Inverse kinematics solution for robot manipulator based on adaptive MIMO neural network model optimized by hybrid differential evolution algorithm

Sơn, Nguyễn Ngọc; Ánh, Hồ Phạm Huy; Chau, Truong Dinh

doi:10.1109/robio.2014.7090633

Cited by 14 publications

(11 citation statements)

References 10 publications

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“…The estimated dynamics of the serial PAM robot arep 1 ¼ 1:5 andp 2 ¼ 3. We pick l in equation (8) as the same bandwidth as in equation (20). It first simply picks A ¼ diag[l, 1] in equation (10).…”

Section: Smc Of the 2-dof Serial Pam Robotmentioning

confidence: 99%

“…Recent studies proved that it can partially surpass this difficulty by combining a SMC controller and other intelligent models. Son et al have successfully identified and controlled the nonlinear dynamic system through combining MDE with adaptive neural model, 8 and Fei et al did that by combining SMC with recurrent neural structure. 9 An efficient combination for nonlinear dynamic system control has been introduced between SMC method and fuzzy proportional-integral controllers, 10,11 or between SMC and adaptive fuzzy model.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New approach of sliding mode control for nonlinear uncertain pneumatic artificial muscle manipulator enhanced with adaptive fuzzy estimator

Ánh

Kien

Sơn

et al. 2018

International Journal of Advanced Robotic Systems

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A new enhanced adaptive fuzzy sliding mode control approach is proposed in this article with its good availability for application in control of a highly uncertain nonlinear two-link pneumatic artificial muscle manipulator. Stability demonstration of the robust convergence of the closed-loop pneumatic artificial muscle manipulator system based on a novel enhanced adaptive fuzzy sliding mode control is experimentally proved using Lyapunov stability theorem. Obtained result confirms that the new enhanced adaptive fuzzy sliding mode control method, applied to the two-link uncertain nonlinear pneumatic artificial muscle manipulator system, is fully investigated with better robustness and precision than the standard sliding mode control and fuzzy sliding mode control techniques.

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Section: Smc Of the 2-dof Serial Pam Robotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New approach of sliding mode control for nonlinear uncertain pneumatic artificial muscle manipulator enhanced with adaptive fuzzy estimator

Ánh

Kien

Sơn

et al. 2018

International Journal of Advanced Robotic Systems

Self Cite

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“…Nguyen and Ho Pham [14] proposed a hybrid differential evolution to train an adaptive MIMO neural network for the solution of inverse kinematic. The hybrid differential evolution algorithm applied to solve the inverse kinematic of a 3 DOF manipulator which is composed by the back-propagation algorithm and the DE algorithm proved a faster performance and better precision than the conventional backpropagation algorithm or the solely differential evolution algorithm.…”

Section: Differential Evolution (De)mentioning

confidence: 99%

A Multi-objective Differential Evolution Algorithm for Robot Inverse Kinematics

Buendía-Rodríguez¹,

Cimat²,

Saha³

et al. 2016

IJCSE

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This paper presents the robot inverse kinematics solution for four Degrees of Freedom (DOF) through Differential Evolution (DE) algorithm. DE can handle real numbers (float, double) which leads more powerful than Genetic Algorithm (GA). We propose a multi-objective fitness function that makes an attempt to minimize the positional error and maximum angular displacement of the robot joints. Maximum angular displacement based fitness function adopt the constraints on different unrealistic rotational movement of the manipulator. We employ an equitable treatment of both fitness functions while maximizing these two over generations that iteratively selects the optimal weights of these two fitness functions automatically. Trigonometric mutation and binomial crossover improve the performance of the conventional DE technique. We compared the results of proposed multi-objective DE with GA and Algebraic Method (AM). Proposed multi-objective DE algorithm obtains less positional error than conventional DE, GA and AM while meeting the rotational constraints of the manipulator's joints.

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“…Therefore, researchers have proposed different control methods as to ameliorate the position/force control performance of PAM-based manipulators. Recent approaches to PAM-based position/force control have included PID control, haptic control, 17 optimal MPC control, 18 sliding mode control, 19 adaptive tracking control, 20 control through experimental modeling in Ganguly et al, 21 adaptive robust posture control in Zhu et al 22 and different meta-heuristic methods including neural-based PAM model 23 and hybrid neuro-fuzzy/GA observer in Carbonell et al 24 and Lilly and Chang, 25 evolutionary differential neural-based controller in Son et al, 26 hybrid fuzzy-based robust controller for PAM-based micro-robot, 27 iterative fuzzy controller for pneumatic muscle driven rehabilitation robot, 28 adaptive self-organizing fuzzy sliding mode controller for a 2-DOF PAM-based rehabilitation robot, 29 and so on. The main restriction of these intelligent control algorithms is that several methods still lack satisfactory robustness and accuracy; meanwhile, other algorithms prove too complex to be applied in practice.…”

Section: Introductionmentioning

confidence: 99%

Advanced force control of the 2-axes PAM-based manipulator using adaptive neural networks

2018

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SUMMARYThis paper proposes a detailed investigation on the new neural-based feed-forward PID direct force control (FNN-PID-DF) approach applied to a highly nonlinear 2-axes pneumatic artificial muscle (PAM) manipulator in order to ameliorate its force output performance. Founded on the novel inverse neural NARX model dynamically identified to learn well all nonlinear characteristics of the contact force dynamics of the 2-axes PAM-based manipulator, the novel proposed neural FNN-PID-DF force controller is innovatively implemented in order to directly force control the 2-axes PAM robot system used as a rehabilitation device subjected to internal systematic interactions and external contact force deviations. The performance of the experimental tests has proven the advantages and merits of the new force control method compared to the classical PID force control method. The new neural FNN-PID-DF force controller guides the wrist/hand of subject/patient to successfully generate the predefined desired force values.

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Inverse kinematics solution for robot manipulator based on adaptive MIMO neural network model optimized by hybrid differential evolution algorithm

Cited by 14 publications

References 10 publications

New approach of sliding mode control for nonlinear uncertain pneumatic artificial muscle manipulator enhanced with adaptive fuzzy estimator

New approach of sliding mode control for nonlinear uncertain pneumatic artificial muscle manipulator enhanced with adaptive fuzzy estimator

A Multi-objective Differential Evolution Algorithm for Robot Inverse Kinematics

Advanced force control of the 2-axes PAM-based manipulator using adaptive neural networks

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