A stable adaptive force/position controller for a C5 parallel robot: a neural network approach

Achili, Brahim; Daachi, B.; Ali-Chérif, Arab; Daâchi, M. E.

doi:10.1017/s0263574711001354

Cited by 9 publications

(10 citation statements)

References 24 publications

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“…Achili et al in [18] proposed an adaptive force/position controller for parallel robot with constrained motions based on multi-layer perceptron neural network. The solution presents the major advantage of obtaining the control law without the prior knowledge of the inverse dynamic model and being able to take into account the endogenous disturbance (uncertainties and nonlinearities related to the robot dynamics) and to compensate exogenous disturbances.…”

confidence: 99%

ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator

Moldovan

Grif

Gligor

2015

INT J COMPUT COMMUN

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This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6-PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity.

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confidence: 99%

ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator

Moldovan

Grif

Gligor

2015

INT J COMPUT COMMUN

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“…The parameters of a robot are roughly known from its design but direct measurement of the coefficients of physical parameters contain uncertainties due to the complexity of the manipulator. To overcome these problems, different controllers have been proposed such as robust control [7], [8], adaptive control [9]- [11], neural network-based force control [12], [13], model predictive control [14] and disturbance observer control [15].…”

Section: Introductionmentioning

confidence: 99%

Model identification for robot manipulators using regressor-free adaptive control

Hayat

Buss

2016

2016 UKACC 11th International Conference on Control (CONTROL)

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This paper proposes a regressor-free adaptive feedback-linearization control technique that does not require a model approximation or a regressor matrix. Adaptation in the proposed feedback process is acquired through an update law involving adjustment of less control parameters as compared to existing controllers. Under the given constraints, the closed-loop asymptotic stability of the proposed control law is verified using Lyapunov techniques. The proposed controller is compared with existing adaptive controllers on a two degree-of-freedom robot manipulator. Based on the new adaptive technique, the model parameters of the robotic arm are identified using adequate excitation trajectories. The proposed adaptive technique was validated through simulations and experiments.

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“…Uncertainties can arise in the constrained environment, for example, a surface with unknown geometrical characteristics, 9,10 or an unknown impedance or stiffness. 11,12 They can also arise in the robotic model, for example, unknown robotic parameters 13,14 or uncertainties in the Jacobian matrix. 15 To optimize the controller parameters of robotic systems, particle swarm optimization (PSO) techniques have been widely used.…”

Section: Introductionmentioning

confidence: 99%

PSO-Lyapunov motion/force control of robot arms with model uncertainties

Mehdi

Boubaker

2014

Robotica

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SUMMARYA method for motion/force control of robot arms with model uncertainties is presented. Tracking control of complex trajectories is guaranteed using a Lyapunov approach with high-precision performance ensured using a particle swarm optimization (PSO) algorithm. Tracking performance and robustness are simulated for a robotic device for limb rehabilitation that is designed to be adapted easily to different subjects by considering model parameter uncertainties. Controller parameters are optimized offline using the PSO algorithm with Lyapunov stability conditions considered as inequality constraints. Using the control scheme, the robot can guide limbs on smooth and non-smooth trajectories, under model uncertainties and measurement noise.

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A stable adaptive force/position controller for a C5 parallel robot: a neural network approach

Cited by 9 publications

References 24 publications

ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator

ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator

Model identification for robot manipulators using regressor-free adaptive control

PSO-Lyapunov motion/force control of robot arms with model uncertainties

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