1, the UBS condition of the uncertain TSFM due to the external disturbance is derived due to a new LMI-based optimization problem. Compared with Echreshavi et al (2022b), Esfahani (2015), and Nguang et al (2005), the controller gains are derived solving a one-stage LMI. In other words, it is not necessary to solve an iterative LMI.…”

Section: Resultsmentioning

confidence: 99%

“…The feasibility region of the suggested offline optimization problem is wider than the proposed methods in Echreshavi et al (2022b), Nachidi and Hajjaji (2012) which were based on the BMI problem and solved in an iterative scheme. Moreover, the closed-loop performance of the considered system is studied under the proposed controller and the robust two-step control approaches (Echreshavi et al, 2022b) with initial condition x 0 ð Þ = À5 1 ½ T and a 1 = 0:6, a 2 = 0:7. The design parameters of the proposed controller are selected as a 1 = a 2 = a 3 = 0:3.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Moreover, against the BMI-based optimization problem (Esfahani, 2015; Nguang et al, 2005), the proposed method is solved based on LMI problem. Therefore, the proposed robust controller is lees conservatism in comparison with Echreshavi et al (2022b), Esfahani (2015), Nguang et al (2005). Indeed, the provided LMI condition is derived by considering the limitation on the amplitude on the control signal using the TSFM of the actuator saturation.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, the provided LMI condition is derived by considering the limitation on the amplitude on the control signal using the TSFM of the actuator saturation. Moreover, the conservatism of the proposed LMI problem is less than the quadratic Lyapunov function-based fuzzy controller (Echreshavi et al, 2022b; Shen et al, 2020). The reason is that all LMI conditions must be satisfied with only one positive matrix (Echreshavi et al, 2022b; Shen et al, 2020) that increases the conservatism of the LMI problem.…”

Section: Resultsmentioning

confidence: 99%

“…Min-max MPC (Teng et al, 2018) Yes Yes Two-step control approach (Echreshavi et al, 2022b;Nachidi and Hajjaji, 2012) No No Event-triggered fuzzy scheme (Wang and Yang, 2019) No No The proposed method No Yes MPC: model predictive control.…”

Section: Controller Online Computation Constrained Performancementioning

confidence: 99%

See 4 more Smart Citations

Relaxed nonquadratic H∞ robust constrained event-triggered fuzzy controller design for discrete-time nonlinear systems

Farbood

Echreshavi

Shasadeghi

2023

Transactions of the Institute of Measurement and Control

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This paper introduces an [Formula: see text] robust constrained event-triggered fuzzy controller for discrete-time nonlinear systems in the presence of system parameter uncertainties, the parameter uncertainties of the output matrices and the disturbances, simultaneously. To reduce the communication resources and improve the robust performance, the event-triggered mechanism is incorporated with the [Formula: see text] robust fuzzy controller design. Moreover, by employing the fuzzy modeling of the actuator saturation and a nonquadratic Lyapunov function (NQLF), the ultimate bounded stability (UBS) of the closed-loop system is guaranteed subject to the amplitude limitations of the control signal. This means, a new linear matrix inequality (LMI) problem is derived to ensure the robustness and constraints in an offline scheme based on the event-triggered strategy. Besides, to meet the practical problem to further improve the controlled system performance, the control input rate constraint is also considered and a new LMI condition is provided with no online computational burden. Therefore, the computational burden of the proposed controller is greatly reduced with less conservatism. Two simulation examples including a numerical dynamic system and a truck–trailer system are employed to illustrate the less conservatism and computational complexity of the proposed method compared with the existing approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Controller Online Computation Constrained Performancementioning

confidence: 99%

See 3 more Smart Citations

Relaxed nonquadratic H∞ robust constrained event-triggered fuzzy controller design for discrete-time nonlinear systems

Farbood

Echreshavi

Shasadeghi

2023

Transactions of the Institute of Measurement and Control

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Bio-Inspired Algorithm for Speed Control of Electric Vehicle

Rameshkumar¹,

Catherine²,

Sudhakar³

et al. 2022

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)

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Intelligent Controller Design for Electric Vehicle(EV) Speed Control Using An Inverse Model Based Reference Model Learning Algorithm

Bulut

2024

Preprint

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The fuzzy rules are knowledge base of a the fuzzy controller which are determined by using the system internal structure and experience information representing the behavior of the system. However, there is a need for automatic determination methods for the extraction of fuzzy rules, since the experience knowledge will be insufficient for systems with nonlinear or variable behavior. In areas where the control parameters vary, an intelligent system can be developed that uses the adaptive fuzzy method to find the coefficients of the controller in the system to overcome this. In this study, an adaptive fuzzy controller is designed using a learning-based fuzzy inverse model to provide speed control of the electric vehicle. The results obtained in the study showed that the designed inverse model learning-based fuzzy controller model is applicable for the dc motor that will provide speed control of electric vehicles. In the simulation of this model, system behavior has been investigated by way of use constant and variable loads. The obtained results remark that the proposed method satisfies system stability in terms of electric vehicle speed control.

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Robust Fuzzy Observer-Based Speed Control of Electric Vehicle with Unmeasurable Premise Variables

Cited by 3 publications

References 19 publications

Relaxed nonquadratic H∞ robust constrained event-triggered fuzzy controller design for discrete-time nonlinear systems

Relaxed nonquadratic H∞ robust constrained event-triggered fuzzy controller design for discrete-time nonlinear systems

Bio-Inspired Algorithm for Speed Control of Electric Vehicle

Intelligent Controller Design for Electric Vehicle(EV) Speed Control Using An Inverse Model Based Reference Model Learning Algorithm

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