Deterministic learning and control of mobile robots
移动机器人的确定学习与控制

By making use of the recent result of deterministic learning theory, we present a learning control scheme for mobile robots. In the process of closed-loop control, the unknown system dynamics is learned and memorized as experience knowledge in the format of constant neural weights. When repeating the same control tasks, the controller may invoke the previously learned knowledge in the new control process to achieve a better performance. With this scheme, the mobile robots can learn and memorize the knowledge of dynamics as experience for later use, exonerating from the repetitive training phase.