Combined multi-layer perceptron neural network and sliding mode technique for parallel robots control : An adaptive approach

Achili, Brahim; Daachi, B.; Ali-Chérif, Arab; Amirat, Yacine

doi:10.1109/ijcnn.2009.5179031

Cited by 10 publications

(11 citation statements)

References 16 publications

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“…The decision process in Figure 9.2 normally contains a correlation device (Aitken, 1957;Rodgers and Nicewander, 1988;Dowdy and Wearden, 1983), a causal device (Gasking, 1955;Simon and Rescher, 1966;Pearl, 2000;Byrne, 2005) as well as a control device (Abdoos et al, 2011;Achili et al, 2009;Al-Faiz and Sabry, 2012;Farouk, 2012;Ghosh and Gude, 2012). A correlation machine, and was discussed in detail in Chapters 2 and 4, has a function of filling the missing information and many routines such as artificial intelligence have been developed to deal with this problem.…”

Section: Informationmentioning

confidence: 99%

Marginalization of Irrationality in Decision Making

Marwala¹

2015

Causality, Correlation and Artificial Intelligence for Rational Decision Making

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The theory of marginalization of irrationality in decision making is proposed as a way of making satisficing decisions in the presence of irrationality. This is achieved by assuming that decision making process is made of a series of decision making actions which are either rational or irrational. The aggregation of these actions determine whether an irrational decision process will satisfice or not. This method is applied in the diagnosis of breast cancer and it is observed that the more irrational a decision making process the less accurate the results become. It is also observed that indeed it is possible for irrational actions to be marginalized by rational actions and that irrationality also marginalizes rationality. 167 Causality, Correlation and Artificial Intelligence for Rational Decision Making Downloaded from www.worldscientific.com by UNIVERSITY OF BIRMINGHAM LIBRARY -INFORMATION SERVICES on 03/22/15. For personal use only.

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Section: Informationmentioning

confidence: 99%

Marginalization of Irrationality in Decision Making

Marwala¹

2015

Causality, Correlation and Artificial Intelligence for Rational Decision Making

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“…Consider a nonlinear uncertain dynamical system represented by (25). If the synchronized adaptive sliding mode control shown in (35) is applied, asymptotic robust stability of the closed-loop system in the presence of model uncertainties and disturbances is guaranteed.…”

Section: Stability Analysismentioning

confidence: 99%

“…Weng [24] used adaptive fuzzy sliding mode control scheme for tracking the trajectory of parallel robot. Achili [25] adopted robust controller with respect to external disturbances to improve the trajectory tracking of parallel robot by combining neural network and sliding mode technique. Redha et al [26] introduced a diagram of control based on SMC and applied iterative learning control (ILC) to Delta parallel robot.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and Control of a Novel 3 -DOF Parallel Manipulator with Actuation Redundancy

Xuemei

Gao

Liu

et al. 2013

Int. J. Autom. Comput.

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Abstract:This paper deals with the dynamics and control of a novel 3-degrees-of-freedom (DOF) parallel manipulator with actuation redundancy. According to the kinematics of the redundant manipulator, the inverse dynamic equation is formulated in the task space by using the Lagrangian formalism, and the driving force is optimized by utilizing the minimal 2-norm method. Based on the dynamic model, a synchronized sliding mode control scheme based on contour error is proposed to implement accurate motion tracking control. Additionally, an adaptive method is introduced to approximate the lumped uncertainty of the system and provide a chattering-free control. The simulation results indicate the effectiveness of the proposed approaches and demonstrate the satisfactory tracking performance compared to the conventional controller in the presence of the parameter uncertainties and un-modelled dynamics for the motion control of manipulators.

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“…Zhao et al 29 used a discrete sliding mode controller with fuzzy adaptive reaching law for the trajectory tracking of a 6-PRRS parallel mechanism. Achili et al 30 adopted a robust controller for external disturbances to improve the trajectory tracking of a parallel mechanism by combining neural network and SMC. Moreover, Tang et al 31 proposed chattering-free SMC and integral-surface adaptive SMC for the two degrees of freedom (DOF) and 6-DOF parallel mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the trajectory tracking performance, some advanced control strategies have been developed, such as adaptive control, 20,21 robust control, 22,23 fuzzy control, 24,25 neural network control, 26 and sliding mode control (SMC). [27][28][29][30][31][32][33] To deal with modeling uncertainties, time-varying properties, and external disturbances, SMC is to be considered as an effective control scheme, having some superior properties such as fast transient response and high robustness to uncertainties. In the past few years, a great deal of SMC for parallel mechanisms have been successfully implemented.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling and adaptive fuzzy terminal sliding mode controller with nonlinear observer of a 2-(PUU)²R hybrid mechanism

Zhang

Wang

2019

International Journal of Advanced Robotic Systems

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This article proposes a new spatial 2-(PUU) 2 R hybrid mechanism that can perform the three degrees of freedom translation and one degree of freedom rotation and presents an analysis of the dynamics of the mechanism. An adaptive fuzzy terminal sliding mode controller with nonlinear observer for the hybrid mechanism is proposed to achieve a precise trajectory tracking, which could be utilized in solving the problems of the hybrid mechanism caused by model uncertainties, varying payloads, and external disturbances. Firstly, through the interrelation between the constraints, the 6 Â 6 Jacobian matrix and 6 Â 6 Â 6 Hessian matrix for the mechanism are derived. Furthermore, dynamic modeling is established based on the virtual work principle, through which the characteristics of dynamic modeling can be proved. To achieve high-precision position tracking, a nonlinear observer was introduced to feed into the terminal sliding mode control which had improved the mechanism's ability to resist the external disturbances. In addition, the chattering caused by the terminal sliding mode control was eliminated by approximating the switching gain with the usage of adaptive fuzzy logic in a finite time. Finally, a series of numerical simulations are carried out to prove the validity of the proposed approach, and the results verify better robustness and higher precision for the trajectory tracking than proportional-integral-derivative and sliding mode control.

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Combined multi-layer perceptron neural network and sliding mode technique for parallel robots control : An adaptive approach

Cited by 10 publications

References 16 publications

Marginalization of Irrationality in Decision Making

Marginalization of Irrationality in Decision Making

Dynamics and Control of a Novel 3 -DOF Parallel Manipulator with Actuation Redundancy

Dynamic modeling and adaptive fuzzy terminal sliding mode controller with nonlinear observer of a 2-(PUU)²R hybrid mechanism

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Combined multi-layer perceptron neural network and sliding mode technique for parallel robots control : An adaptive approach

Cited by 10 publications

References 16 publications

Marginalization of Irrationality in Decision Making

Marginalization of Irrationality in Decision Making

Dynamics and Control of a Novel 3 -DOF Parallel Manipulator with Actuation Redundancy

Dynamic modeling and adaptive fuzzy terminal sliding mode controller with nonlinear observer of a 2-(PUU)2R hybrid mechanism

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Product

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Dynamic modeling and adaptive fuzzy terminal sliding mode controller with nonlinear observer of a 2-(PUU)²R hybrid mechanism