Diagonal Recurrent Neural Networks for a Walking Robot
نویسندگان
چکیده
Whilst the necessity of finding an intelligent-based controlling method for a two-leg walking robot increases, the balance of the under-actuated leg consisting of two links is emphasized in this study. This is not only a nonlinear structure, but also a single-input double-output system. However, the problem becomes concrete through the proposed diagonal recurrent neural networks (DRNN) method. In this paper, two kinds of DRNN are introduced into the control system. The diagonal recurrent neuroidentifier (DRNI) is selected as an identifier, and the diagonal recurrent neurocontroller (DRNC) is determined as a controller. Additionally, a generalized dynamic backpropagation algorithm (DBP) is also applied to train both DRNC and DRNI. With the simulated results, it is shown that the under-actuated leg is balanced and stabilized by DRNN. This study definitely contributes the intelligent-based as well as the real-time controlled method for a two-leg walking robot with profound insight
منابع مشابه
Neuro-Optimizer: A New Artificial Intelligent Optimization Tool and Its Application for Robot Optimal Controller Design
The main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. Recently there have been attempts for using artificial neural networks (ANNs) in optimizationproblems and some types of ANNs such as Hopfield network and Boltzm...
متن کاملModular Reactive Neurocontrol for Biologically Inspired Walking Machines
A neurocontroller is described which generates the basic locomotion and controls the sensor-driven behavior ofa four-legged and a sixlegged walking machine. The controller utilizes discrete-time neurodynamics, and is ofmodular structure. One module isfor processing sensor signals, one is a neural oscillator network serving as a central pattern generator, and the third one is a so-called velocit...
متن کاملA proposed neural internal model control for robot manipulators
This paper describes the design of a Neural Internal Model Control (NIMC) system for robots, based on Recurrent Hybrid Networks (RHNs). The NIMC, an alternative to the basic inverse control scheme, consists of a forward internal neural model of robot, a neural controller and a conventional feedback controller. An Alopex Learning Algorithm (ALA) was used to adjust weights of the proposed neural ...
متن کاملDesigning Path for Robot Arm Extensions Series with the Aim of Avoiding Obstruction with Recurring Neural Network
In this paper, recurrent neural network is used for path planning in the joint space of the robot with obstacle in the workspace of the robot. To design the neural network, first a performance index has been defined as sum of square of error tracking of final executor. Then, obstacle avoidance scheme is presented based on its space coordinate and its minimum distance between the obstacle and ea...
متن کاملTrajectory Generation for Bipedal Robots Using Recurrent Neural Networks
Motivation: The idea of learning a feedback control function in a neural network has exciting implications. If we could train a neural network to output the optimal control variables for any position in state space, then the walking problem would be completely solved. Unfortunately, our own recent experiments with using neural networks to directly learn the control variables (torques) worked we...
متن کامل