多变量半自适应预测控制系统架构

A framework for multi-variable, semi-adaptive predictive control system

针对模型因素引起的预测控制性能退化问题,本文提出一种多变量半自适应预测控制系统架构.该架构将传统的控制模式改变为测试模式,变传统的设定值控制为区间控制,从而实现了测试过程中输出变量的约束满足.所提出的半自适应预测控制体系架构能够在确保生产正常进行的前提下,实现过程的开环测试,提高测试效率,并通过极大化测试信号的幅值来确保测试过程的信噪比.进一步地,将该框架从约束预测控制扩展到双层结构预测控制,引入平衡系数实现经济效益与测试之间的平衡.本文提及的测试方法是一种在线开环测试,避免了闭环测试过程中测试输入信号与不可测噪声的相关性问题.仿真实例验证了该方法的有效性.

In view of the degradation of predictive control performance caused by model mismatch, a multivariable, semi-adaptive zone predictive control system framework is presented. The proposed framework changes the traditional control mode to testing mode and turns set-point to zone control, thereby realizing the constraint satisfaction of the output variables of the test process. For the constraint zone model’s predictive control in the integrated testing mode, the amplitude strength of testing input signals is introduced to realize the constraint guarantee function and signal-to-noise ratio maximization. The framework implements the open-loop test to improve test efficiency under the premise of production on the rails. The signal-to-noise ratio of the testing process is ensured by maximizing the test signal amplitude. Furthermore, the framework is extended from constrained to two-layer model predictive control, and the benefit balance coefficient is introduced to realize the balance between economic benefit and testing. The method proposed in the paper is a type of on-line open-loop identification,which solves the problem of the correlation between input signals and noises in closed-loop identification. The simulation results verify the effectiveness of the method.