A hybrid Fuzzy approach of different controllers to stabilize a 3-link swinging robotic (Robogymnast)

Samad, Bdereddin Abdul; Mohamed, Mahmoud; Anavi, Fatih; Melikhov, Yevgen

doi:10.1109/icacite53722.2022.9823768

Cited by 6 publications

(8 citation statements)

References 18 publications

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“…Moreover, although rising time was quicker for the in first optimised technique comparing with PSO, at approximately, 0.21, 0.03 and 0.01 seconds, as well as the settling time is quicker. Upon comparing the current work with previous works in [1], [17] the differences between the tuned and previous approaches can be observed. The effectiveness of TLBO in minimizing the error to optimize the hybrid control proposal for greater system stabilization is also evident.…”

Section: B Discussionmentioning

confidence: 90%

“…Earlier studies have provided extensive information on independent swing-up motion for triple-link robots, and especially for robots using a single unactuated and 2 actuated joints, including for example those reported in [11][12][13][14][15] The main focus of such work has been on designing and implementing various types of controllers to operate the Robogymnast, which is a triple-link system with a pendulum action. In addition, these studies have investigated the problem of stabilising multiple-link robots using swinging mechanisms, again through different controller designs, : e.g., LQR [1] PID [16] Fuzzy-PD [17] and FLQR [18] and LQR-PID [19]. Moreover, prior work has investigated problems of motion in people who have restricted or impaired limb motion due to injury or disability [20].…”

Section: Related Workmentioning

confidence: 99%

“…The hybrid FLQR controller developed here is a combination of an optimised LQR control and a rules-based fuzzy control system [16]. FLC systems centrally utilise Fuzzy Control Rules (FCRs) linked together with fuzzy implications as well as the compositional rule of inference key component of an FLC system is a set of Fuzzy Control Rules (FCRs) that are connected through a fuzzy implication and the compositional rule of inference [17]. Here, the design process to develop controllers based on fuzzy logic is discussed, and this is frequently applied supplementally to bring improvements in conventional controllers to respond to dynamic conditions.…”

Section: Control Designmentioning

confidence: 99%

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Enhanced the Control Strategy of a Triple Link Robotic System (Robogymnast)

2023

Self Cite

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This research aims to optimise performance in a Fuzzy Linear Quadratic Regulator controller by implementing 2 types of algorithm to stabilise the triple-link 'Robogymnast' robotic system. The Robogymnast recreates the movements of a human gymnast, with the hand/arm element of the system securely attached to a high bar with ball0bearing mountings, which can rotate freely. This article explores the complex factors involved in swing-up control in the underactuated Robogymnast and presents a linearisation of the mathematical modelling for the system, considering methods which apply Lagrangian equations in defining state space in such systems. Both the Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithm were used for tuning the hybrid Fuzzy Linear Quadratic Regulator controller for later application with the robot and assessment of response stability. In addition, MATLAB Simulink was used to simulate performance and show the effect of altering parameters including time for rising, settling and overshoot. This work aims primarily to explore how a Linear Quadratic Regulator/Fuzzy Logic controller can be applied with acrobatic robots.

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Section: B Discussionmentioning

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Control Designmentioning

confidence: 99%

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Enhanced the Control Strategy of a Triple Link Robotic System (Robogymnast)

2023

Self Cite

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“…The multivariate nature of LQR allows for simultaneous control of displacement angles across the 3-link inverted pendulum [3]. LQR was chosen based on its capacity to deal with significant disturbance events and keep systems stable with no reductions in operational performance [14][15][16][17].…”

Section: Pid Controllermentioning

confidence: 99%

Analysing Various Control Technics for Manipulator Robotic System (Robogymnast)

Mahmoud¹,

Samad²,

Anayi³

et al. 2023

Computers, Materials &Amp; Continua

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The Robogymnast is a highly complex, three-link system based on the triple-inverted pendulum and is modelled on the human example of a gymnast suspended by their hands from the high bar and executing larger and larger upswings to eventually rotate fully. The links of the Robogymnast correspond respectively to the arms, trunk, and lower limbs of the gymnast, and from its three joints, one is under passive operation, while the remaining two are powered. The passive top joint poses severe challenges in attaining the smooth movement control needed to operate the Robogymnast effectively. This study assesses four types of controllers used for systems operation and identifies how far response stabilisation is achieved with each. The system is simulated using MATLAB Simulink, with findings generated regarding rising and settling time, as well as overshoot. The research primarily seeks to examine the application of a linear quadratic regulator controller, proportionalintegral-derivative controller, fuzzy linear quadratic regulator controller and linear quadratic regulator-proportional-integral-derivative controller for this type of system and comparisons between the different controllers to demonstrate successful performance, which highlights the claimed advantages of the proposed system.

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“…Brehm and Rattan [11] presented a hybrid fuzzy PID controller that takes advantage of the properties of the fuzzy PI and PD controllers. Samad et al [12] proposed a fuzzy logic controller tuned for both a Linear Quadratic Regulator (LQR) and a proportional derivative (PD) controller, which is applied in stabilizing a multi-link robotic system. Paramasivam and Arumugam [13] show that a hybrid controller for switched reluctance motor (SRM) drive has fast tracking capability, less steady-state error, and is robust to load disturbance.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy Controller Design for Position Control System

Noaman¹

2023

MMEP

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A linear control system can be controlled by using the conventional controller after formulating a mathematical model of the system and finding its transfer function. The fuzzy logic controller enables the representation of human information pertaining to the system's control, manipulation, and execution. The developer of a controller must establish the rules, and the information may come from the human controller or from an understanding of the plant dynamics. The expansion in the rules leads the fuzzy controller to take efficient decisions and to be very strong for controlling the plant, besides the ability to control the nonlinearity that occurs in the plant. In this paper, the transfer function and response to a unit-step input of a closed-loop position control system were derived. The response of the system was improved by the design and simulation of a PD controller. Then, the formulation and construction of four fuzzy controllers gradually increase in the rule base 9, 25, 47, and 81. Finally, design and simulate a hybrid fuzzy controller. The system was simulated using MATLAB Simulink, examining a range of factors such as the rise, settling time, and overshoot. The implementation of a hybrid-fuzzy controller provides the benefits of the fuzzy controller and PD controller. This means that the hybrid fuzzy has a better transient response than the uncontrolled system, a system with PD controller, and a fuzzy controller.

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A hybrid Fuzzy approach of different controllers to stabilize a 3-link swinging robotic (Robogymnast)

Cited by 6 publications

References 18 publications

Enhanced the Control Strategy of a Triple Link Robotic System (Robogymnast)

Enhanced the Control Strategy of a Triple Link Robotic System (Robogymnast)

Analysing Various Control Technics for Manipulator Robotic System (Robogymnast)

Hybrid Fuzzy Controller Design for Position Control System

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