Hybrid Robot Controller Based On Neural Network Compensator

In the framework of hybrid position / froce control process, we used a perceptron network with the extended backpropagation learning algorithm to adjust the parameters of the network. The underlying control system, here a robot, represents a non-linear system with an uncertainty related to it. In the proposed method a perceptron network is used as an approximator for modeling uncertain parts of robot dynamic but we assumed there is known parts through human knowledge and experience. Neural network parameter matrices are updated online with no initial offline training using the error as the objective function. The controller guarantees stability for any arbitrary initial values of neural network parameters and any unknown-but-bounded disturbances. Simulation is dealt with the hybrid control scheme in the Cartesian coordinates of the work space especially from the view point of tracking not only force but also position by citing an example of two degree of freedom robot.

کلیدواژه ها:

Neural Network ، Hybrid Position/Force Control ، Uncertainty