Development of a Neural Network Control System for a Micro-Flexible Robotic Arm

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A reference signal self-organizing control system using neural networks for flexible micro-manipulators is presented. This control system consists of a series combination of both a plant with a feedback loop and a neural network with a feedforward loop. In this system, the neural network functions as the reference input filter and it organizes a new reference signal to the closed-loop system. Backpropagation can be improved in the two different ways : by heuristics, and by using more powerful methods of optimization. This two new backpropagation methods with improved performance is proposed. By using momentum and an adaptive learning rate training time can be decreased. Momentum decreases backpropagation's sensitivity to small details in the error surface. This helps the network avoid getting stuck in shallow minima which would prevent the network from finding a lower error solution. The use of an adaptive learning rate attempts to keep the learning step size as large as possible while keeping learning stable. The learning rate is made responsive to the complexity of the local error surface. Levenberg-Marquardt optimization makes training times even shorter. Levenberg-Marquardt optimization is more sophisticated method than gradient descent. Experimental results for the trajectory tracking control of flexible micro-manipulators show that proposed control system is effective in accurately following the reference signal.