The Application of Learning Impedance Control to Exoskeleton Arm

2012 ◽  
Vol 463-464 ◽  
pp. 900-904
Author(s):  
Jie Liu ◽  
Yu Wang ◽  
He Ting Tong ◽  
Ray P.S. Han
Keyword(s):  
Neural Network ◽  
Impedance Control ◽  
Rbf Neural Network ◽  
Robotic Manipulators ◽  
Iterative Learning ◽  
Artificial Muscles ◽  
Experimental Setup ◽  
Control Input ◽  
Control Scheme ◽  
Pneumatic Artificial Muscles

In this paper, we discuss the application of learning impedance control scheme to exoskeleton arm driven by pneumatic artificial muscles (PAM), for assisting in the rehabilitation of patients who suffer from debilitating illness. An iterative learning impedance control problem for robotic manipulators is analyzed, proposed and solved. The target impedance reference modifies a desired trajectory according to the force signals and position signals of the joint. The desired control input of learning impedance control was estimated by radial basis function (RBF) neural network incorporated experience database. The curves of experiment result on the experimental setup show that the algorithm is successful also in the application of exoskeleton arm.

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

2019 ◽  
Vol 41 (12) ◽  
pp. 3452-3467 ◽  
Author(s):  
Tarek Bensidhoum ◽  
Farah Bouakrif ◽  
Michel Zasadzinski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Iterative Learning ◽  
Unknown Control Directions ◽  
Control Scheme ◽  
Unknown Control

In this paper, an iterative learning radial basis function neural-networks (RBF NN) control algorithm is developed for a class of unknown multi input multi output (MIMO) nonlinear systems with unknown control directions. The proposed control scheme is very simple in the sense that we use just a P-type iterative learning control (ILC) updating law in which an RBF neural network term is added to approximate the unknown nonlinear function, and an adaptive law for the weights of RBF neural network is proposed. We chose the RBF NN because it has universal approximation capabilities and can approximate any continuous function. In addition, among the advantages of our controller scheme is the fact that it is applicable to deal with a class of nonlinear systems without the need to satisfy the global Lipschitz continuity condition and we assume, only, that the unstructured uncertainty is norm-bounded by an unknown function. Another advantage of the proposed controller and unlike other works on ILC, we do not need any prior knowledge of the control directions for MIMO nonlinear system. Thus, the Nussbaum-type function is used to solve the problem of unknown control directions. In order to prove the asymptotic stability of the closed-loop system, a Lyapunov-like positive definite sequence is used, which is shown to be monotonically decreasing under the control design scheme. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed control scheme.

Iterative Learning Control Based on Radial Basis Function Network for Exoskeleton Arm

2011 ◽  
Vol 415-417 ◽  
pp. 116-122 ◽  
Author(s):  
Jie Liu ◽  
Yu Wang ◽  
He Ting Tong ◽  
Ray P.S. Han
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Iterative Learning Control ◽  
Rbf Neural Network ◽  
Radial Basis Function Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Radial Basis

In this paper, we propose iterative learning control (ILC) scheme for exoskeleton arm driven by pneumatic artificial muscles (PAM), with special and unknown parameters, performing repetitive tasks. This desired control input of ILC was estimated by radial basis function (RBF) neural network incorporated experience database. An ILC controller, which uses the position of the joint where an angular sensor is used as the input of the ILC controller, is developed and tested on exoskeleton arm under well controlled conditions. RBF neural network was proposed to obtain the initial value of ILC. The experiment result on the experimental platform show that the algorithm is successful also in the application of exoskeleton arm.

scholarly journals Position Domain Synchronization Control For Robotic Manipulators

2021 ◽  
Author(s):  
Vangjel Pano
Keyword(s):  
Time Domain ◽  
Robotic Manipulators ◽  
Synchronization Control ◽  
Control Law ◽  
Control Input ◽  
Contour Tracking ◽  
Hybrid Controller ◽  
Reference Motion ◽  
Control Scheme ◽  
Control Schemes

Developed in this thesis is a new control law focusing on the improvement of contour tracking of robotic manipulators. The new control scheme is a hybrid controller based on position domain control (PDC) and position synchronization control (PSC). On PDC, the system’s dynamics are transformed from time domain to position domain via a one-to-one mapping and the position of the master axis motion is used as reference instead of time. The elimination of the reference motion from the control input improves contouring performance relative to time domain controllers. Conversely, PSC seeks to reduce the error of the systems by diminishing the synchronization error between each agent of the system. The new control law utilizes the aforementioned techniques to maximize the contour performance. The Lyapunov method was used to prove the proposed controller’s stability. The new control law was compared to existing control schemes via simulations of linear and nonlinear contours, and was shown to provide good tracking and contouring performances.

scholarly journals Adaptive PD Control Based on RBF Neural Network for a Wire-Driven Parallel Robot and Prototype Experiments

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yuqi Wang ◽  
Qi Lin ◽  
Xiaoguang Wang ◽  
Fangui Zhou
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Approximation Error ◽  
Wind Tunnel Test ◽  
Parallel Robot ◽  
Network Control ◽  
Pd Control ◽  
Control Scheme

An adaptive PD control scheme is proposed for the support system of a wire-driven parallel robot (WDPR) used in a wind tunnel test. The control scheme combines a PD control and an adaptive control based on a radial basis function (RBF) neural network. The PD control is used to track the trajectory of the end effector of the WDPR. The experimental environment, the external disturbances, and other factors result in uncertainties of some parameters for the WDPR; therefore, the RBF neural network control method is used to approximate the parameters. An adaptive control algorithm is developed to reduce the approximation error and improve the robustness and control precision of the WDPR. It is demonstrated that the closed-loop system is stable based on the Lyapunov stability theory. The simulation results show that the proposed control scheme results in a good performance of the WDPR. The experimental results of the prototype experiments show that the WDPR operates on the desired trajectory; the proposed control method is correct and effective, and the experimental error is small and meets the requirements.

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