Karakasoglu A, Sudharsanan S I, Sundareshan M K
Dept. of Electr. and Comput. Eng., Arizona Univ., Tucson, AZ.
IEEE Trans Neural Netw. 1993;4(6):919-30. doi: 10.1109/72.286887.
Efficient implementation of a neural network-based strategy for the online adaptive control of complex dynamical systems characterized by an interconnection of several subsystems (possibly nonlinear) centers on the rapidity of the convergence of the training scheme used for learning the system dynamics. For illustration, in order to achieve a satisfactory control of a multijointed robotic manipulator during the execution of high speed trajectory tracking tasks, the highly nonlinear and coupled dynamics together with the variations in the parameters necessitate a fast updating of the control actions. For facilitating this requirement, a multilayer neural network structure that includes dynamical nodes in the hidden layer is proposed, and a supervised learning scheme that employs a simple distributed updating rule is used for the online identification and decentralized adaptive control. Important characteristic features of the resulting control scheme are discussed and a quantitative evaluation of its performance in the above illustrative example is given.
基于神经网络的策略有效实现复杂动态系统的在线自适应控制，该系统由多个（可能是非线性的）子系统相互连接构成，其核心在于用于学习系统动力学的训练方案的收敛速度。举例来说，为了在高速轨迹跟踪任务执行期间对多关节机器人操纵器实现令人满意的控制，高度非线性和耦合的动力学以及参数变化要求快速更新控制动作。为满足这一要求，提出了一种在隐藏层包含动态节点的多层神经网络结构，并采用一种使用简单分布式更新规则的监督学习方案进行在线识别和分散自适应控制。讨论了所得控制方案的重要特征，并对其在上述示例中的性能进行了定量评估。
