AN INTELLIGENT METHOD TO ESTIMATE THE INERTIA MATRIX OF A ROBOT ARM FOR ACTIVE FORCE CONTROL USING ON-LINE NEURAL NETWORK TRAINING SCHEME (Jurnal Mekanikal, 2018)
This paper presents a new intelligent controller algorithm comprisingan on-line multi-layer artificial neural network (ANN) trainingscheme to estimate the inertia matrix ofthe robot arm to enhance theperformance of the active force control (AFC) scheme. The robotunder study is a planar two-link rigid robot which is subjected to anon-linear disturbance torques acting at the robot joints. Thealgorithm has two stages, namely the ANN training stage and -theimplementation stage. During the training stage, the proposed ANNscheme trains the ANNparameters (weights and biases) for a periodoftime by utilising the back-propagation (BP) learning method. Aftera sufficient training period, the training session is switched off, andthe ANN is ready to be used in the implementation stage of theintelligent AFC-ANN controller scheme. The results of the trainingand implementation stages are shown and discussed. It is shown that the proposed controller scheme is very effective and robust. The simulation is accomplished using MATLAB@ software.
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