摘要
提出一种基于稀疏核学习辨识模型的单步预测控制(sparse kernel learningone-step-ahead predictive control,SKL-OPC)框架,并推导了该框架下采用多项式核的一种控制算法。该算法在求取最优控制律时可将调节变量从目标函数分离出来,并最终转化为求解一奇数次代数方程根的问题。因此无需复杂的非线性优化技术,且克服了基于二次多项式核辨识模型不准确造成控制算法失效的缺点。在一非线性连续搅拌反应釜的控制研究表明了该方法的有效性和优越性。
A novel control framework based on sparse kernel learning one-step ahead predictive control (SKL-OPC) was presented for the general unknown nonlinear processes. The polynomial kernel function was adopted to derive a simple control strategy as a natural extension of SKL-OPC. The manipulated input can be separated from the control performance index due to the special structure of polynomial kernel. Consequently, the control problem was resolved by solving the roots of an odd degree polynomial equation. The proposed control strategy did not require the nonlinear optimization technique, which resulted in a small computation scale and made it very suitable for real-time control. Application of the proposed approach to a highly nonlinear continuous stirred tank reactor indicated its validity and showed superior performance, compared to other methods.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2008年第10期2541-2545,共5页
CIESC Journal
基金
国家自然科学基金项目(20576116)
国家科技支撑计划课题(2007BAF14B02)~~
关键词
非线性过程控制
稀疏核学习
多项式核函数
nonlinear process control
sparse kernel learning
polynomial kernel function