三电机驱动系统的神经网络模糊自整定解耦控制

多电机驱动系统是一种多输入多输出、非线性、强耦合的系统.它广泛应用在许多需要高精度协调控制的驱动领域,比如电动汽车驱动、城市轨道交通以及印刷业等.本文提出了一种新的方法用于三电机驱动系统的速度与张力的解耦控制,其核心由模糊自整定控制与BP神经网络广义逆组成.首先,由神经网络广义逆与原系统串联实现复合伪线性系统;其次,在该伪线性系统中采用模糊自整定方法.仿真结果表明:所提方法能有效实现速度与张力间的解耦,将三电机驱动系统转化为多个具有开环稳定性的单输入单输出线性子系统,同时系统的响应速度快、超调量小、瞬态时间较短,具有良好的跟踪性能,这有助于改善系统的启动特性,降低系统振荡.

Fuzzy self-tuning decoupling control based on neural network of three-motor drive system

Multi-motor drive system is a multi-input multi-output (MIMO), nonlinear and strong-coupling system. It is applied to many drive fields where high precision coordinated control is of importance, such as the electric vehicle drive, urban rail transit, and printing. In this paper, a new control strategy is proposed for decoupling the speed and the tension of the three-motor drive system, in which the key is to incorporate the fuzzy self-tuning control with back-propagation (BP) neural network generalized inverse (NNGI). The pseudo-linear composite system is formed by connecting NNGI in series with the original system; and then, the fuzzy self-tuning control method is introduced to this pseudo-linear system. Simulation results demonstrate that the proposed strategy can effectively decouple speeds and tensions, and transform the three-motor drive system into several single-input single-output (SISO) linear subsystems with open-loop stability. This system has obvious superiority in rapid response speed, low overshoot, short transient time and good tracking effect, which help to improve the starting characteristics of the system and decrease the system oscillation.