摘要
提出一种基于改进型极限学习机的内模控制策略,以改进型极限学习机建立控制系统的内模,利用泰勒级数对内模进行一次项展开,来间接获得控制系统的控制量,避免了直接求解模型的逆.同时,对所提出的内模控制系统在建模误差和干扰条件下分析系统稳定性条件和误差.将所提出的控制策略应用于连续搅拌釜反应器系统中实现内模控制仿真.仿真结果表明该控制策略能很好地实现反应器的浓度控制,且具备很强的抗干扰性.同时基于改进极限学习机的系统比极限学习机具有更好的控制性能.
A novel internal model control strategy is presented based on the improved extreme learning machine. Specifically, the improved extreme learning machine is used to estimate the internal model, and internal model is expanded with first-order term by Taylor series to calculate the controller for the control system indirectly, which avoids calculating the inverse for internal model directly. Moreover, the system stability and error are also analyzed for this control system with model errors and disturbance. The proposed control strategy is simulated to control in the continuous stirred tank reactor. The results indicate that the proposed control strategy has an excellent system performance to control the consistence with a strong anti-noise performance. Moreover, the system based on the improved extreme learning machine has a better performance than that based on extreme learning machine.
出处
《信息与控制》
CSCD
北大核心
2013年第5期618-624,共7页
Information and Control
基金
教育部博士点新教师基金资助项目(20113514120007)
福建省自然科学基金资助项目(2010J05132)
关键词
极限学习机
结构风险
内模控制系统
泰勒级数
稳定性
extreme learning machine
structural risk
internal model control system
Taylor series
stability