On adaptive trajectory tracking of a robot manipulator using inversion of its neural emulator

Neural and fuzzy robotic hand control

An efficient first grasp for a wheelchair robotic arm-hand with pressure sensing is determined and presented. The grasp is learned by combining the advantages of neural networks and fuzzy logic into a hybrid control algorithm which learns from its tip and slip control experiences. Neurofuzzy modifications are outlined, and basic steps are demonstrated in preparation for physical implementation. Choice of object approach vector based on fuzzy tip and slip data and an expert supervisor, as well as training of a diagnostic neural tip and slip controller, are the focus of this work.

Context in temporal sequence processing: a self-organizing approach and its application to robotics

A self-organizing neural net for learning and recall of complex temporal sequences is developed and applied to robot trajectory planning. We consider trajectories with both repeated and shared states. Both cases give rise to ambiguities during reproduction of stored trajectories which are resolved via temporal context information. Feedforward weights encode spatial features of the input trajectories, while the temporal order is learned by lateral weights through delayed Hebbian learning. After training, the net model operates in an anticipative fashion by always recalling the successor of the current input state. Redundancy in sequence representation improves noise and fault robustness. The net uses memory resources efficiently by reusing neurons that have previously stored repeated/shared states. Simulations have been carried out to evaluate the performance of the network in terms of trajectory reproduction, convergence time and memory usage, tolerance to fault and noise, and sensitivity to trajectory sampling rate. The results show that the model is fast, accurate, and robust. Its performance is discussed in comparison with other neural-networks models.

On Adaptive Learning Rate That Guarantees Convergence in Feedforward Networks

This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima. This modification also helps in improving the convergence speed in some cases. Conditions for achieving global minimum for these kind of algorithms have been studied in detail. The performances of the proposed algorithms are compared with BP algorithm and extended Kalman filtering (EKF) on three bench-mark function approximation problems: XOR, 3-bit parity, and 8-3 encoder. The comparisons are made in terms of number of learning iterations and computational time required for convergence. It is found that the proposed algorithms (LF I and II) are much faster in convergence than other two algorithms to attain same accuracy. Finally, the comparison is made on a complex two-dimensional (2-D) Gabor function and effect of adaptive learning rate for faster convergence is verified. In a nutshell, the investigations made in this paper help us better understand the learning procedure of feedforward neural networks in terms of adaptive learning rate, convergence speed, and local minima.

Constrained Motion Control of Flexible Robot Manipulators Based on Recurrent Neural Networks

In this paper, a neural network approach is presented for the motion control of constrained flexible manipulators, where both the contact force everted by the flexible manipulator and the position of the end-effector contacting with a surface are controlled. The dynamic equations for vibration of flexible link and constrained force are derived. The developed control, scheme can adaptively estimate the underlying dynamics of the manipulator using recurrent neural networks (RNNs). Based on the error dynamics of a feedback controller, a learning rule for updating the connection weights of the adaptive RNN model is obtained. Local stability properties of the control system are discussed. Simulation results are elaborated on for both position and force trajectory tracking tasks in the presence of varying parameters and unknown dynamics, which show that the designed controller performs remarkably well.