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Backstepping Adaptive Fuzzy Control for two-link robot manipulator  [PDF]
Yongqiao Wei,Jingdong Zhang,Li Hou,Fenglan Jia
International Journal of Computer Science Issues , 2013,
Abstract: In this paper, based on hyapunov method, a backstepping adaptive fuzzy control scheme is presented for the two-link robot manipulator system. The control strategy consists of the traditional backstepping control and adaptive fuzzy control to cope with the model unknown and parameter disturbances.The simulation is presented to verify the effectiveness of the proposed control scheme. From the simulation results ,fast response, strong robustness, good disturbance rejection capability and good angle tracking capability can be obtained. The output tracking error between the actual position output and the desired position output can asymptotically converge to zero. It is also revealed from simulation results that the proposed control strategy is valid for the two-link robot manipulator.
Adaptive Neuro-fuzzy Inference System Based Control of puma 600 Robot Manipulator
Ouamri Bachir,Ahmed-Foitih Zoubir
International Journal of Electrical and Computer Engineering , 2012, DOI: 10.11591/ijece.v2i1.116
Abstract: The strong dependence of the computed torque control of dynamic model of the robot manipulator makes this one very sensitive to uncertainties of modelling and to the external disturbances. In general, the vector of Coriolis torque, centrifugal and gravity is very complicated, consequently, very difficult to modelled. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). This paper presents the control of puma 600 robot arm using Adaptive Neuro Fuzzy Inference System (ANFIS) based computed torque controller (type PD). Numerical simulation using the dynamic model of puma 600 robot arm shows the effectiveness of the approach in improving the computed torque method. Comparative evaluation with Fuzzy computed torque (type PD) control is presented to validate the controller design. The results presented emphasize that a satisfactory trajectory tracking precision and stabilility could be achieved using ANFIS controller than Fuzzy controller. Keywords: Fuzzy computed torque control, Robot control, Adaptive neuro-fuzzy inference system (ANFIS).
Novel Robot Manipulator Adaptive Artificial Control: Design a Novel SISO Adaptive Fuzzy Sliding Algorithm Inverse Dynamic Like Method
Farzin Piltan, N. Sulaiman, Hajar Nasiri, Sadeq Allahdadi, Mohammad A. Bairami
International Journal of Engineering , 2011,
Abstract: Refer to the research, design a novel SISO adaptive fuzzy sliding algorithm inverse dynamic like method(NAIDLC) and application to robot manipulator has proposed in order to design high performance nonlinearcontroller in the presence of uncertainties. Regarding to the positive points in inverse dynamic controller,fuzzy logic controller and self tuning fuzzy sliding method, the output has improved. The main objective inthis research is analyses and design of the adaptive robust controller based on artificial intelligence andnonlinear control. Robot manipulator is nonlinear, time variant and a number of parameters are uncertain,so design the best controller for this plant is the main target. Although inverse dynamic controller haveacceptable performance with known dynamic parameters but regarding to uncertainty, this controller'soutput has fairly fluctuations. In order to solve this problem this research is focoused on two methodologythe first one is design a fuzzy inference system as a estimate nonlinear part of main controller but thismethod caused to high computation load in fuzzy rule base and the second method is focused on designnovel adaptive method to reduce the computation in fuzzy algorithm.
Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator  [PDF]
Srinivasan Alavandar,M. J. Nigam
Journal of Engineering Science and Technology Review , 2008,
Abstract: The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). This paper presents the control of six degrees of freedom robot arm (PUMA Robot) using Adaptive Neuro Fuzzy Inference System (ANFIS) based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers
An Adaptive Fuzzy Controller for Trajectory Tracking of Robot Manipulator  [PDF]
Amol A. Khalate, Gopinathan Leena, Goshaidas Ray
Intelligent Control and Automation (ICA) , 2011, DOI: 10.4236/ica.2011.24041
Abstract: In this paper, an adaptive fuzzy control algorithm is proposed for trajectory tracking of an n-DOF robot manipulator subjected to parametric uncertainty and it is advantageous compared to the conventional nonlinear saturation controller. The asymptotic stability of the proposed controller has been derived based on Lyapunaov energy function. The design procedure is straightforward due to its simple fuzzy rules and control strategies. The simulation results show that the present control strategy effectively reduces the control effort with negligible chattering in control torque signals in comparison to the existing nonlinear saturation controller.
An adaptive fuzzy logic controller for robot-manipulator
Ho Dac Loc,Tran Thu Ha,Ngo Cao Cuong
International Journal of Advanced Robotic Systems , 2008,
Abstract: In this paper, an adaptive fuzzy controller is designed for the robot-manipulator. The synthesized controller ensures that 1) the close-loop system is globally stable and 2) the tracking error converges to zero asymptotically and a cost function is minimized. The fuzzy controller is synthesized from a collection of IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. The proposed control scheme is demonstrated in a typical nonlinear plant two link manipulator. The computer simulation of control is done by the language MATLAB. The results of simulation show that the adaptipresented results are analyzed.
Position Control of Robot Manipulator: Design a Novel SISO Adaptive Sliding Mode Fuzzy PD Fuzzy Sliding Mode Control  [PDF]
Farzin Piltan,Sadedeq Allahdadi,Mohammadali Dialame,Abbas Zare
International Journal of Artificial Intelligence and Expert Systems , 2011,
Abstract: This research focuses on design Single Input Single Output (SISO) adaptive sliding mode fuzzy PD fuzzy sliding mode algorithm with estimates the equivalent part derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Proposed method introduces a SISO fuzzy system to compensate for the model uncertainties of the system and eliminate the chattering by linear boundary layer method. This algorithm is used a SISO fuzzy system to alleviate chattering and to estimate the control gain in the control law and presented a scheme to online tune of sliding function. To attenuate the chattering phenomenon this method developed a linear boundary layer and the parameter of the sliding function is online tuned by adaptation laws. This algorithm will be analyzed and evaluated on robotic manipulators and design adaption laws of adaptive algorithms after that writing Lyapunov function candidates and prove the asymptotic convergence of the closed-loop system using Lyapunov stability theorem mathematically. Compare and evaluate proposed method and sliding mode algorithms under disturbance. In regards to the former, we will be looking at the availability of online tuning methodology and the number of fuzzy if-then rules inherent to the fuzzy system being used and the corresponding computational load. Our analysis of the results will be limited to tracking accuracy and chattering.
Adaptive Control of 4-DoF Robot manipulator  [PDF]
P. Mironchyk
Computer Science , 2015,
Abstract: In experimental robotics, researchers may face uncertainties in parameters of a robot manipulator that they are working with. This uncertainty may be caused by deviations in the manufacturing process of a manipulator, or changes applied to manipulator in the lab for sake of experiments. Another situation when dynamical and inertial parameters of a robot are uncertain arises, is the grasping of objects by a manipulator. In all these situations there is a need for adaptive control strategies that would identify changes in dynamical properties of manipulator and adjust for them. This article presents a work on designing of an adaptive control strategy for 4-DoF manipulator with uncertain dynamical properties, and outcomes of testing of this strategy applied to control of simulator of robot.
Design Novel Fuzzy Robust Feedback Linearization Control with Application to Robot Manipulator  [cached]
Farzin Piltan,MohammadHossain Yarmahmoudi,Mina Mirzaie,Sara Emamzadeh
International Journal of Intelligent Systems and Applications , 2013,
Abstract: First three degree of six degree of freedom robotic manipulator is controlled by a new fuzzy sliding feedback linearization controller. The robot arm has six revolute joints allowing the corresponding links to move horizontally. When developing a controller using conventional control methodology (e.g., feedback linearization methodology), a design scheme has to be produced, usually based on a system’s dynamic model. The work outline in this research utilizes soft computing applied to new conventional controller to address these methodology issues. Feedback linearization controller (FLC) is influential nonlinear controllers to certain systems which this method is based on compute the required arm torque using nonlinear feedback control law. When all dynamic and physical parameters are known FLC works superbly; practically a large amount of systems have uncertainties and fuzzy feedback linearization controller (FFLC) reduce this kind of limitation. Fuzzy logic provides functional capability without the use of a system dynamic model and has the characteristics suitable for capturing the approximate, varying values found in a MATLAB based area. To increase the stability and robustness new mathematical switching sliding mode methodology is applied to FFLC. Based on this research model free mathematical tunable gain new sliding switching feedback linearization controller applied to robot manipulator is presented to have a stable and robust nonlinear controller and have a good result compared with conventional and pure fuzzy logic controllers.
Comparative Study of Some New Hybrid Fuzzy Algorithms for Manipulator Control  [PDF]
Sudeept Mohan,Surekha Bhanot
Journal of Control Science and Engineering , 2007, DOI: 10.1155/2007/75653
Abstract: The robot manipulator is a highly complex system, which is multi-input, multi-output, nonlinear, and time variant. Controlling such a system is a tedious and challenging task. In this paper, some new hybrid fuzzy control algorithms have been proposed for manipulator control. These hybrid fuzzy controllers consist of two parts: a fuzzy controller and a conventional or adaptive controller. The outputs of these controllers are superimposed to produce the final actuation signal based on current position and velocity errors. Simulation is used to test these controllers for different trajectories and for varying manipulator parameters. Various performance indices like the RMS error, steady state error, and maximum error are used for comparison. It is observed that the hybrid controllers perform better than only fuzzy or only conventional/adaptive controllers.
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