Fuzzy adaptive state-feedback control for a revolute-prismatic-revolute robot manipulator

Nejadkourki, N.; Mahmoodabadi, Mohammad Javad

doi:10.1080/23311916.2019.1698690

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…The adaptive robust PID controller subjected to the sliding surface was originally introduced, and its stability was mathematically proved by Chang and Yan (2005). It was used to use the advantages of sliding mode control as a robust controller and PID for being straight forward, simple, and applicable; therefore, they were used in accordance to each other (Mahmoodabadi and Khoobroo Haghbayan, 2019; Nejadkourki and Mahmoodabadi, 2019; Mahmoodabadi and Shahangian, 2019; Mahmoodabadi et al, 2015). The original AR-PID was presented for chaotic systems and to avoid the chattering problem of the sliding mode control (Chang and Yan, 2005).…”

Section: Proposed Controllermentioning

confidence: 99%

Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor

Mahmoodabadi

Babak

2020

Journal of Vibration and Control

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Proportional–integral–derivative is one of the most applicable control methods in industry. Although it is simple and effective in most cases, it does not provide robustness against disturbances and may not perform well in cases with uncertainties and nonlinearities. In this study, a fuzzy adaptive robust proportional–integral–derivative controller is used to control a nonlinear 4 degree-of-freedom quadrotor. An adaptation mechanism is submitted to the proportional–integral–derivative controller for updating the proportional, derivative, and integral gains of proportional–integral–derivative control. Furthermore, a sliding surface is generated and submitted to the adaptation mechanism for better regulation of proportional–integral–derivative gains. Afterward, a fuzzy engine is applied to regulate the sliding surface for better performance of the adaptive proportional–integral–derivative when there are disturbance and uncertainties. The multi-objective grasshopper optimization algorithm is implemented on the control system for the regulation of the control system parameters to minimize the error and control effort of the proposed hybrid control system. Finally, the obtained results are presented for a nonlinear 4 degree-of-freedom multi-purpose (for marine, ground, and aerial maneuvers) quadrotor system designed and built in Sirjan University of Technology, Sirjan, Iran, to assure the effectiveness of this technique.

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Section: Proposed Controllermentioning

confidence: 99%

Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor

Mahmoodabadi

Babak

2020

Journal of Vibration and Control

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“…Event-triggered composite adaptive fuzzy laws were developed to solve this problem. In addition, Nejadkourki and Mahmoodabadi [25] developed a fuzzy adaptive state-feedback control scheme for a revolute-prismatic-revolute (RPR) robot manipulator. Fuzzy-logic-based control theories offer several advantages for nonlinear chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Fuzzy Fractional Control for Nonlinear Chaotic Systems with Uncertainties

2023

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The control of nonlinear chaotic systems with uncertainties is a challenging problem that has attracted the attention of researchers in recent years. In this paper, we propose a robust adaptive fuzzy fractional control strategy for stabilizing nonlinear chaotic systems with uncertainties. The proposed strategy combined a fuzzy logic controller with fractional-order calculus to accurately model the system’s behavior and adapt to uncertainties in real-time. The proposed controller was based on a supervised sliding mode controller and an optimal robust adaptive fractional PID controller subjected to fuzzy rules. The stability of the closed-loop system was guaranteed using Lyapunov theory. To evaluate the performance of the proposed controller, we applied it to the Duffing–Holmes oscillator. Simulation results demonstrated that the proposed control method outperformed a recently introduced controller in the literature. The response of the system was significantly improved, highlighting the effectiveness and robustness of the proposed approach. The presented results provide strong evidence of the potential of the proposed strategy in a range of applications involving nonlinear chaotic systems with uncertainties.

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Optimal adaptive state feedback control for a three degree-of-freedom series-type double inverted pendulum system by using particle swarm optimisation

Mahmoodabadi

Nejadkourki

2022

Australian Journal of Mechanical Engineering

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Fuzzy adaptive state-feedback control for a revolute-prismatic-revolute robot manipulator

Cited by 5 publications

References 22 publications

Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor

Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor

Robust Adaptive Fuzzy Fractional Control for Nonlinear Chaotic Systems with Uncertainties

Optimal adaptive state feedback control for a three degree-of-freedom series-type double inverted pendulum system by using particle swarm optimisation

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