Design of a Fuzzy Fractional Order Adaptive Impedance Controller with Integer Order Approximation for Stable Robotic Contact Force Tracking in Uncertain Environment

Cao, Hongli

doi:10.2478/ama-2022-0003

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…In practice, however, the dynamic and complex uncertainty of the external physical environment and the difficulty of predicting environmental parameters in advance make impedance control systems often perform poor force tracking characteristics, stability and robustness. To solve this problem, a series of admittance control algorithms are proposed, such as environment identification (Xu, 2016; Seraji and Colbaugh, 1997), adaptive control (Cao et al , 2019; Duan et al , 2018; Jung et al , 2004), neural networks (Hamedani et al , 2021; Peng et al , 2019; Jung and Hsia, 2010; Liang et al , 2019), and fuzzy logic (Liu et al , 2022; Cao, 2022; Li et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al (2020) investigated the effect of the inertia and damping terms in the conventional impedance equation when they are of fractional order on the stability and robustness of the system, proposed a fractional-order impedance control strategy with faster response, lower overshoot and good tracking performance and verified the stability of this control algorithm theoretically. In Cao (2022), the authors proposed a fuzzy fractional-order admittance controller based on percentages, which has greater stability and robustness with respect to the classical integer-order impedance controller. However, the above fractional-order admittance algorithms have difficulty in showing good force tracking performance in highly dynamic changing environments (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive fractional-order admittance control for force tracking in highly dynamic unknown environments

et al. 2023

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Purpose With the increasing demands of industrial applications, it is imperative for robots to accomplish good contact-interaction with dynamic environments. Hence, the purpose of this research is to propose an adaptive fractional-order admittance control scheme to realize a robot–environment contact with high accuracy, small overshoot and fast response. Design/methodology/approach Fractional calculus is introduced to reconstruct the classical admittance model in this control scheme, which can more accurately describe the complex physical relationship between position and force in the interaction process of the robot–environment. In this control scheme, the pre-PID controller and fuzzy controller are adopted to improve the system force tracking performance in highly dynamic unknown environments, and the fuzzy controller is used to improve the trajectory, transient and steady-state response by adjusting the pre-PID integration gain online. Furthermore, the stability and robustness of this control algorithm are theoretically and experimentally demonstrated. Findings The excellent force tracking performance of the proposed control algorithm is verified by constructing highly dynamic unstructured environments through simulations and experiments. In simulations and experiments, the proposed control algorithm shows satisfactory force tracking performance with the advantages of fast response speed, little overshoot and strong robustness. Practical implications The control scheme is practical and simple in the actual industrial and medical scenarios, which requires accurate force control by the robot. Originality/value A new fractional-order admittance controller is proposed and verified by experiments in this research, which achieves excellent force tracking performance in dynamic unknown environments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive fractional-order admittance control for force tracking in highly dynamic unknown environments

et al. 2023

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“…A fuzzy fractional order (FO) adaptive impedance controller was presented on the robot manipulator to prevent force overshoots in the contact stage while keeping force error in the dynamic tracking stage where conventional control methods are ineffective [10]. The simulation results showed that the suggested controller can be designed to be more stable and superior to the general impedance controller and the force tracking results have also been compared to earlier control methods.…”

Section: Introductionmentioning

confidence: 99%

Fractional Order Fuzzy Like PID Controller Design for Three Links Rigid Robot Manipulator

2022

IJCCCE

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Four Fractional/Integer Order Fuzzy Proportional Integral Derivative controller structures are designed in this study to successfully control a nonlinear, coupled, multi-input, multi-output, three-link rigid robotic manipulator system. The performance of Fractional Order Fuzzy Proportional Integral Derivative and Integer Order Fuzzy Proportional Integral Derivative controllers is evaluated for reference trajectory tracking, changing beginning circumstances, disturbance rejection, and model uncertainty. These controllers' parameters are tuned using a meta-heuristic optimization approach called the most valuable player algorithm for the objective function, which is defined as the integral of the time-squared error. Simulation results show that the suggested Fractional Order Fuzzy Proportional Integral Derivative controllers outperform Integer Order Fuzzy Proportional Integral Derivative controllers for tracking performance, stability, and robustness for all structures. Fractional Order Fuzzy Proportional Derivative Fractional Order Proportional Integral Derivative controller is the best one for trajectory tracking, disturbances rejection, and parameter variation with the least integral of time square error equal to 2.7420×10-6, 3.4×10-3 and 2.0108×10-4 respectively and the response of the angular position for all links for trajectory tracking has minimum settling time which is equal to 0.0290 s for the first link, 0.0160 s for the second link and 0.0050 s for the third link. When the initial condition is changed, the One Block Fractional Order Fuzzy Proportional Integral Derivative controller is the best one, since the integral of time square error is minimum and equal to 1.6253×10-4. Index Terms— Fractional order controller, Fuzzy logic, Most valuable player algorithm, PID controller, Robotic manipulator.

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Coot optimization algorithm-tuned neural network-enhanced PID controllers for robust trajectory tracking of three-link rigid robot manipulator

Mohamed,

Oleiwi,

Azar

et al. 2024

Heliyon

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Design of a Fuzzy Fractional Order Adaptive Impedance Controller with Integer Order Approximation for Stable Robotic Contact Force Tracking in Uncertain Environment

Cited by 3 publications

References 34 publications

Adaptive fractional-order admittance control for force tracking in highly dynamic unknown environments

Adaptive fractional-order admittance control for force tracking in highly dynamic unknown environments

Fractional Order Fuzzy Like PID Controller Design for Three Links Rigid Robot Manipulator

Coot optimization algorithm-tuned neural network-enhanced PID controllers for robust trajectory tracking of three-link rigid robot manipulator

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