基于大脑情感学习的转台逆模型补偿控制

针对传统控制方法难以克服飞行仿真转台伺服系统中存在的摩擦等非线性干扰力矩问题，提出了一种基于大脑情感学习(brain emotional learning, BEL)模型的转台伺服系统复合控制方法. 在传统PI反馈控制器的基础上，设计了基于BEL模型的同结构系统逆模型辨识器和前馈补偿控制器. 通过在线辨识系统逆模型来学习BEL模型的节点权值. 理想转台模型的数值仿真和实际转台伺服系统的实验结果都表明：BEL模型学习能力强，能满足实时控制要求，基于BEL模型的复合控制策略能有效抑制摩擦力矩的影响，提高转台系统的跟踪性能.

Inverse Model Compensation Control of Turntable Based on Brain Emotional Learning

In view of the difficulty in dealing with nonlinear disturbance moments such as friction in the flight simulator’s servo system, a compound control method using a brain emotional learning (BEL) model is proposed. Based on the traditional PI feedback controller, a BEL model-based identifier and a forward compensation controller of the same structure are designed. The node weights of the BEL model are obtained through inverse model identification of the system. Simulation results of an ideal simulator model and experimental results of a real simulator servo system show that the BEL model has good learning ability and meets the real-time control requirement. Moreover, the compound control strategy based on the BEL model can effectively reduce the influence of friction moment and improve tracking performance of the simulator.