基于POPOV超稳定性定理的神经元智能控制

An Adaptive Neural Controller Based on Popov Hyperstability Approach

研究一种渐近超稳定的神经元智能控制器结构．基于ＰＯＰＯＶ超稳定性定理，提出一种能保证控制系统渐近超稳定的控制器参数学习算法．针对神经元智能控制的直流全数字调速系统做了仿真实验，给出了仿真结果．

An adaptive neural controller are used to control a given plant. A learning architecture is proposed for modifying the weights of the neural controller to provide appropriate inputs to the plant so that a desired response is obtained and global asymptotic hyperstability is guaranteed. The properties of the proposed control architecture is studied by simulating a D. C. digital speed-regulating system.