A Critical Review of Control Techniques for Flexible and Rigid Link Manipulators

Alandoli, Esmail Ali; Lee, Tian Soon

doi:10.1017/s0263574720000223

Cited by 39 publications

(12 citation statements)

References 204 publications

(195 reference statements)

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“…Schematic of rigid link manipulator [65] on the classical Lagrangian technique which the model was used in robustness test of H 2 controller, H ∞ controller, integrated controller of H 2 ∕H ∞ , and -synthesis controller [75]. The Lagrange's equation was applied in Ref.…”

Section: Lagrangian Methodsmentioning

confidence: 99%

A critical review of modelling methods for flexible and rigid link manipulators

Lee

Alandoli

2020

J Braz. Soc. Mech. Sci. Eng.

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Mathematical modelling plays an important role for robotic manipulators in order to design their particular controllers. Also, it is hard challenge to obtain an accurate mathematical model or obtain a suitable modelling method in such vast field. Thus, this critical review is advantageous and indispensable for researchers who are interested in the area to gain fruitful knowledge on the mathematical modelling methods. This paper is classified based on the type of robotic manipulators such as flexible link manipulators (FLMs), rigid link manipulators (RLMs) and hybrid manipulators which involves rigid links and flexible links. The used modelling methods for FLMs are the assumed mode method, the finite element method, and the lumped parameter method as approximation techniques which are well explained and reviewed. The Lagrangian method has inclusive explanation and review which is widely participated for obtaining the dynamic equations of FLMs, and it is appropriate and commonly employed for modelling RLMs. The Newtonian method, the forward kinematic, and the inverse kinematic are also well discussed and reviewed which are suitable and commonly employed for modelling RLMs. The critical discussion of 170 articles reported in this paper guides researchers to select the suitable method for modelling. This paper reviews the published articles in the period of 2010-2020 except for few older articles for the need of providing essential theoretical knowledge. The advantages and disadvantages of each method are well summarized at the end of the paper. The intelligent identification methods are briefly discussed due to the lack of publications especially on the period of 2010-2020.

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Section: Lagrangian Methodsmentioning

confidence: 99%

A critical review of modelling methods for flexible and rigid link manipulators

Lee

Alandoli

2020

J Braz. Soc. Mech. Sci. Eng.

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“…The passive control method is to eliminate the resonant influence of the flexible manipulators by adding a notch device. The active control methods mainly include PID (Proportional Integral Differentiation) control [12,13], sliding mode control [14,15], robust control [16,17], etc. In literature [13], the nonlinear self-tuning PID controller is used to control the flexible manipulators.…”

Section: Introductionmentioning

confidence: 99%

Control Method of Flexible Manipulator Servo System Based on a Combination of RBF Neural Network and Pole Placement Strategy

Shang

Yin

et al. 2021

Mathematics

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Gravity and flexibility will cause fluctuations of the rotation angle in the servo system for flexible manipulators. The fluctuation will seriously affect the motion accuracy of end-effectors. Therefore, this paper adopts a control method combining the RBF (Radial Basis Function) neural network and pole placement strategy to suppress the rotation angle fluctuations. The RBF neural network is used to identify uncertain items caused by the manipulator’s flexibility and the time-varying characteristics of dynamic parameters. Besides, the pole placement strategy is used to optimize the PD (Proportional Differential) controller’s parameters to improve the response speed and stability. Firstly, a dynamic model of flexible manipulators considering gravity is established based on the assumed mode method and Lagrange’s principle. Then, the system’s control characteristics are analyzed, and the pole placement strategy optimizes the parameters of the PD controllers. Next, the control method based on the RBF neural network is proposed, and the Lyapunov stability theory demonstrates stability. Finally, numerical analysis and control experiments prove the effectiveness of the control method proposed in this paper. The means and standard deviations of rotation angle error are reduced by the control method. The results show that the control method can effectively reduce the rotation angle error and improve motion accuracy.

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“…Integrated controllers which consist of more than on control technique were also used for position control of 2-DOF robots such as FLC-SMC 28,29 , FLC-PID 30 , and FLC-NN 31 . There are many other control techniques were critically discussed in the literature 32 .…”

Section: Introductionmentioning

confidence: 99%

2-DOF robot modelling by SimMechanics and PD-FL integrated controller for position control and trajectory tracking

2021

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Background Due to the high demand of robots to perform several industrial tasks, such as welding, machining, pick and place, position control in robotics has attracted high attention recently. Controllers’ improvement is also continuous specifically in terms of design simplicity and performance accuracy. This research plans to obtain the SimMechanics model of a two-degree of freedom (DOF) robot and to propose an integrated controller of a proportional–derivative (PD) controller and a fuzzy logic (FL) controller. Methodology The SimMechanics model of the 2-DOF robot is obtained using MATLAB SimMechanics toolbox from a CAD assembly design of the 2-DOF robot. Then, the proposed PD-FL integrated controller is designed and simulated in MATLAB Simulink. The PD controller is widely used for its simplicity, but it doesn’t have a satisfactory performance in difficult tasks. Furthermore, the FL controller is also easy for design and implementation even by non-experts in control theory, but it has the disadvantage of long computational time for multi-input systems due to the increased fuzzy rules. Results The FL controller is integrated with the PD controller for enhanced performance of the 2-DOF robot. The PD-FL integrated controller is developed and tested to control the 2-DOF robot for point-to-point position control and also tip trajectory tracking (TTT) such as triangular TTT and rhombic TTT. Conclusion The PD-FL integrated controller demonstrates enhanced performance compared to the conventional PD controller in both point-to-point position control and TTT. Furthermore, the PD-FL integrated controller has the advantage of less fuzzy rules which helps to overcome the computational time issue of the FL controller.

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A Critical Review of Control Techniques for Flexible and Rigid Link Manipulators

Cited by 39 publications

References 204 publications

A critical review of modelling methods for flexible and rigid link manipulators

A critical review of modelling methods for flexible and rigid link manipulators

Control Method of Flexible Manipulator Servo System Based on a Combination of RBF Neural Network and Pole Placement Strategy

2-DOF robot modelling by SimMechanics and PD-FL integrated controller for position control and trajectory tracking

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