Robust Learning Control Of Robots

In the past decades, artificial intelligence theory and methods such as neural networks, fuzzy logics and evolution algorithms have been used to control robot system. Neural networks have lots of advantages including universal approximation abilities, distributed architecture, accurate system mathematic model is not needed in control process and powerful ability of working with other control theory such as adaptive control, variable structure control, so neural networks have been widely applied to the control of robot system with unknown dynamics.In recent years, stable adaptive control has been the main trend of neural network based adaptive control of nonlinear system, and there is stricter theoretical proof on system stability. But one of the shortness of adaptive neural networks is that the network need to be trained repeatedly, do not have the ability of learning as human brains and reusing the learned knowledge. Great achievements have been made toward adaptive neural networks based robot control, however, the system unknown dynamics are still unknown when the control task is finished. The researchers have not explored whether the neural network have learned the unknown dynamics, even for the same control task the neural networks have to be trained repeatedly, which is time and energy wasted. So it is meaningful to further investigate the neural networks'ability of learning the unknown dynamics in the stable adaptive closed loop control process. The learned knowledge is expected to be reused in the same or similar control tasks so that the repeated training phase can be avoided. Moreover, the adaptive neural network controller is expected to accumulate knowledge by experiences so that better control performance can be achieved.According to deterministic learning theory, for an appropriately designed adaptive neural network controller, the sub-vector of the radial basis function networks'regression vector satisfy the persistent exciting condition when tracking period or period-like desired trajectory, the estimate errors of neural network weights are proved to be convergent to a small neighborhood of zero, and ultimately the unknown the nonlinearity could be learned by a constant neural network due to the local property of radial basis function networks.In this thesis, by using the achievements of deterministic learning theory, robust learning control scheme is presented for robots with unknown dynamics and disturbances. In the stable closed loop control process, the regression vector of the designed robust adaptive neural network controller satisfies partial persistent exciting condition. By analyzing the linear time varying system obtained from the closed loop system, partial neural network weights convergence can be achieved. The unknown dynamics of closed loop robot system can be learned by neural network, and saved as experience knowledge in the form of constant neural weights. The learning enables us to understand the underlying characteristic of the unknown system dynamics. When repeat the same or similar control task, the controller can also reuse the learned dynamic knowledge and better control performance can be achieved with little efforts. Time and energy wasting repeated training phase can be avoided through the proposed scheme, both of the true learning and the reusing of learned knowledge are realized.