Mobile Robots Adaptive Control Using Neural Networks
This work addresses control challenges for mobile robots in dynamic environments, but it appears incremental as it builds on existing neural network methods without claiming major breakthroughs.
The paper tackles the problem of controlling mobile robots with non-linear dynamics and model uncertainties by proposing a feed-forward neural network controller that compensates for these uncertainties, extending classical velocity control strategies.
The paper proposes a feed-forward control strategy for mobile robot control that accounts for a non-linear model of the vehicle with interaction between inputs and outputs. It is possible to include specific model uncertainties in the dynamic model of the mobile robot in order to see how the control problem should be addressed taking into consideration the complete dynamic mobile robot model. By means of a neural network feed-forward controller a real non-linear mathematical model of the vehicle can be taken into consideration. The classical velocity control strategy can be extended using artificial neural networks in order to compensate for the modelling uncertainties. It is possible to develop an intelligent strategy for mobile robot control.